Forward deployed engineer for founder-led B2B SaaS
Your first working AI workflow, in your product, in ten business days.
Delivered by Eupraxic, the forward deployed AI engineering firm founded by Nasser Ghanemzadeh.
OpenAI and Anthropic each launched a new company this year to put engineers inside their customers. I do the same work as an independent forward deployed engineer, sized for a founder-led team: one narrow workflow, scoped on Day 1, running on your data by Day 5, in your repo by Day 10. I pick the model that fits the job, not the one I'm paid to resell.
Built by Nasser Ghanemzadeh, ex-CEO/CPO of Nivo (acquired, 200K+ users) · building Vectig in public · featured in Forbes, TechCrunch, Al Jazeera
Next kickoffs: Tuesday November 3 (deposit by Thursday October 29) · Tuesday November 17 (deposit by Thursday November 12) · One Sprint at a time · $5,000 through the Tuesday November 17 kickoff, $7,500 from Tuesday December 1
An AI workflow running in my own product, end to end, and how one gets built into yours. 16 minutes.
What is the FDE Sprint?
The FDE Sprint is Eupraxic's fixed-scope engagement in which a forward deployed engineer ships one AI workflow into your product in ten business days. It's built for founder-led B2B SaaS teams of 5 to 50 people, and the workflow runs on your data. It's a fixed price, $5,000, refunded in full if there's no working prototype by Day 5 or no working workflow by Day 10.
What is a forward deployed AI engineer?
A forward deployed AI engineer works inside the customer's codebase and ships AI features into their product, instead of advising from outside. The term comes from Palantir. In 2026, OpenAI and Anthropic both built teams around it to deploy their models inside customers. The FDE Sprint is the founder-tier version of that work.
Your team knows AI matters. The hard part is shipping the right thing.
Most SaaS teams are already experimenting: someone has tried Claude Code, built a prototype, or floated a chatbot. The experiments stall because a useful AI feature needs more than a demo. It needs a clear workflow, the right data, a narrow scope, and an implementation path the team can actually ship.
That's what the Sprint produces: one managed AI workflow with a clear use case, a review process, and a handoff your team can run with.
03 · The model
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. The deliverable is working code in the customer's repo, not a deck about it. The term comes from Palantir, where it has been the dominant model for two decades. In May 2026, Anthropic announced a new AI services company with Blackstone, Hellman & Friedman, and Goldman Sachs, in which its Applied AI engineers build Claude systems for mid-sized companies. Days later, OpenAI launched the OpenAI Deployment Company, with more than $4 billion of investment, to place Forward Deployed Engineers inside customer organizations. The FDE 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.
Investor update assistant. Reads the month's metrics from Stripe or your finance sheet, plus a few sentences from the founder, and drafts the update in your usual format. The founder edits and sends.
Runway scenario explainer. Takes a proposed change (a hire, a price change, a churn assumption), runs it against current burn and revenue, and writes a plain-language note on what it does to runway. Someone checks the numbers before anyone acts on them.
Customer summary generator. Pulls an account's CRM notes, usage events, and open tickets, and writes a one-page summary into the CRM record before a renewal call. The account owner reads it before the call.
Support ticket summarizer. Reads a long Zendesk or Intercom thread and writes the issue, urgency, what has been tried, and a suggested next step into the ticket sidebar. The agent accepts it, edits it, or ignores it.
Report generator. Turns a week of product data or notes into your weekly customer or internal report, in your template, as a draft. Whoever sends it reviews it first.
Onboarding assistant. Uses a new account's setup data to suggest the next step in your product's first key workflow, inside the app. It suggests; the user clicks.
Thirty more, grouped by team, with the data each one needs and whether it fits a first Sprint: AI use cases for B2B SaaS.
05 · Proof
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. In my own test runs, open to send took about 8 minutes, against the 90+ minutes a blank-page update usually takes.
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.
The numbers I can show you today
Every Sprint workflow ships with an output quality rubric, so each engagement produces its own before-and-after numbers. No Sprint has closed yet, and Vectig is still early, so the two rows below come from my own test runs of the Investor Update Assistant during development, not from customer usage.
Metric
Before
After
Sample
Investor update draft time, open to send
90+ minutes of blank-page writing
8 minutes
My test runs during development
Output quality rubric score
Not measured
4-of-5 average across accuracy, clarity, honesty, and ask-coverage
30 test runs during development
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 Sprints complete, their numbers land 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.
06 · Day 0
Day 0: the readiness check
Before a Sprint starts, we check your use case, data, codebase, and team. Below is what a typical founder-led SaaS team looks like at the start. The scorecard and the Scoping Call map these gaps, so the Sprint begins on Day 1 with them already known.
