# Nasser Ghanemzadeh > Forward Deployed AI Engineer for founder-led B2B SaaS. Ships one narrow AI workflow into your product in 10 business days. Building Vectig in public with Claude Code. Former CEO/CPO at Nivo (acquired, 200K+ users). Nasser Ghanemzadeh has spent 17+ years building products and companies across AI, SaaS, and fintech. As CEO and later CPO at Nivo he built the product organization and shipped AI-powered applications that grew to 200K+ users and 450% revenue growth before the company was acquired. His current commercial focus is the AI Feature Sprint: a 10-business-day engagement for founder-led B2B SaaS teams of 5 to 50 that ships one narrow AI workflow into their product, with fixed scope, fixed price, and a Day-5 prototype guarantee. A focused 2-hour Discovery Day scoping session assesses whether an AI use case is useful, buildable, and worth a Sprint. His live R&D lab is Vectig, an AI-native cash flow forecasting product for pre-seed-to-Series-A founders, built solo with Claude Code, in public. Every workflow pattern he ships into a client codebase has already shipped in Vectig first. He is the author of two books: Forward Deployed AI Engineering (a working guide to the Forward Deployed AI Engineer role: what it is, where it came from, how it is practiced, and how to do it well; 304 pages, first edition May 2026, Version 1.5 August 2026) and Founder Mode (how great founders stay close to product, people, judgment, and execution as companies scale). He writes a weekly newsletter, Notes from the edge of building, and his work has been covered in Forbes, TechCrunch, Al Jazeera, and HuffPost. ## The book Forward Deployed AI Engineering: A Working Guide to the Hottest Job in Software, by Nasser Ghanemzadeh. Independently published. First edition May 2026; current version 1.5, August 2026. 304 pages, 5 parts, 20 chapters, plus a workbook and annotated references. ISBN 979-8199517683. - Paperback: $19.99 at https://www.amazon.com/Forward-Deployed-AI-Engineering-Software/dp/B0H3VKKG38/ - Kindle: $9.99 (also on Kindle Unlimited) at https://www.amazon.com/dp/B0H3KP9CVH/ ## The service The AI Feature Sprint is a 10-business-day engagement that ships one narrow AI workflow into a founder-led B2B SaaS product. Fixed scope, fixed price: $5,000 at beta pricing, $7,500 to $10,000 standard. Working prototype by Day 5, guaranteed. A $500 Discovery Day scoping session assesses whether an AI use case is useful, buildable, and worth a Sprint, credited toward the Sprint if you continue within 14 days. --- # Nasser Ghanemzadeh: Forward Deployed AI Engineer and Author URL: https://ghanemzadeh.com/ Forward Deployed AI Engineer for founder-led B2B SaaS ## Nasser Ghanemzadeh I embed for 10 business days and ship the AI feature your roadmap keeps deferring. [Book a Sprint →](https://ghanemzadeh.com/ai-feature-sprint/) [Subscribe to the newsletter](https://ghanemzadeh.com/newsletter/) Building Vectig in public. Former CEO/CPO at Nivo (acquired). Built Nivo to 200K users and an acquisition as CEO/CPO. Work covered in Forbes, TechCrunch, Al Jazeera, and HuffPost. **[Vectig](https://vectig.com)**, my own AI-native SaaS, is the live R&D lab. Every pattern I ship into your codebase has already shipped in mine. ### What I'm doing now One commercial focus, one live R&D lab, two books, one newsletter. The hierarchy matters. **Commercial focus:** [10-business-day AI Feature Sprint](https://ghanemzadeh.com/ai-feature-sprint/) for founder-led B2B SaaS teams. One narrow AI workflow shipped into their product. Fixed scope, fixed price, Day-5 prototype guarantee. **Live R&D lab:** [Vectig](https://vectig.com), AI-native cash flow forecasting for pre-seed-to-Series-A founders. Built solo with Claude Code, in public. Every workflow pattern I sell to a Sprint client has shipped in Vectig first. **Authority and method:** two books, [Forward Deployed AI Engineering](https://ghanemzadeh.com/books/forward-deployed-ai-engineering/) and [Founder Mode](https://ghanemzadeh.com/book/). The first explains the role; for the short version, see [what a Forward Deployed AI Engineer is](https://ghanemzadeh.com/forward-deployed-ai-engineer/), and for the reading list around it, [the best FDE books](https://ghanemzadeh.com/best-forward-deployed-engineer-books/). The second explains the operating practice underneath it. **Trust and capture:** [weekly newsletter](https://ghanemzadeh.com/newsletter/) covering AI-native product building, Claude Code workflows, and the unglamorous middle of solo SaaS. **Earlier projects:** [Sengi](https://sengi.co), a financial OS for freelancers, built solo as a learning vehicle for AI-native financial tooling. Paused to focus on Vectig and the Sprint. ### Selected work **Nivo**. CEO, then CPO. Built the product organization and shipped AI-powered applications that grew to 200K+ users and 450% revenue growth. Acquired. **Pangouan**. Founding Head of Product (to 2026). Zero to one: no product, no team, no users. Built it from the ground up. **Finnova**. Co-founder. Coworking Space and Accelerator backing early-stage startups. Got to see the ecosystem from the investor side, which changes how you think about everything. **Iran Startups**. Co-founder of a founder community. Earlier, ran [Techly](https://techly.me/), an English-language publication covering the Iranian startup ecosystem (2014 to 2016), and wrote about The Lean Startup methodology in Persian through the [Business of Software](https://businessofsoftware.ir/) blog when almost no one in Iran was talking about it. ### Speaking & media Featured in [Forbes](https://www.forbes.com/sites/elizabethmacbride/2016/04/30/seven-reasons-iran-is-likely-to-be-an-entrepreneurial-powerhouse/), [TechCrunch](https://techcrunch.com/2014/09/02/the-next-tech-startup-ecosystem-to-emerge-iran/), [HuffPost](https://www.huffpost.com/entry/ibridges-the-iranian-runa_b_7627470), and [Al Jazeera](https://www.aljazeera.com/gallery/2016/12/1/inside-irans-silicon-valley). Host of [The Great CEO Podcast](https://www.youtube.com/@ghanemzadeh). ### Get in touch Email: [ghanemzadeh@gmail.com](mailto:ghanemzadeh@gmail.com). For a Sprint, the application form is on [the AI Feature Sprint page](https://ghanemzadeh.com/ai-feature-sprint/). Newsletter ### Notes from the edge of building One email a week. Building solo with Claude Code, AI product distribution, and the unglamorous middle of solopreneurship. No spam. Unsubscribe in one click. ### Quick answers #### What is Nasser Ghanemzadeh working on? Running a 10-business-day AI Feature Sprint for founder-led B2B SaaS teams (one narrow AI workflow shipped into their product, fixed scope, $5K beta). Building Vectig, an AI-native cash flow forecasting product for founders, as a public R&D lab. Previously CEO/CPO at Nivo (acquired, 200K+ users, 450% revenue growth). Earlier work: Sengi (financial OS for freelancers, paused), Pangouan (Founding Head of Product, language learning), Iran Startups ecosystem. #### Where can I work with him? Through the [AI Feature Sprint](https://ghanemzadeh.com/ai-feature-sprint/), the [newsletter](https://ghanemzadeh.com/newsletter/), or by [email](mailto:ghanemzadeh@gmail.com). ### Elsewhere [GitHub](https://github.com/ghanemzadeh) · [LinkedIn](https://www.linkedin.com/in/ghanemzadeh/) · [Goodreads](https://www.goodreads.com/ghanemzadeh) · [AngelList](https://wellfound.com/u/ghanemzadeh) · [Crunchbase](https://www.crunchbase.com/person/nasser-ghanemzadeh) · [SlideShare](https://www.slideshare.net/Ghanemzadeh/presentations) · [F6S](https://www.f6s.com/member/ghanemzadeh) --- # Hire a Forward Deployed AI Engineer: 10-Day AI Feature Sprint URL: https://ghanemzadeh.com/ai-feature-sprint/ ## Forward Deployed AI engineering for founder-led B2B SaaS. OpenAI and Anthropic each launched deployment subsidiaries in May 2026 to embed engineers inside Fortune 500 companies. The AI Feature Sprint is how you hire a Forward Deployed AI Engineer without a $250K procurement process: fixed-price AI development, sized for founder-led SaaS teams. I scope, build, and ship an AI feature, one narrow workflow, into staging, production, or a production-ready pull request in 10 business days. Built by **Nasser Ghanemzadeh**, ex-CEO/CPO of Nivo (acquired, 200K+ users) · building Vectig in public · featured in Forbes, TechCrunch, Al Jazeera ⚡ Now booking the next Sprint slot · beta capped at 3 teams [Book a $500 Discovery Day](https://ghanemzadeh.com/discovery-day/) [Apply for the $5,000 Sprint](https://tally.so/r/ja5pD6) Beta pricing: $5,000, capped at 3 teams. After beta: $7,500–$10,000. By [Nasser Ghanemzadeh](https://ghanemzadeh.com/). Last updated August 2026. As covered in Forbes · TechCrunch · Al Jazeera · HuffPost ### Your team knows AI matters. The hard part is shipping the right thing. Most SaaS teams are already experimenting with AI. Someone has tried Claude Code. Someone has built a prototype. Someone has suggested adding a chatbot. But useful AI features do not come from vague experimentation. They need a clear workflow, the right data, a narrow scope, product judgment, and an implementation path the team can actually ship. That is what the AI Feature Sprint is designed to create: one managed AI workflow with a clear use case, implementation path, review process, and handoff. ### What is Forward Deployed AI Engineering? Forward Deployed AI Engineering is a delivery model where an engineer embeds inside a customer's team and ships AI capabilities directly into the customer's product and codebase. Not a dashboard. Not a report. Not a Loom. The term comes from Palantir, where it has been the dominant model for two decades. In May 2026, OpenAI launched a $10 billion deployment subsidiary using exactly this title, and Anthropic launched a $1.5 billion deployment subsidiary the same day. Both are aimed at the Fortune 500. The **AI Feature Sprint is the same playbook, sized for founder-led B2B SaaS teams of 5 to 50 people** who can't run a quarter-million-dollar procurement process. More on the role: [what a Forward Deployed AI Engineer is](https://ghanemzadeh.com/forward-deployed-ai-engineer/), and the full guide in the book, [Forward Deployed AI Engineering: A Working Guide to the Hottest Job in Software](https://ghanemzadeh.com/books/forward-deployed-ai-engineering/). ### Examples of narrow AI workflows - **Investor update assistant.** Turn monthly metrics, asks, risks, and progress into a clear investor update draft. - **Finance/runway scenario explainer.** Help founders understand how burn, hiring, churn, or revenue changes affect runway. - **Customer summary generator.** Turn scattered account notes, usage data, and history into a concise customer summary. - **Support ticket summarizer.** Summarize long support threads and surface the issue, urgency, and suggested next step. - **Report generator.