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, and the full guide in the book, Forward Deployed AI Engineering: A Working Guide to the Hottest Job in Software.
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 outcome from the live Vectig implementation: average draft-to-send time 8 minutes, vs. 90+ minutes for blank-page writing. Output quality rubric: 4-of-5 average across accuracy, clarity, honesty, and ask-coverage dimensions in the first 30 sample runs.
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.
$ 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.
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
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) and Sengi (financial OS for solopreneurs), both 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.
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.
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.