~/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: 15-minute Scoping Call to pick the workflow and define success criteria.
The Sprint has a fixed price. What the workflow is worth depends on your numbers, so we work them out on the Scoping Call and write them into the Brief.
For an internal workflow the math is short: the hours a week the work takes today, times the loaded hourly cost of the people doing it, times 52. That's what the work costs you in a year. Divide the Sprint price by what the workflow saves each week, and you have the payback period.
Worked example, with example inputs. Four support agents each spend ten hours a week reading long ticket threads before they can reply. A summarizer that halves that saves 20 hours a week. At a loaded cost of $40 an hour, that's $800 a week, or $41,600 a year. The Sprint pays for itself in under ten weeks at either published price.
For a customer-facing feature, hours saved is the wrong measure. The value is in the deals the feature helps close or the accounts it helps keep, and the Brief names that metric instead. If neither number is bigger than the Sprint price, I'll say so on the call.
07 · The offer
One workflow, ten business days, one fixed price
Days 1 to 10
FDE Sprint
$5,000fixed price
10 business days. One narrow AI workflow shipped into staging, production, or a production-ready pull request.
Scope and design
AI use-case selection, feature scope, UX flow, product and technical architecture, prompt and data design.
Build
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.
Handoff
Team handoff and enablement session, Loom walkthrough, handoff notes, 30-day roadmap, output quality rubric, reusable workflow playbook.
Support
30 days of async support after handoff. Something breaks in week three, you write, I fix.
Guarantee
No working prototype on your data by Day 5? Stop the Sprint, full refund. The workflow in your signed Brief isn't running to its acceptance criteria by Day 10? Full refund.
Price
$5,000 for Sprints kicking off through Tuesday November 17. From the Tuesday December 1 kickoff, $7,500. Half on signing, half on Day 10. The deposit is invoiced on signing.Pay the deposit.
Next workflow
Book a second Sprint within 6 months of your handoff and it runs at the price you paid for the first, whatever the published price is by then.
Prefer a full two-hour scoping session before committing? The Discovery Day is $500, credited to the Sprint.
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.
Why Day 1 decides the Sprint
I learned this on my own product first. The first AI features I built for Vectig were based on the wrong hypothesis: they solved a problem founders didn't actually have. It took conversations with founders, more research, and several pivots before the workflow pointed at something they really need.
Most stalled AI features go wrong the same way, before any code is written. So the Sprint starts by pinning the workflow down in one signed sentence, with the person who'll use it, and nothing gets built until that sentence holds.
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."
Under the sentence goes one value line: the hours a week the work takes today, times the loaded hourly cost, times 52. For a customer-facing feature, it's the deal or retention metric the workflow moves.
Value line for the example. 82 minutes saved a month is not where the money is. The value is an update that goes out every month, on time, which keeps investors close before the next round.
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 the deposit.
I take one active sprint at a time. Kickoffs run on the fixed dates listed at the top of this page, and the paid deposit locks yours.
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
09 · Who's behind it
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. Founder and first managing director of Finnova.
Currently building Vectig (AI-native startup finance), shipped daily with Claude Code and MCP.
The Sprint method comes out of shipping these workflows in my own products first. Eupraxic is remote-first, based in Turkey (UTC+3), and works across North American, UK and European business hours.
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.
How long does it take to ship an AI feature in a SaaS product?
Ten business days for one narrow workflow, if codebase access and sample data are ready on Day 1. Day 1 scopes it, Days 2 and 3 design it, a working prototype runs on your data by Day 5, and Day 10 is handoff. A broader AI feature takes longer; the Sprint ships its first narrow slice.
How do I know if my AI feature idea is feasible?
It's feasible in ten days if one person runs it, its data sits in one system you can export, it fits your existing APIs, and a person reviews the output before anything leaves. The Workflow Scorecard checks this in two minutes across eight dimensions and four readiness checks.
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?
$5,000 for Sprints kicking off through Tuesday November 17, and $7,500 from the Tuesday December 1 kickoff. Half on signing, half on Day 10. The optional Discovery Day is $500, credited to the Sprint.
How do I justify the cost to my co-founder or board?
With your own numbers. For an internal workflow: the hours a week the work takes today, times the loaded hourly cost, times 52. Divide the Sprint price by the weekly saving and you have the payback period. In the worked example, a support summarizer that saves 20 hours a week pays back in under ten weeks. For a customer-facing feature, use the deal or retention metric it moves. Either way, the number goes into the value line of the Brief draft you get after the Scoping Call, so the case is written down before you pay anything.
Why is the Scoping Call free?
Some founders who take it ask me to run the Sprint. That's how I earn. The rest keep the scope.
What happens if you can't deliver?