** Turn structured data or notes into a useful weekly, customer, or internal report. - **Onboarding assistant.** Guide new users or customers through the first important workflow in your product. ### A concrete example: Investor Update Assistant Here's a workflow I've built into Vectig, the AI-native startup finance product I'm developing in parallel. The Investor Update Assistant pulls structured monthly metrics (MRR, burn, runway, hires, churn, key wins), takes 3–5 sentences of founder-written context, and generates a clean investor update draft. The founder edits, approves, and sends. Average time from open to send: 8 minutes vs. the typical 90+ minutes of blank-page writing. What you'd get in a Sprint: the workflow scoped to your data sources (Stripe, your spreadsheet, your CRM, whatever you use), a working prompt with output quality rubric, integration into your existing product or admin tool, and the handoff document so your team can maintain and iterate. Two weeks. Live by Day 10. ### Measured outcomes Every Sprint workflow ships with an output quality rubric, so results are measured rather than asserted. These are the measured results from the live Vectig implementation of the Investor Update Assistant. | Metric | Before | After | Sample | | --- | --- | --- | --- | | Investor update draft time, open to send | 90+ minutes of blank-page writing | 8 minutes | Live Vectig implementation | | Output quality rubric score | Not measured | 4-of-5 average across accuracy, clarity, honesty, and ask-coverage | First 30 sample runs | The draft-time number measures the founder's full loop: open the workflow, review the generated draft, edit, and send. The rubric score is the average across the four dimensions the workflow is graded on, scored run by run. As new Sprint engagements complete, measured before/after results are added here. ### This is not an AI science project. This is not: - A generic AI strategy workshop - A chatbot brainstorm - A 40-page roadmap - An attempt to make your whole product "AI-native" in two weeks - A replacement for your engineering team - A vague exploration of agents, copilots, and buzzwords - A compliance-heavy enterprise AI transformation The sprint is intentionally narrow: one workflow, one clear user problem, one useful shipped outcome. ### Day 0: The readiness check Before we scope a Sprint, we run a 2-hour readiness check on your use case, data, codebase, and team. Below is what a typical founder-led SaaS team looks like at the start. The Sprint begins on Day 1 with these gaps already mapped. ~/sprint-readiness/check.md $ run readiness_check --target founder-saas **use case** vague **data sources** scattered **codebase access** ready **AI provider setup** configured **team availability** partial **success criteria** undefined → recommended path: 2-hour Discovery Day to scope use case and define success criteria. Output simulated. Real diagnostics are generated live during your Discovery Day. Entry offer ### Discovery Day A 2-hour working session designed to assess whether your AI use case is actually useful, buildable, and worth turning into a sprint. $500 **Includes** - 2-hour working session - Use-case clarification - Data and API readiness check - Workflow selection - Risk assessment - Recommended feature scope - Go / no-go memo - Sprint recommendation if there is a strong fit If you continue to the full Sprint within 14 days, the $500 Discovery Day fee is credited toward the Sprint price. [See Discovery Day details →](https://ghanemzadeh.com/discovery-day/) Main offer ### AI Feature Sprint 10 business days. One narrow AI workflow shipped into staging, production, or a production-ready pull request. $5,000 for the first 3 teams. Later: $7,500 to $10,000 after the beta slots. **What is included** - AI use-case selection - Feature scope - UX flow - Product and technical architecture - Prompt and data design - Claude Code workflow - Hands-on implementation of the selected workflow - Working prototype by Day 5 - Staging deployment, production-ready PR, or handoff by Day 10 - Team handoff and enablement session - Loom walkthrough - Handoff notes - 30-day roadmap - Output quality rubric - Reusable workflow playbook [Apply for the $5,000 Sprint](https://tally.so/r/ja5pD6) ### How the 10-business-day sprint works **Day 1: Scope** Define the workflow, user problem, available data, constraints, and success criteria. **Days 2 to 3: Design** Map the UX flow, architecture, prompt and data structure, and implementation plan. **Days 4 to 5: Prototype** Build the first working version and validate the direction. **Days 6 to 9: Implementation** Refine the workflow, handle edge cases, add error states, improve prompts, prepare for handoff or deployment. **Day 10: Handoff** Staging deployment, production-ready pull request, or implementation handoff, depending on your infrastructure and access. ### How a Sprint gets scoped on Day 1 Every Sprint Brief is written around a single sentence: > *For [user], when [trigger or input] happens, the workflow will [process or action] and produce [output], so that [business value].* **Worked example.** "For the founder, when monthly metrics and 3 to 5 sentences of context are added, the workflow will draft a clean investor update covering metrics, risks, and asks, so that the founder can send consistent updates in 8 minutes instead of 90." If we can't agree on the words that fill those five brackets on Day 1, the Sprint isn't ready to start. ### The output quality rubric Every Sprint ships with a 1 to 5 rubric tuned to its specific dimensions. The scale is fixed; the dimensions change per workflow. | Score | Meaning | | --- | --- | | 1 | Unusable. Output is wrong, incoherent, or unsafe. | | 2 | Needs major rewrite. Shape is right; substance requires substantial editing. | | 3 | Usable with significant edits. Team starts from this draft but invests real time refining. | | 4 | Good with minor edits. Small adjustments only. Usable in roughly the time it takes to read. | | 5 | Ready to use. No edits needed. Ships as-is. | Each workflow's rubric specifies its own dimensions: accuracy, clarity, tone, format, completeness. Each comes with worked examples at every score level. ### What we'll take on as a first Sprint - Low risk Internal-only workflows. Drafts and summaries reviewed by a human before anything leaves the company. Most first Sprints sit here, and they should. - Medium risk Workflows touching customer-facing data (support tickets, CRM notes, sales transcripts, internal financial data) with explicit human review. - High risk Legal, medical, compliance, payments, or anything that takes customer-facing actions autonomously. Declined as a first Sprint. We'll help redefine to a lower-risk scope, or wait until trust and tooling are mature. ### Clear scope. Clear expectations. The sprint timeline begins on the agreed kickoff date, scheduled after contract signing and upfront payment. I take one active sprint at a time. Kickoff dates are scheduled on a first-available basis. Client access, decision-maker availability, and timely feedback are required for the 10-business-day timeline. ### This is for you if… - You run or lead product at a B2B SaaS company - You know AI should be part of your product but do not want vague experimentation - You want one useful AI workflow, not a giant AI transformation - Your team can provide access to relevant product, data, or codebase context - You can make decisions quickly during the sprint - You want product judgment and execution, not just code ### This is not for you if… - You want to "add AI" without a real user workflow - You need a full enterprise AI transformation - Your data is inaccessible or not ready - Your team cannot provide access or feedback during the sprint - You want a general chatbot without a clear product use case - You need guaranteed production deployment regardless of infrastructure constraints ### Why work with me I'm Nasser Ghanemzadeh, a former CEO/CPO and product leader with 17+ years building and scaling technology products. - Exited Nivo (200K+ users, 450% revenue growth, acquired). Press coverage in Forbes, TechCrunch, Al Jazeera, HuffPost. - Founding Head of Product at Pangouan. Co-founder of Iran Startups. Former accelerator lead at Finnova. - Currently building Vectig (AI-native startup finance), shipped daily with Claude Code and MCP. This sprint runs on production-grade AI engineering experience from my own products, not generic AI consulting theory. Based in Turkey, available across European, Middle Eastern, and North American business hours. [LinkedIn](https://www.linkedin.com/in/ghanemzadeh/) · [Email](mailto:ghanemzadeh@gmail.com) · [Vectig](https://vectig.com) ### FAQ #### What is a 10-day AI Feature Sprint? A 10-business-day engagement to ship one narrow AI workflow into a B2B SaaS product. Scoping on Day 1, design on Days 2 to 3, prototype by Day 5, implementation on Days 6 to 9, handoff on Day 10. Fixed scope, fixed price. Deliverables include the workflow code committed to your repo, an output quality rubric, a workflow playbook, a Loom walkthrough, and a 30-day post-Sprint roadmap. #### Who is this for? Founder-led B2B SaaS companies with 5 to 50 employees, post-product-market-fit, with paying customers, real data inside their product, and pressure to ship AI features. Not for indie hackers, solopreneurs, or enterprises with formal procurement. #### What does it cost? $500 for a Discovery Day (credited toward the Sprint if you continue within 14 days). $5,000 for the Sprint at beta pricing (first 3 customers). $7,500 to $10,000 after beta. #### What happens if you can't deliver? If by Day 5 there's no working prototype demonstrating the core workflow, you can stop the Sprint and receive 50% back. No subjective judgment, no fine print. Either the prototype runs or it doesn't. #### Why pay you instead of using your own engineers? Three reasons: speed (10 days, not 6 weeks of internal roadmap displacement), judgment (knowing which workflow to ship first is harder than building it), and a fixed-price/fixed-timeline commitment that internal work can't match. Pulling two engineers off your roadmap for six weeks costs roughly $30K in salary plus the opportunity cost of the slipped roadmap. #### How is this different from an AI consulting firm? AI consulting firms deliver decks and roadmaps. This Sprint delivers a working AI workflow in your codebase by Day 10. The category is "Forward Deployed AI Engineering". The same shape of work OpenAI's Deployment Company and Anthropic's deployment arm sell to Fortune 500 customers, at a different price point. #### What kinds of AI workflows do you build? Investor update assistants. Runway scenario explainers. Customer summary generators. Support ticket summarizers. Report generators. Onboarding assistants. The common shape: narrow input, narrow output, single user, measurable improvement, human-in-the-loop review. #### What if our data isn't ready? Then a Sprint isn't the right starting point. The Discovery Day will say so explicitly and recommend what to do first. About half of Discovery Days end with "this isn't a Sprint yet, here's what to do instead." #### Do you handle compliance, security, GDPR, SOC 2? No. Those are explicitly out of scope. The Sprint can produce a workflow that's ready to deploy through your existing compliance posture, but the compliance review itself is your team's work. #### What stack do you work in? Whatever you already use. The Sprint integrates with your existing product, your existing data sources (Stripe, HubSpot, Linear, Slack, Notion, Intercom, Zendesk, your CRM, your admin), and your existing AI provider (Anthropic, OpenAI, AWS Bedrock, Google Vertex AI). The Sprint is too short to introduce new infrastructure. #### Can you guarantee production deployment? No. Production deployment depends on access, infrastructure, and third-party dependencies that aren't fully under our control. If production release is blocked, the deliverable is a staging deployment or a production-ready pull request your team can merge. #### What happens after Day 10? Three paths. Most teams take the workflow and run with it. The rubric and playbook are designed for self-service maintenance. Some come back for a second Sprint with a different workflow. Some sign an ongoing engagement for governance and iteration. All three are explicit options at handoff. ### Terms used on this page **Forward Deployed Engineer** An engineer who embeds inside a customer's team and ships inside their codebase, rather than delivering an external dashboard or report. Originally Palantir's model; adopted by OpenAI and Anthropic in May 2026. **AI Feature Sprint** This offer. 10 business days, one narrow AI workflow, fixed scope, fixed price. **Workflow** A specific process a user runs with clear inputs and outputs, producing a measurable improvement. The unit of work shipped in a Sprint. Not a "feature." **Sprint Brief** The one-to-two-page document written on Day 1 of a Sprint that defines scope, success criteria, deliverables, and explicit out-of-scope items. Part of the contract. **Output Quality Rubric** A 1 to 5 scoring checklist tuned to a specific workflow, used to evaluate generated outputs consistently after handoff. **MCP** Model Context Protocol, the standard for letting AI models talk to external systems. Many Sprint workflows integrate with customer systems via MCP servers. Full definitions for these and more terms are in the [Forward Deployed AI Engineering glossary](https://ghanemzadeh.com/glossary/). ### Want to ship one useful AI feature? Start with a Discovery Day if you need help choosing the right use case. Apply for the Sprint if you already know what you want to build and need help getting it shipped. [Book a $500 Discovery Day](https://ghanemzadeh.com/discovery-day/) [Apply for the $5,000 Sprint](https://tally.so/r/ja5pD6) --- # The Best Forward Deployed Engineer Books & Resources (2026) URL: https://ghanemzadeh.com/best-forward-deployed-engineer-books/ [Forward Deployed Engineer](https://ghanemzadeh.com/forward-deployed-ai-engineer/) ## The best Forward Deployed Engineer books & resources (2026) There is exactly one book written specifically about the Forward Deployed Engineer role, so a useful FDE reading list pairs it with the best books on the skills the role is made of: AI engineering, data systems, product discovery, and the customer room. These five books, plus five free essays and talks, cover the territory. By [Nasser Ghanemzadeh](https://ghanemzadeh.com/). Last updated August 2026. ### The five books #### 1. Forward Deployed AI Engineering, by Nasser Ghanemzadeh The working guide to the role itself: what an FDE is, where the model came from, how the work is practiced phase by phase, and how to break in. 304 pages across five parts and 20 chapters, with a workbook and annotated references at the back. It is the only book on this list written specifically about the Forward Deployed Engineer job. Full disclosure: I wrote this one. [Read about the book](https://ghanemzadeh.com/books/forward-deployed-ai-engineering/), including the full structure and sample chapters. #### 2. AI Engineering, by Chip Huyen The technical complement. Huyen covers building applications with foundation models: evaluation, prompt engineering, retrieval, finetuning decisions, and inference. If the FDE book is about the job, this is the deepest treatment of the engineering underneath it. For readers who want the model-layer fundamentals before their first engagement. #### 3. Designing Data-Intensive Applications, 2nd edition, by Martin Kleppmann and Chris Riccomini The systems substrate of enterprise integration work. Most FDE engagements are won or lost on data plumbing, and this is the standard text on the storage, replication, and pipeline machinery inside every enterprise stack you will integrate with. Read it slowly. It pays off for years. #### 4. Inspired, 2nd edition, by Marty Cagan The product-discovery half of the craft. An FDE decides what to build under pressure, in front of the customer, and Cagan's book is the standard text on discovering what is worth building before you build it. Written for product teams, but the discovery discipline maps directly onto scoping an engagement. #### 5. Impro, by Keith Johnstone The odd one out, deliberately. Johnstone's book on improvisational theatre appears on Palantir's FDE onboarding reading list because customer-room work is improvisation: status, listening, and responding honestly to what is actually happening. It teaches the temperament of the work rather than the technique. ### Essential essays and talks (free) - [How Palantir built the ultimate founder factory](https://www.lennysnewsletter.com/p/inside-palantir-nabeel-qureshi). Nabeel Qureshi with Lenny Rachitsky, Lenny's Newsletter, May 2025. Reflections on nearly eight years as a Palantir forward deployed engineer, and why the role keeps producing founders. - [The FDE Playbook for AI Startups](https://www.ycombinator.com/library/Mt-the-fde-playbook-for-ai-startups-with-bob-mcgrew). Bob McGrew on Y Combinator's Lightcone podcast, September 2025. The person who helped pioneer the model at Palantir, and later led research at OpenAI, explains why it is at the heart of the AI boom. - [The Sales Strategy Conquering the AI Market](https://tomtunguz.com/fde-cs/). Tomasz Tunguz, July 2025. The go-to-market economics of forward deployed engineering, from the investor side. - [Forward Deployed Engineering 101](https://www.youtube.com/watch?v=KwhgfwOSToQ). Kevin Bai at the AI Engineer World's Fair, June 2026. A 17-minute framework for when FDE is necessary and how to structure it, from someone who did the work at Palantir, Rippling, and Anthropic. - [Software Is Changing (Again)](https://www.ycombinator.com/library/MW-andrej-karpathy-software-is-changing-again). Andrej Karpathy at YC's AI Startup School, June 2025. The wider shift the FDE role sits inside: LLMs as a new kind of computer, programmed in English. ### Where to start If you are deciding whether the role is for you, start with the [explainer on what a Forward Deployed Engineer is](https://ghanemzadeh.com/forward-deployed-ai-engineer/), then the Qureshi and McGrew conversations. If you have decided and want the map, start with [the Forward Deployed Engineer book](https://ghanemzadeh.com/books/forward-deployed-ai-engineering/) and follow the path in [how to become an FDE](https://ghanemzadeh.com/how-to-become-a-forward-deployed-ai-engineer/). --- # The Founder Mode Book: From HP to NVIDIA | Nasser Ghanemzadeh URL: https://ghanemzadeh.com/book/ **Brian Chesky just reopened the AI Founder Mode conversation for the AI Era.** This book turns the idea into an operating lens. A practical book on how founders stay close to product, people, judgment, and execution as companies scale. ## Founder Mode *How the Great Companies Are Actually Built* In 2024, Paul Graham named "Founder Mode" after Brian Chesky's talk at a YC alumni dinner. The practice is older than the name. HP, Disney, Apple, Pixar, Amazon, NVIDIA, and Costco built their best work this way. This book traces how, and is honest about the costs. Sign up to be notified when the book ships. The first two chapters are yours to read now. You'll also get the newsletter. One email a week, easy unsubscribe. ### Why this book, now Founder Mode is back in the conversation because Brian Chesky is again explaining how great founders operate differently. But the idea is still easy to misunderstand. Founder Mode is not micromanagement. It is not founder ego. It is calibrated involvement: staying close to the decisions, standards, customers, products, and people that shape the company. 01 · What's inside ### Four parts **Foundations** lays out what Founder Mode actually is, separating the practice from the personality around it. It is not micromanagement, and it is not charisma. It is calibrated involvement. **Practices** is the operating manual. The small group of trusted operators. The meetings the founder still runs personally. The decisions the founder still owns. The handoffs that have to happen anyway. **Anchors** is the case-study core. Eight companies, eight chapters, and the specific moves that made each one work. **Returns, Costs, Coda** is the part most books skip. Founder Mode works, and it costs the founder things. The book is honest about both. 02 · Anchor chapters ### Eight companies, eight chapters #### HP Bill and Dave still walking the lab decades after founding, and the practice that institutionalized it. #### Walt Disney The *Snow White* production and a $1.5M bet on feature animation when nobody believed it would work. #### PayPal The small group, the relentless iteration, and the alumni network it produced. #### Apple Jobs in the Newton era. The products that almost shipped, the ones that did, and what he refused to delegate. #### Pixar Ed Catmull's Brain Trust as a structured, durable form of involvement. #### Amazon Bezos's Day 1 doctrine and the six-page memos that enforced it. #### NVIDIA Jensen Huang at Denny's, then thirty years of the same direct-report meetings. #### Costco The buying-club discipline that survived Sinegal's retirement. 03 · About ### About the author I'm Nasser Ghanemzadeh. 17+ years building products and companies across AI, SaaS, and fintech. Previously CEO/CPO at Nivo (acquired). Currently building Vectig solo with Claude Code, and running a 10-business-day [AI Feature Sprint](https://ghanemzadeh.com/ai-feature-sprint/) for B2B SaaS teams. Also by Nasser: [Forward Deployed AI Engineering: A Working Guide to the Hottest Job in Software](https://ghanemzadeh.com/books/forward-deployed-ai-engineering/). More on the [home page](https://ghanemzadeh.com/). 