A full refund, at either of two checkpoints. No working prototype on your data by Day 5? Stop the Sprint, full refund. The workflow in your signed Brief isn't running to its acceptance criteria by Day 10? Full refund.
Do you have case studies?
Not yet. This is a new offer. What you can see is the same kind of workflow built into Vectig, my own product, with numbers from my own test runs. That's why the price is $5,000 through November and why the guarantee is a full refund.
Why pay you instead of using your own engineers?
Speed, mostly: 10 days instead of six weeks of internal roadmap displacement, when pulling two engineers off the roadmap costs roughly $30K in salary before the opportunity cost. The rest is judgment. Knowing which workflow to ship first is harder than building it, and a fixed price on a fixed timeline keeps the scope honest.
We tried building an AI feature and it stalled. Why would this be different?
Most AI features stall in the same three places: a demo that worked on five examples, no agreement on what good output looks like, and nobody owning the last stretch to production. The Sprint is built against all three. Day 1 ends with a signed Brief that names one user, one input, one output, and the acceptance criteria. Day 5 is a working prototype on your real data, or you stop and get your money back. Days 6 to 9 are the edge cases, error states, and prompt tuning that demos skip, scored against the output rubric.
How is this different from an AI consulting firm?
This Sprint ships a working AI workflow into your codebase by Day 10, where AI consulting firms deliver decks and roadmaps. The category is "Forward Deployed AI Engineering": the same shape of work OpenAI's Deployment Company sells to large organizations and Anthropic's new services company to mid-sized ones, at a different price point.
Why hire an independent FDE instead of a model vendor's deployment team?
A vendor's forward deployed engineers work for the vendor, so the natural outcome is more of that vendor's platform and more spend on it. An independent FDE picks the model per workflow, keeps prompts and model settings in a config file your team controls, and can switch models when a better or cheaper one ships. Vendor teams also start at enterprise minimums; the FDE Sprint is sized for a 5 to 50 person company.
Is the FDE Sprint FDE as a service?
Yes, in fixed-scope form. Most FDE-as-a-service providers sell an engineer's time: by the hour through a talent marketplace, or by the month from a consultancy, where published prices run about $15,000 to $40,000 a month. The Sprint sells one shipped workflow instead: a fixed price, ten business days, and a full refund if it doesn't run. The FDE-as-a-service market map compares 34 providers and every price they publish.
Should I hire a fractional forward deployed engineer instead?
A fractional FDE works part-time on a retainer, often two fixed days a week for three months or more, with scope that moves as the work goes. That fits ongoing AI work once you know what to build. If your first workflow hasn't shipped yet, a Sprint settles what to build and proves it on your data in ten days. A fractional engagement or a hire can follow, with a working example to point at.
What does it cost to hire a forward deployed engineer for a project?
In October 2026, published prices for outside FDE work run from $23 an hour for Asia-based contractors to $50 to $99 an hour from European firms. One provider's rate card puts a mid-level US contractor at $119 to $185 an hour. Embedded engineers from consultancies cost about $15,000 to $40,000 a month, and a senior builder through A.Team about $25,000 a month. The FDE Sprint is a fixed $5,000 for one workflow. The FDE cost calculator compares a hire, a contractor, and a Sprint.
What kinds of AI workflows do you build?
Narrow workflows with one input, one output, and one user: investor update assistants, runway scenario explainers, customer summary generators, support ticket summarizers, report generators, and onboarding assistants. The common shape: narrow input, narrow output, single user, measurable improvement, human-in-the-loop review. Thirty more examples, by team, are in AI use cases for B2B SaaS.
Do you build AI agents?
Yes, when the workflow needs one: when the model has to call tools, look things up in your systems, or take several steps to produce its output. Many Sprint workflows reach your systems through MCP servers for exactly that. What I don't build in a first Sprint is an agent that acts on customers without a person approving it. Every v1 keeps a human in the loop, and autonomy comes later, when your team decides it's earned.
What if our data isn't ready?
Then a Sprint isn't the right starting point yet. The scorecard usually flags it in two minutes, and the Scoping Call will say so explicitly and recommend what to do first.
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.
What do you need from us?
Read-only codebase access, sample data, and one decision-maker who answers within a business day. About five hours of the founder's time across the ten days, most of it on Day 1.
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?
Your team runs the workflow. The rubric and playbook are built so they can maintain it without me, and most teams do. If you want a second workflow, book another Sprint within 6 months of handoff and it runs at the price you paid for the first. There's no retainer to keep the first one running.
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; OpenAI and Anthropic both built deployment teams around it in 2026.
FDE Sprint
This offer. FDE stands for forward deployed engineer. 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, the value line, 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.