04 · Start reading ### Read the first two chapters Join the launch list and I'll send you the first two chapters now. You'll also be notified when the Kindle version ships. You'll also get the newsletter. One email a week, easy unsubscribe. This book is independent and is not affiliated with Airbnb, Brian Chesky, Paul Graham, Y Combinator, or Invest Like the Best. --- # Books | Nasser Ghanemzadeh URL: https://ghanemzadeh.com/books/ Books ## Two books, one author Different topics, same operating lens. Founder Mode is about how great companies are actually built. Forward Deployed AI Engineering is about a specific job that has become the default way to ship AI into the real world. Both are written for people who actually do the work. [2026 · New ### Forward Deployed AI Engineering A Working Guide to the Hottest Job in Software 304 pages. First edition, May 2026. Version 1.5, August 2026. On May 4, 2026, OpenAI and Anthropic each launched deployment subsidiaries on the same day, both built around the title Forward Deployed Engineer. This book is for the person who wants to do that work. Five parts: the landscape, the mindset and the method, the craft, becoming an FDE, and the economy and future. Read the first chapters →](https://ghanemzadeh.com/books/forward-deployed-ai-engineering/) [2026 ### Founder Mode How the Great Companies Are Actually Built 254 pages. First edition. In 2024, Paul Graham named "Founder Mode" after Brian Chesky's talk at a YC alumni dinner. The practice is older than the name. HP, Disney, Apple, Pixar, Amazon, NVIDIA, and Costco built their best work this way. This book traces how, and is honest about the costs. Read the first two chapters →](https://ghanemzadeh.com/book/) ### Why two books The two books are about the same thing seen from different angles. Founder Mode is about the people who choose to stay close to the work that matters as their companies scale. Forward Deployed AI Engineering is about the work itself, in a specific form, in a specific moment. Read either one first. The connection becomes obvious by the end. ### About the author Nasser Ghanemzadeh is a [Forward Deployed AI Engineer](https://ghanemzadeh.com/forward-deployed-ai-engineer/) for founder-led B2B SaaS. 17+ years building products and companies across AI, SaaS, and fintech. Former CEO and CPO at Nivo (acquired, 200K+ users, 450% revenue growth), Founding Head of Product at Pangouan, and co-founder of Finnova and the Iran Startups community. Work covered in Forbes, TechCrunch, Al Jazeera, and HuffPost. Today he runs the [AI Feature Sprint](https://ghanemzadeh.com/ai-feature-sprint/) and builds [Vectig](https://vectig.com) in public with Claude Code. More on the [home page](https://ghanemzadeh.com/). --- # Forward Deployed AI Engineering: The FDE Book | Nasser Ghanemzadeh URL: https://ghanemzadeh.com/books/forward-deployed-ai-engineering/ **May 4, 2026: OpenAI and Anthropic each launched deployment subsidiaries on the same day, both using the title Forward Deployed Engineer.** This book is for the person who wants to do that work. The Forward Deployed Engineer book: a working guide to the role OpenAI and Anthropic just put on the map. ## Forward Deployed AI Engineering *A Working Guide to the Hottest Job in Software* First edition, May 2026 · Version 1.5, August 2026 · 304 pages · ISBN 979-8199517683 What's new in Version 1.5: coverage updated through August 2026, including the AWS $1 billion FDE program and Microsoft's $2.5 billion Frontier initiative. For eighteen months before May 4, 2026, the Forward Deployed Engineer was already the most-discussed engineering role in the industry. Then the two most valuable private AI companies announced, on the same day, that they were creating new entities to put their engineers inside their customers' offices and ship working AI systems. OpenAI capitalized its vehicle at $10 billion. Anthropic at $1.5 billion. Same title. Same playbook. This book is for the person who wants to do that work. [Order on Kindle](https://www.amazon.com/dp/B0H3KP9CVH/) [Order Paperback](https://www.amazon.com/Forward-Deployed-AI-Engineering-Software/dp/B0H3VKKG38/) Or read the sample chapters (PDF) now. Sign up and they land in your inbox. You'll also get the newsletter. One email a week, easy unsubscribe. 01 · Who this is for ### Three readers The book is written for three people specifically. The chapters are ordered for each of them in turn. - **The early-career engineer.** Three to five years into your career. You have read some of the job postings. You recognize something in the description that fits how you want to work. You want a map. - **The mid-career switcher.** Consultant, solutions architect, product manager, ex-founder. You suspect you already have most of the skills. You want to know what is missing. - **The founder or hiring manager.** Building an FDE function for the first time. Needs a primer before going to market. 02 · What you'll be able to do ### By the last chapter The book is the FDE playbook, engineered around four working outcomes. Finish it and a reader should be able to do all four. - Explain the role to a hiring manager in your own words, without using "forward deployed" as a magic incantation. - Describe an end-to-end FDE engagement, from the first customer conversation through the production deployment and the synthesis review that turns the engagement into product. - Execute the first week of a real engagement using the templates in the workbook at the back of the book. - Plan a credible path from your current role to a first offer, with a portfolio you can defend and an interview loop you can prepare for. 03 · Structure ### Five parts **Part I. The Landscape.** What the role is, where it came from, and why the AI moment has made it the default operating model of the industry. **Part II. The Mindset and the Method.** The four traits that distinguish people who succeed at this work. The Echo and Delta split that organizes the work into two halves of one craft. The three-phase engagement that gives the work its rhythm. The gravel-road-to-highway pattern that makes the work compound into product. **Part III. The Craft.** Discovery, evaluation, shipping the first slice, integrating with the enterprise stack, and closing the loop. **Part IV. Becoming an FDE.** The paths in. The portfolio. The interview loop. The first year on the inside. **Part V. Economy and Future.** Pricing, outcomes, contracts. Cargo cults and failure modes. The FDE diaspora the labs are producing whether they want to or not. A closing note for the buyer. 04 · Anchor chapters ### Selected chapters #### The Pilot-to-Production Gap Why 95% of enterprise AI pilots produce zero measurable return, and why the gap isn't closed by buying more tokens. #### A Brief History: From Palantir to the Frontier Labs Two decades of FDE practice at Palantir. How it escaped that niche and became the default model for OpenAI, Anthropic, and the labs that followed. #### Echo and Delta: Two Halves of One Craft Echo is the deployment that's working now. Delta is the change that makes the next one work better. Most engineers are good at one. FDEs do both. #### Scope, Validate, Deliver: The Three-Phase Engagement The rhythm every FDE engagement follows, and the specific moves at each phase that separate the engagements that ship from the ones that drift. #### Evals as Specification In FDE work the eval is the spec. How to write one that customers will sign, engineers can build against, and the model can be measured on. #### The FDE Interview Loop What the loop actually tests. The case study, the writing sample, the customer simulation. How to prepare for each, with worked examples. #### Pricing, Outcomes, and Contracts Day rates, project rates, outcome rates, and equity. When each works. The contractual language that protects both sides. #### Cargo Cults and Failure Modes The five ways FDE engagements fail. Each one is recoverable if you catch it by the second week. 05 · Why now ### A snapshot in motion Most of the canonical literature on the role was written between September 2025 and April 2026. Some of it was written while this book was being drafted. The Anthropic and Blackstone joint venture, the OpenAI Deployment Company, the Google Cloud hires, the Big Four repositionings were news, not history, when the manuscript was working through draft. The book is written to still be useful three years from now, but the snapshot dimension is real, and a reader picking it up in 2029 will need to interpret the contemporary references through whatever has happened by then. One principle is most likely to outlast the specific organizations and titles around it. **The artifact is the truth. The code shipping is the substance. The document is the imitation.** Everything else in the book is an unpacking of that. 06 · About the author ### About the author I'm Nasser Ghanemzadeh. 17+ years building products and companies across AI, SaaS, and fintech. Previously CEO/CPO at Nivo (acquired). Currently building Vectig solo with Claude Code. I'm not a Palantir alumnus. I did not invent the FDE motion and I make no claim to having shaped it. What I have done is seventeen years of customer-side software work with the particular intensity that comes from having most often been the one ultimately accountable for whether the software shipped, worked, and earned its keep. I also run a 10-business-day [AI Feature Sprint](https://ghanemzadeh.com/ai-feature-sprint/) for founder-led B2B SaaS teams: ship one narrow AI workflow into their product. Same playbook, sized for teams that don't have a $250K procurement process. More on the [home page](https://ghanemzadeh.com/). Also by Nasser: [Founder Mode](https://ghanemzadeh.com/book/). 07 · Read it ### Read the first chapters Join the launch list and the first chapters land in your inbox now. The full Kindle and paperback editions are available on Amazon today. You'll also get the newsletter. One email a week, easy unsubscribe. ### FAQ #### Who is this book for? Three readers in particular. Early-career engineers (3 to 5 years in) who recognize something in the FDE job description that fits how they want to work. Mid-career switchers (consultants, solutions architects, PMs, ex-founders) who suspect they already have most of the skills. Founders and hiring managers building an FDE function for the first time. If none of those descriptions fits you, the book has failed at scoping. #### How long is it and how is it organized? 304 pages, organized into five parts and 20 chapters: the landscape, the mindset and method, the craft, becoming an FDE, and the economy and future. Plus a workbook and annotated references at the back. #### Why this book instead of the other AI books? Most AI books are about the models. This one is about the practice of deploying them inside someone else's company, with all the customer-side friction that implies. It is also written from inside that work, not from the bench. #### When is it available? First edition, May 2026. The current version is 1.5, August 2026. The full Kindle and paperback editions are available now on Amazon, and you can subscribe to the launch list above to read the first chapters. #### What formats? Kindle and paperback are both available now on Amazon. You can also read the first chapters by subscribing to the launch list. #### Is this affiliated with Palantir, OpenAI, or Anthropic? No. Independent publication. The book discusses these organizations because they shape the role, but the author is not affiliated with any of them. Views are the author's alone. ### Keep exploring the role - [What is a Forward Deployed Engineer?](https://ghanemzadeh.com/forward-deployed-ai-engineer/) The definition, the salary data, and how the role works. - [How to become an FDE](https://ghanemzadeh.com/how-to-become-a-forward-deployed-ai-engineer/). The backgrounds that transfer and the step-by-step path in. - [FDE vs Solutions Engineer](https://ghanemzadeh.com/forward-deployed-ai-engineer-vs-solutions-engineer/). The closest neighboring role, compared side by side. - [Hire a Forward Deployed AI Engineer](https://ghanemzadeh.com/ai-feature-sprint/). The 10-business-day AI Feature Sprint for founder-led B2B SaaS. - [The best Forward Deployed Engineer books and resources](https://ghanemzadeh.com/best-forward-deployed-engineer-books/). What to read around this book, including the free essays and talks. Independent publication. Not affiliated with Anthropic, OpenAI, Google, Palantir, Y Combinator, Andreessen Horowitz, or any other organization mentioned in this book. Product and company names are trademarks of their respective owners. Views are the author's alone. --- # AI Discovery Day: 2-Hour Use-Case Scoping Session ($500) URL: https://ghanemzadeh.com/discovery-day/ ## Before we scope a Sprint, we scope the problem. A 2-hour working session with Nasser Ghanemzadeh. We look at your use case, your data, your codebase, and your team. By the end you have either a clean Sprint Brief or an honest "this isn't a Sprint yet, here's what to do first" memo. $500. Credited toward your Sprint if you continue within 14 days. [Book a Discovery Day →](https://cal.com/ghanemzadeh/discovery) Next available slot: visible on the booking page. One Discovery Day per week max. As covered in Forbes · TechCrunch · Al Jazeera · HuffPost ### What you walk away with By the end of the 2 hours, you have a working document with all of the following. Not a deck. Not a follow-up email two days later. The document, on the call, ready to forward to your team. - Use-case clarification, written in a single sentence using the Sprint Brief template. - Data and API readiness assessment for the candidate workflow. - Recommended workflow scope (or a recommended deferment, with reasoning). - A go / no-go memo on whether a Sprint is the right next step. - A Sprint Brief draft if there's a strong fit, including success criteria and out-of-scope items. - Honest answers about what fits in 10 business days and what doesn't. ### Who this is for - B2B SaaS founders or product leaders considering shipping an AI feature. - Teams that have tried building AI features and gotten stuck somewhere between prototype and production. - Teams that know AI should be part of their product but want a second opinion before committing engineering time. - Teams comparing Sprint vendors and wanting to validate fit before signing. ### Who this is not for - Teams looking for a generic AI strategy consultation. - Solo indie hackers without a B2B SaaS product. - Enterprise teams with formal procurement or RFP processes. - Anyone looking for a free 30-minute consultation. This is a paid scoping session, not a sales call. ### How a Discovery Day works The 2 hours are structured but not rigid. Rough shape: **Minutes 0–30: Your context.** Current product, team, AI experiments to date, what's worked and what hasn't, what's pulling the AI conversation forward inside your company. **Minutes 30–75: The candidate workflow.** Inputs, outputs, users, data sources, current manual process, what good output looks like, who reviews it before it ships. **Minutes 75–105: Feasibility.** What fits in a 10-day Sprint and what doesn't. Risk classification (low / medium / high). Integration complexity. Where the Day-5 prototype likely lands. **Minutes 105–120: The memo.** Go / no-go recommendation, Sprint Brief draft (if applicable), scoping language, next-step recommendation. Shared as a Google Doc you keep. About half of Discovery Days end with a Sprint recommendation. The other half end with "here's what to do first, come back when X is in place." Both are valid outcomes. The point of a Discovery Day is to find out which one you are, not to convince you to buy a Sprint. ### The credit toward Sprint If you continue to a full Sprint within 14 days of your Discovery Day, the $500 is credited against the Sprint price. So a Discovery Day → Sprint path costs $5,000 total at beta pricing, not $5,500. If we agree the Sprint isn't the right next step, the $500 covers the working document and the 2 hours. No upsell, no follow-up sequence, no retainer pitch. ### What I need from you before the call A short pre-call form, sent after booking. Three things: - **The workflow you're considering.** One paragraph is enough. Examples I've worked through with other founders: investor update assistant, runway scenario explainer, customer summary generator, support ticket triage, onboarding assistant. - **Your stack and data access.** What's your product built in, where does the relevant data live (Stripe, your DB, HubSpot, Linear, Notion, your CRM, your admin), and what AI provider you already use (Anthropic, OpenAI, Bedrock, Vertex) if any. - **The decision-maker question.** Who needs to approve a Sprint, and are they on the call. If they're not, a Discovery Day is fine, but the Sprint recommendation will be harder to act on. That's it. No data dumps, no NDAs to sign before the call. We sign a mutual NDA on the call itself if either side wants one. ### Why work with me I'm Nasser Ghanemzadeh, a former CEO/CPO and product leader with 17+ years building and scaling technology products. - Exited Nivo (200K+ users, 450% revenue growth, acquired). Press coverage in Forbes, TechCrunch, Al Jazeera, HuffPost. - Founding Head of Product at Pangouan. Co-founder of Iran Startups. Former accelerator lead at Finnova. - Currently building Vectig (AI-native startup finance), shipped daily with Claude Code and MCP. **Speaking on AI-native product development:** OMR Festival, re:publica, GITEX EUROPE. This sprint runs on production-grade AI engineering experience from my own products, not generic AI consulting theory. Based in Turkey, available across European, Middle Eastern, and North American business hours. [LinkedIn](https://www.linkedin.com/in/ghanemzadeh/) · [Email](mailto:ghanemzadeh@gmail.com) · [Vectig](https://vectig.com) ### FAQ #### What if we decide not to do a Sprint after the Discovery Day? Then you walk away with the working document, the go / no-go memo, and 2 hours of focused outside-perspective on your AI roadmap. No follow-up sales sequence. You're free to take the memo to another vendor, to your internal team, or to nobody at all. #### Do you sign an NDA? If either side wants a mutual NDA, we sign one on the call itself before getting into specifics. I don't ask for NDAs to be signed before booking, and I don't expect you to. #### What if our data isn't ready? That's one of the most common Discovery Day outcomes: "the workflow makes sense, but the data foundation isn't there yet." The memo will say exactly what to do first, usually a 2 to 4 week data-readiness project that you can run internally, and we revisit Sprint readiness when it's done. #### How is this different from a free consultation call? A free call is sales discovery. This is a paid working session. The $500 buys two things: focused 2-hour attention with no agenda except your problem, and a written document you keep regardless of outcome. The economics force both sides to take it seriously. #### What if I'm not sure I want a Sprint yet? Then a Discovery Day is exactly the right starting point. About half of them end with a deferment recommendation. That's a feature, not a failure mode. #### Can a co-founder or technical lead join? Yes. Up to three people from your side on the call. Beyond three the conversation slows down. If you want broader team alignment, the right pattern is: founder + tech lead on the Discovery Day, then I record a Loom walkthrough of the memo for everyone else. #### What if our use case is in a high-risk category (legal, medical, compliance, payments)? The Discovery Day still works, but the Sprint recommendation will likely be "not yet" or "redefine scope to a lower-risk adjacent workflow." High-risk first Sprints don't make sense for teams new to AI deployment. The memo will explain what does. #### Do you work with companies outside B2B SaaS? Mostly no. The methodology, examples, and Sprint structure are built around founder-led B2B SaaS at 5 to 50 people, post-product-market-fit. If you're outside that, the Discovery Day may still be useful but the Sprint may not be the right vehicle. ### Two hours, one document, a clear next step. Book a Discovery Day if you want a focused outside perspective on whether the AI feature you're considering is ready to ship in 10 business days. If you'd rather start with the Sprint details, read the full breakdown of the [10-day AI Feature Sprint](https://ghanemzadeh.com/ai-feature-sprint/). [Book a Discovery Day →](https://cal.com/ghanemzadeh/discovery) [Read the Sprint details](https://ghanemzadeh.com/ai-feature-sprint/) --- # Forward Deployed Engineer vs Solutions Engineer: Key Differences URL: https://ghanemzadeh.com/forward-deployed-ai-engineer-vs-solutions-engineer/ [Forward Deployed AI Engineer](https://ghanemzadeh.com/forward-deployed-ai-engineer/) ## Forward Deployed AI Engineer vs solutions engineer A Forward Deployed AI Engineer and a solutions engineer share the same technical, customer-facing instinct, but they work for different sides and ship different things. The Forward Deployed AI Engineer builds an AI workflow into the customer's own product and owns the result. The solutions engineer sells, demos, and integrates a vendor's product on the vendor's behalf. By [Nasser Ghanemzadeh](https://ghanemzadeh.com/). Last updated August 2026. ### The short answer If the code you ship lives in the customer's repository and the win is the customer's outcome, that is forward deployed work. If the code you ship is a demo or integration that helps close and onboard a customer onto your employer's platform, that is solutions engineering. The two roles look similar from the outside because both put a technical person in front of a customer. The forward deployed engineer vs solutions engineer question comes down to whose product gets built and who you answer to. ### Side by side | Dimension | Forward Deployed AI Engineer | Solutions engineer | | --- | --- | --- | | Works for | The customer's outcome, via a deployment company or a direct engagement | The software vendor selling the product | | Primary deliverable | A production AI workflow in the customer's codebase | Demos, proofs of concept, and integrations | | Where the code lives | The customer's product and repository | The vendor's platform and demo environments | | Measured on | A shipped workflow the customer's team can run and maintain | Pipeline, deals closed, and successful onboarding | | AI focus | Prompt and context design, data integration, evaluation rubrics | Showing how the vendor's AI features fit the customer's stack | | Compensation | Median base $174,000; median package $238,000; senior frontier packages above $500,000 (public data, 2026) | Varies by vendor; typically base plus commission (OTE), tied to sales and onboarding outcomes. | ### Where they overlap Both roles need to read a customer's real environment quickly, translate a business problem into a technical plan, and stay credible in a room with engineers and executives at the same time. Both live with ambiguity and incomplete data. The instincts transfer cleanly, which is why solutions engineers are one of the most common backgrounds for people moving into forward deployed AI work. ### How to choose between them Choose by what you want to own. If you want to close deals and help many customers adopt one product, solutions engineering fits. If you want to build the actual feature and own whether it works in production, the Forward Deployed AI Engineer role fits. One more practical signal: seen as solutions engineer vs forward deployed engineer, the trade is breadth against depth. Solutions engineering rewards breadth across a pipeline; forward deployed work rewards depth on one customer at a time. Neither is more senior than the other. They optimize for different outcomes. ### Applied AI Engineer vs Solutions Engineer Applied AI Engineer is Anthropic's title for the FDE-shaped role, the same job this page describes under a different name. So the same contrast applies: the applied AI engineer builds a production AI workflow inside the customer's systems and is measured on whether it ships, while the solutions engineer sells and integrates the vendor's product and is measured on the deal. If a posting says Applied AI, read it as forward deployed. ### Related - [What is a Forward Deployed AI Engineer?](https://ghanemzadeh.com/forward-deployed-ai-engineer/) - [How to become a Forward Deployed AI Engineer](https://ghanemzadeh.com/how-to-become-a-forward-deployed-ai-engineer/) - [The book: Forward Deployed AI Engineering](https://ghanemzadeh.com/books/forward-deployed-ai-engineering/) ### Frequently asked questions #### Can a solutions engineer become a Forward Deployed AI Engineer? Often, yes. Solutions engineers already have the customer-facing instinct and the integration skills. The shift is from selling and configuring a vendor's product to building and owning a production AI workflow inside the customer's codebase, plus the evaluation discipline to prove it works. #### Who does each role work for? A solutions engineer almost always works for the software vendor and is measured on sales and onboarding. A Forward Deployed AI Engineer works on the customer's outcome, whether employed by a deployment company or engaged directly, and is measured on shipping a workflow the customer's team can run. #### Does a solutions engineer write production code? Sometimes, but usually demos, proofs of concept, and integrations rather than features that live permanently in the customer's product. A Forward Deployed AI Engineer's main deliverable is production code in the customer's repository, with edge cases, logging, and a handoff. --- # What Is a Forward Deployed Engineer (FDE)? Definition, Salary, Role URL: https://ghanemzadeh.com/forward-deployed-ai-engineer/ The role ## What Is a Forward Deployed Engineer? **A Forward Deployed Engineer (FDE) is a software engineer who sits at the customer's site and fills the gap between what the product does and what the customer needs, shipping working software, today usually an AI workflow, directly into the customer's systems.** That definition comes from Bob McGrew, who spent 11 years at Palantir, where the role was invented, and later led research at OpenAI. This page covers what the work looks like, what it pays, who hires for it under which titles, and how to get in. [Read the book →](https://ghanemzadeh.com/books/forward-deployed-ai-engineering/) [See it as a service](https://ghanemzadeh.com/ai-feature-sprint/) By [Nasser Ghanemzadeh](https://ghanemzadeh.com/). Last updated August 2026. ### What does a Forward Deployed Engineer do? An FDE takes one narrow use case from a vague idea to a working AI feature inside the customer's product, end to end, and owns every step in between. The work is concrete, not advisory, and the deliverable is code in the customer's repository. A typical engagement runs through: - **Scoping.** Turn "we should add AI" into one workflow with a named user, a clear input, and a clear output. - **Architecture and UX.** Decide where the workflow lives in the product and how a person actually triggers and reviews it. - **Prompt and context design.** Build the model interaction, including the system prompt, the retrieved context, and the guardrails. - **Data integration.** Connect the workflow to the customer's real data and systems, often through the Model Context Protocol. - **Implementation.** Write production code in the customer's repository, with edge cases, fallbacks, error states, and logging. - **Evaluation.** Ship a written rubric that defines what good output looks like and how the team scores it over time. - **Handoff.** Hand the team code they can maintain, plus the rubric and a short playbook, so the workflow survives without the engineer. The output is code in the customer's repo. Not a dashboard. Not a report. Not a Loom. ### Why is it called "forward deployed"? The phrase is military language. Forward deployed units are stationed at the edge, close to the situation, with the autonomy to act without calling headquarters first. Applied to engineers, it means the same thing: you work inside the customer's organization, and you decide on the spot. Palantir made the term a job. It built its business on engineers who deploy into a customer's organization and deliver outcomes inside that customer's systems, not from a vendor booth. The model spread because it solved a specific failure: software that works in a demo and dies in the customer's real environment. In May 2026 the term went mainstream. OpenAI and Anthropic each launched a deployment subsidiary within a day of each other, both using the title Forward Deployed Engineer, both aimed at getting frontier models into enterprise products rather than selling API access and hoping. The AI in some job titles marks the shift: the capability being deployed is no longer just software, it is an AI workflow. ### What does a Forward Deployed Engineer earn? The median FDE total package is $238,000, on a median base salary of $174,000, per public compensation data from 2026. Senior packages at the frontier labs go above $500,000. | Compensation measure | Figure (2026) | | --- | --- | | Median base salary | $174,000 | | Median total package | $238,000 | | Senior frontier-lab packages | Above $500,000 | Figures are from public compensation data collected in 2026. Individual offers vary with seniority, location, and equity. ### FDE titles by company Almost nobody posts this job under one name. The same customer-embedded, ship-into-production role is hired under different titles at different companies, so searching for one string misses most of the market. | Company | What they call the role | | --- | --- | | Palantir | Echo and Delta, the two halves of its forward deployed practice | | OpenAI | Forward Deployed Engineer | | Anthropic | Applied AI | | Sierra | Agent Engineer | | Harvey | Implementation Engineer | | Decagon | Agent builder / agent SWE | Echo and Delta are defined in the [glossary](https://ghanemzadeh.com/glossary/), and the split between them is a core chapter of [the book](https://ghanemzadeh.com/books/forward-deployed-ai-engineering/). ### How is an FDE different from a solutions engineer? A solutions engineer works for the vendor and is measured on the sale; an FDE works on the customer's outcome and is measured on what ships. Both are technical and customer-facing, which is why the two roles are so often confused. | Role | What they ship | Where the work lives | Optimizes for | | --- | --- | --- | --- | | Forward Deployed AI Engineer | A working AI workflow built into the customer's product | The customer's codebase and product | A shipped outcome the customer's team can run and maintain | | Solutions engineer | Integrations, demos, and configuration of a vendor's product | The boundary between vendor and customer | Closing and onboarding the customer onto the vendor's platform | | ML engineer | Models, training pipelines, and inference infrastructure | The model and data platform | Model performance, scale, and reliability | | AI consultant | Strategy, roadmaps, and recommendations | Documents and meetings | Advice and direction, with execution left to others | For a side-by-side that goes deeper on the closest comparison, see [Forward Deployed AI Engineer vs solutions engineer](https://ghanemzadeh.com/forward-deployed-ai-engineer-vs-solutions-engineer/). ### Why did the FDE role explode in 2026? Because enterprises learned that buying AI is not the same as deploying it. An MIT study cited in the book found that 95% of enterprise AI pilots produce no measurable return. The FDE is the industry's answer to that gap, and in 2026 the money followed. - **May 4, 2026.** OpenAI launched a $10 billion deployment subsidiary and Anthropic a $1.5 billion one, on the same day, both hiring under the title Forward Deployed Engineer. - **May 16, 2026.** Business Insider reported that Indeed postings for the role grew from 643 in April 2025 to 5,330 in April 2026, more than 700% in a year. The same month, the title returned 55,000+ LinkedIn results in the US and over 100 postings on the YC job board. - **June 30, 2026.** AWS announced a $1 billion FDE program. - **July 2, 2026.** Microsoft launched Frontier: $2.5 billion and 6,000 embedded experts. - **July 30, 2026.** [TechCrunch reported](https://techcrunch.com/2026/07/30/forward-deployed-engineers-are-the-ai-industrys-latest-talent-obsession/) a C&T study projecting demand for FDEs to surge 2,100% by the end of 2026, against an estimated 2,000 qualified FDEs in total. ### What skills does the role require? Less model research, more product engineering and judgment. The hard part is usually deciding what to build and proving it works, not calling the API. - **Product engineering.** Shipping real features into a real codebase, with the discipline that production demands. - **Prompt and context design.** Getting reliable output from frontier models, including retrieval and structured context. - **Data integration.** Connecting models to messy customer systems and APIs, often via [MCP](https://ghanemzadeh.com/glossary/#mcp). - **Evaluation.** Defining what good output is and scoring it, so quality is measured rather than asserted. - **Customer-facing judgment.** Scoping under pressure, saying no to the wrong workflow, and protecting a fixed timeline. ### How do you become a Forward Deployed Engineer? Most people who do this well arrive from product engineering, solutions or consulting, or a founder background, then prove they can ship one narrow AI workflow end to end. The fastest credible path is to build and ship a real workflow, write the evaluation rubric for it, and be able to walk someone through the code. For a step-by-step version, see [how to become a Forward Deployed AI Engineer](https://ghanemzadeh.com/how-to-become-a-forward-deployed-ai-engineer/). ### Forward Deployed AI Engineering for founder-led B2B SaaS The enterprise version of this role is built for Fortune 500 procurement. The same work, sized for a 5-to-50-person founder-led SaaS team, is the AI Feature Sprint: one narrow AI workflow shipped into the product in 10 business days, fixed scope and fixed price. That is the work I do. Every pattern I ship into a customer's codebase has already shipped in [Vectig](https://vectig.com), my own AI-native product and live R&D lab. If you want to see the engagement, start with the [AI Feature Sprint](https://ghanemzadeh.com/ai-feature-sprint/) or a [Discovery Day](https://ghanemzadeh.com/discovery-day/). The book ### A working guide to the role [Forward Deployed AI Engineering: A Working Guide to the Hottest Job in Software](https://ghanemzadeh.com/books/forward-deployed-ai-engineering/) is the long-form version of this page: what the role is, where it came from, how it is practiced, and how to do it well. Written from inside the work, in the months after OpenAI and Anthropic put the title on the map. For the wider reading list around the role, see [the best Forward Deployed Engineer books](https://ghanemzadeh.com/best-forward-deployed-engineer-books/). ### About the author I'm Nasser Ghanemzadeh, a Forward Deployed AI Engineer for founder-led B2B SaaS. 17+ years building products and companies across AI, SaaS, and fintech. Previously CEO/CPO at Nivo (acquired, 200K+ users, 450% revenue growth). Work covered in Forbes, TechCrunch, Al Jazeera, and HuffPost. I run the [AI Feature Sprint](https://ghanemzadeh.com/ai-feature-sprint/), build [Vectig](https://vectig.com) in public with Claude Code, and wrote the book on this role. More on the [home page](https://ghanemzadeh.com/). ### Frequently asked questions #### Do Forward Deployed Engineers write production code? Yes. The defining deliverable is working code in the customer's repository, with edge cases, fallbacks, error states, logging, and a handoff the customer's team can maintain. Demos and decks are what the role exists to replace. #### Is Forward Deployed Engineer a good career in 2026? The demand data is unusually one-sided. Indeed postings grew more than 700% in a year per Business Insider, TechCrunch reports projected demand growth of 2,100% by the end of 2026 against an estimated 2,000 qualified FDEs, and the median package is $238,000. The constraint is proving you can do the work, not finding openings. #### Do you need a machine learning background to become an FDE? No. The role applies existing models rather than training them. The core skills are product engineering, prompt and context design, data integration, evaluation, and customer-facing judgment. A research or MLOps background helps in some engagements but is not the entry requirement. #### How much travel does the role involve? More than most software roles. Anthropic's listing puts travel at 25 percent. OpenAI expects its FDEs on customer sites a few days a week during an engagement. At Palantir, Nabeel Qureshi describes four to five days a week at the customer's office as the high end; two to three is more typical. #### Is a Forward Deployed Engineer the same as an applied AI engineer? Functionally, yes. Applied AI is Anthropic's title for the FDE-shaped role, the same way Sierra says Agent Engineer and Harvey says Implementation Engineer. The employer's label changes; the work of shipping AI into a customer's production systems does not. ### Sources - TechCrunch, "Forward deployed engineers are the AI industry's latest talent obsession," July 30, 2026. [techcrunch.com](https://techcrunch.com/2026/07/30/forward-deployed-engineers-are-the-ai-industrys-latest-talent-obsession/) - Business Insider, reporting on Indeed job-posting data for Forward Deployed Engineer roles, May 16, 2026. - Public compensation data for Forward Deployed Engineer roles, 2026. - MIT study on enterprise AI pilot outcomes, as cited in *Forward Deployed AI Engineering*. - Bob McGrew on the Forward Deployed Engineer role. McGrew spent 11 years at Palantir and later led research at OpenAI. - Anthropic and OpenAI job listings for the role (travel expectations). - Nabeel Qureshi on Palantir's forward deployed model, Lenny's Newsletter, May 2025. [lennysnewsletter.com](https://www.lennysnewsletter.com/p/inside-palantir-nabeel-qureshi) Newsletter ### Notes from the edge of building One email a week on Forward Deployed AI Engineering, building AI-native SaaS solo with Claude Code, and the unglamorous middle of shipping. No spam. Unsubscribe in one click. --- # Forward Deployed AI Engineering Glossary | Nasser Ghanemzadeh URL: https://ghanemzadeh.com/glossary/ [Forward Deployed AI Engineer](https://ghanemzadeh.com/forward-deployed-ai-engineer/) ## Forward Deployed AI Engineering glossary The working vocabulary of Forward Deployed AI Engineering for founder-led B2B SaaS. Plain definitions of the role, the offer, the deliverables, and the tools, written the way they are actually used. By [Nasser Ghanemzadeh](https://ghanemzadeh.com/). Last updated August 2026. **Forward Deployed AI Engineer (FDE)** An engineer who embeds inside a customer's team and ships AI capabilities directly into the customer's product and codebase, rather than delivering a deck, a dashboard, or a demo. The delivery model comes from Palantir. OpenAI and Anthropic both launched deployment subsidiaries using it in May 2026. See the full explainer: [what is a Forward Deployed Engineer?](https://ghanemzadeh.com/forward-deployed-ai-engineer/) **Echo (Palantir)** At Palantir, the embedded analyst with deep domain expertise who owns the customer relationship, one half of its two-role FDE model. In [Forward Deployed AI Engineering](https://ghanemzadeh.com/books/forward-deployed-ai-engineering/), Echo also names one half of the craft: the deployment that is working now. **Delta (Palantir)** At Palantir, the deployed engineer who ships code inside the customer's environment; the company describes the responsibilities as similar to a startup CTO's. In *Forward Deployed AI Engineering*, Delta also names the other half of the craft: the change that makes the next deployment work better. **AI Feature Sprint** A ten-business-day engagement that ships one narrow AI workflow into a customer's product. Fixed scope, fixed price, end-to-end delivery and handoff. See the [AI Feature Sprint](https://ghanemzadeh.com/ai-feature-sprint/). **Discovery Day** A two-hour paid working session, priced at $500, to assess whether an AI use case is useful, buildable, and worth a Sprint. The output is a go/no-go memo within 24 hours, credited toward a Sprint if the customer continues. See [Discovery Day](https://ghanemzadeh.com/discovery-day/). **Workflow** A specific process a user runs, with a clear input and a clear output, that produces a measurable improvement. It is the unit of work shipped in a Sprint. Not a feature, and not a platform. **Output Quality Rubric** A practical checklist, usually on a 1-to-5 scale, that defines what good output from an AI workflow looks like, how the customer's team reviews it, and how the workflow improves over time. It ships as a deliverable with every Sprint. **Sprint Brief** The one-to-two-page document written on Day 1 of a Sprint and signed off by the customer's decision-maker, defining scope, success criteria, deliverables, and what is explicitly out of scope. It is part of the contract. **Workflow Playbook** A short handoff document covering a workflow's purpose, inputs, expected outputs, review steps, edge cases, and maintenance guidance, so the customer's team can run and improve it without the engineer. **Model Context Protocol (MCP)** The open standard Anthropic introduced for letting AI models talk to external systems and data. Many AI workflows connect to a customer's tools and data through MCP servers. **Claude Code** Anthropic's command-line tool for agentic coding. A primary delivery tool for shipping AI workflows directly into a codebase. **AI-native product** A product built around AI workflows from the start, rather than a traditional product with an AI feature added on later. **Vectig** Nasser Ghanemzadeh's AI-native startup finance product: cash-flow forecasting, runway scenarios, and investor update drafting. It serves as the live R&D lab where workflow patterns are proven before they ship to customers. See [vectig.com](https://vectig.com). ### Related - [What is a Forward Deployed AI Engineer?](https://ghanemzadeh.com/forward-deployed-ai-engineer/) - [How to become a Forward Deployed AI Engineer](https://ghanemzadeh.com/how-to-become-a-forward-deployed-ai-engineer/) - [The book: Forward Deployed AI Engineering](https://ghanemzadeh.com/books/forward-deployed-ai-engineering/) --- # How to Become a Forward Deployed Engineer (FDE) in 2026 URL: https://ghanemzadeh.com/how-to-become-a-forward-deployed-ai-engineer/ [Forward Deployed AI Engineer](https://ghanemzadeh.com/forward-deployed-ai-engineer/) ## How to become a Forward Deployed AI Engineer You become a Forward Deployed AI Engineer by proving you can take one narrow AI use case from a vague idea to a working feature in a real product, then explain every decision behind it. The fastest credible path is to build and ship one workflow end to end, evaluation rubric included, and be able to walk someone through the code. By [Nasser Ghanemzadeh](https://ghanemzadeh.com/). Last updated August 2026. ### Who already has a head start Most people who do this well arrive from one of three backgrounds: product and software engineering, solutions engineering or consulting, or a founder background. Product engineers bring the ability to ship into a real codebase. Solutions engineers and consultants bring the customer-facing instinct. Founders bring judgment about what is worth building. You do not need all three. You need to be honestly strong in one and willing to build the others. ### The path, step by step - **Get genuinely good at shipping software.** The role is product engineering first. If you cannot ship a feature into a real codebase with edge cases and error states, start there. - **Use frontier models every day.** Work with the current Anthropic and OpenAI models until their behavior is intuitive. Adopt an agentic coding tool like Claude Code as your default way of building. - **Learn prompt and context design.** Reliable output is an engineering problem: system prompts, retrieved context, structured outputs, and guardrails. Treat it like code, not like magic words. - **Learn to connect models to real systems.** Understand the [Model Context Protocol (MCP)](https://ghanemzadeh.com/glossary/#mcp) and how to wire a model into a customer's data and tools safely. - **Build one narrow workflow, end to end.** Pick a real use case with a named user, a clear input, and a clear output. Ship it inside a real product or a convincing clone. Narrow beats ambitious. - **Write the evaluation rubric.** Define what good output looks like on a simple scale and score against it. This discipline is what separates an engineer from someone who got one lucky demo. - **Develop customer-facing judgment.** Practice scoping under pressure, saying no to the wrong workflow, and protecting a fixed timeline. The hardest skill is resisting the pull of a vague request. - **Show the work.** Record a short walkthrough of the code, the prompts, and the rubric. Being able to defend every decision out loud is the real credential. ### What to study, and what to skip Study product engineering, prompt and context design, data integration, and evaluation. You can skip the machine learning research track. The role applies existing models; it does not train them. A research or MLOps background is useful in some engagements, but it is not the entry requirement, and chasing it first is the most common way to delay the thing that actually gets you hired: a shipped workflow. Go deeper ### The book [Forward Deployed AI Engineering: A Working Guide to the Hottest Job in Software](https://ghanemzadeh.com/books/forward-deployed-ai-engineering/) covers the full path in depth, including a part on becoming an FDE, plus a workbook. It is written from inside the work. ### Related - [What is a Forward Deployed AI Engineer?](https://ghanemzadeh.com/forward-deployed-ai-engineer/) - [Forward Deployed AI Engineer vs solutions engineer](https://ghanemzadeh.com/forward-deployed-ai-engineer-vs-solutions-engineer/) - [Forward Deployed AI Engineering glossary](https://ghanemzadeh.com/glossary/) ### Frequently asked questions #### What background do most Forward Deployed AI Engineers come from? Three backgrounds are most common: product or software engineers who can ship into a real codebase, solutions engineers and consultants who already work with customers, and ex-founders who have built and sold something. The shared trait is the ability to ship and to make decisions in front of a customer. #### How long does it take to become a Forward Deployed AI Engineer? If you can already ship software, the AI-specific layer (prompt and context design, model integration, evaluation) takes weeks of focused practice, not years. The honest gate is not study time. It is whether you have shipped one real AI workflow end to end and can defend every decision in it. #### What should I build to prove I can do the work? Build one narrow workflow with a named user, a clear input, and a clear output, inside a real product or a convincing clone. Ship it, write an evaluation rubric for it, and record a short walkthrough of the code and prompts. That single artifact is more persuasive than any certificate. --- # Forward Deployed AI Engineering Newsletter | Nasser Ghanemzadeh URL: https://ghanemzadeh.com/newsletter/ 01 · The Newsletter ## Notes from the edge of building Weekly notes on building AI-native SaaS solo. Product, distribution, founder ops, and the unglamorous middle. Free. No spam. Unsubscribe in one click. 02 · Latest ### Latest articles - [August 12, 2026 #### Validation Is Not Optional: The Three-Phase Engagement Scope, validate, deliver. The middle phase is the one everyone wants to skip, and the one that decides whether the engagement is worth attempting at all.](https://ghanemzadeh.substack.com/p/validation-is-not-optional-the-three?utm_source=ghanemzadeh.com&utm_medium=newsletter_page) - [August 6, 2026 #### I Fact-Checked My Own Book. It Did Not Go Perfectly. What re-verifying every number in FDE revealed, what the $9 billion summer of forward deployment means, and what version 1.5 adds.](https://ghanemzadeh.substack.com/p/i-fact-checked-my-own-book-it-did?utm_source=ghanemzadeh.com&utm_medium=newsletter_page) - [August 5, 2026 #### Echo and Delta: The Two Modes of One Craft Palantir split the role in two for a reason. Most AI startups compress it into one person, and the discipline is knowing which mode you're in at any given moment.](https://ghanemzadeh.substack.com/p/echo-and-delta-the-two-modes-of-one?utm_source=ghanemzadeh.com&utm_medium=newsletter_page) - [July 31, 2026 #### The $29 question Pieter Levels, Fireship, and the end of the micro-SaaS playbook.](https://ghanemzadeh.substack.com/p/the-29-question?utm_source=ghanemzadeh.com&utm_medium=newsletter_page) - [July 29, 2026 #### The FDE Arbitrage: Why Failed Founders Are the Most Undervalued Talent in AI The labor market discounts failed founders heavily. FDE hiring managers who know better are quietly capturing the spread.](https://ghanemzadeh.substack.com/p/the-fde-arbitrage-why-failed-founders?utm_source=ghanemzadeh.com&utm_medium=newsletter_page) - [July 23, 2026 #### Gravel Roads and Paved Highways: The Palantir Pattern That Runs the AI Industry For twenty years the industry dismissed Palantir's operating model as a curiosity. Then it became the consensus in about eighteen months.](https://ghanemzadeh.substack.com/p/gravel-roads-and-paved-highways-the?utm_source=ghanemzadeh.com&utm_medium=newsletter_page) - [July 17, 2026 #### The great software rewrite AI coding agents are making it cheaper to rebuild software in Rust, Go, C, and C++. But faster code is only part of the story.](https://ghanemzadeh.substack.com/p/the-great-software-rewrite?utm_source=ghanemzadeh.com&utm_medium=newsletter_page) - [July 15, 2026 #### What Exactly Is a Forward Deployed AI Engineer? It is not a sales engineer, a consultant, or a CSM with a GitHub account. Every word in the title is doing work.](https://ghanemzadeh.substack.com/p/what-exactly-is-a-forward-deployed?utm_source=ghanemzadeh.com&utm_medium=newsletter_page) - [July 8, 2026 #### Why 95% of Enterprise AI Pilots Fail (And It's Not the Models) The most important number in enterprise AI is not a benchmark score. It is the gap between pilot and production.](https://ghanemzadeh.substack.com/p/why-95-of-enterprise-ai-pilots-fail?utm_source=ghanemzadeh.com&utm_medium=newsletter_page) - [July 1, 2026 #### Forward Deployed Engineering at $200/Hour The brutal math of fixed-price AI work, and why every operator who underprices this work goes broke profitably.](https://ghanemzadeh.substack.com/p/forward-deployed-engineering-at-200hour?utm_source=ghanemzadeh.com&utm_medium=newsletter_page) - [June 24, 2026 #### What Forward Deployed AI Engineering Actually Is The most important new role in B2B software is the one almost nobody can define correctly. By the end of this article, you will.](https://ghanemzadeh.substack.com/p/what-forward-deployed-ai-engineering?utm_source=ghanemzadeh.com&utm_medium=newsletter_page) - [June 17, 2026 #### The Five Mistakes Every Forward Deployed AI Engagement Makes Operational wisdom from running engagements yourself, watching other operators run theirs, and seeing in-house teams attempt them. These five mistakes show up over and over.](https://ghanemzadeh.substack.com/p/the-five-mistakes-every-forward-deployed?utm_source=ghanemzadeh.com&utm_medium=newsletter_page) [All posts on Substack →](https://ghanemzadeh.substack.com/archive?utm_source=ghanemzadeh.com&utm_medium=newsletter_page) 03 · What you'll get ### What you'll get A short, weekly email from someone in the trenches building AI-native SaaS solo with Claude Code. No frameworks borrowed from Twitter threads. No promises of overnight scale. Just what is working, what is not, and what I am trying next. 04 · What I write about ### Five topics - **Forward Deployed AI Engineering in practice.** What works in the field, what the labs are getting right and wrong. - **Building AI-native products solo.** Vectig as the live case study, including the failures, plus lessons from Sengi, an earlier build I paused. - **Distribution without paid ads.** LinkedIn, warm outreach, the referrer network. - **Founder operations.** Cash flow, hiring, scope, the unglamorous middle. - **Claude Code workflows.** The specific patterns that make a one-person shop ship like a small team. 05 · A taste ### A taste of the voice > One narrow workflow. Not a platform, not an AI strategy, not a copilot, not a chatbot the marketing team thought of. One specific use case with a clear input, a clear output, and a person on the customer's team who will actually use it. Examples we'll come back to: an investor update assistant, a customer summary generator, a support ticket summarizer. > The workflow lives inside the customer's existing software. A feature in their app, an internal admin tool, or a production-ready pull request their team can merge. Not a standalone tool we host. Not a demo on a Loom. Code in their repo. End-to-end. > From the Forward Deployed AI Onboarding Handbook. Representative of this newsletter's voice. 06 · About ### About I'm Nasser Ghanemzadeh. 17+ years building products and companies across AI, SaaS, and fintech. Previously CEO/CPO at Nivo (acquired). Currently building Vectig solo with Claude Code, and running a 10-business-day [AI Feature Sprint](https://ghanemzadeh.com/ai-feature-sprint/) for B2B SaaS teams. Also wrote [Forward Deployed AI Engineering](https://ghanemzadeh.com/books/forward-deployed-ai-engineering/) and [Founder Mode](https://ghanemzadeh.com/book/). More on the [home page](https://ghanemzadeh.com/). 07 · Get the next one ### Subscribe One email a week. Built for founders and operators shipping AI-native products in small teams.