How I price a Forward Deployed Engineer engagement
How I scope and price FDE-style AI work — discovery, thin slice, harden, handoff — mapped to the published engagement tiers, without fake day rates or open-ended retainers.
*Scope the loop, not the hours. Pay for an operable system, not demo theatre.*
People ask for a "day rate for AI." That question usually means they are buying **time**. Forward deployed work buys an **outcome shape**: something running on your stack, hardened enough for operators, with a handoff that does not depend on me living in your Slack. How I price follows that shape. Role definition: the Forward Deployed Engineer hub. Delivery loop: what an FDE actually does.
The short version
I price around a fixed path — **discover, thin slice on live data, harden with evals and guardrails, hand off** — not unlimited tickets. The tiers are public on services: an **AI Architecture Sprint from $2,000**, a **Proof-of-Value Build at $8K–$20K**, a **Production Build from $25K**, and a **Fractional AI Lead from $5K/month**. What varies inside those bands is risk, not hours: identity complexity, data mess, whether the agent writes or spends, and how much compliance shape is involved.
I do not sell strategy with no path to production code, and I do not sell cheap ticket bundles that pretend to include discovery, SSO, security review and evals.
What you are buying
| Included in FDE-shaped work | Not included unless scoped |
|---|---|
| Scoping ambiguous problems with stakeholders | An ongoing feature factory with no outcome metric |
| Building against your data and tools | Greenfield company-building as a fractional CTO |
| Hardening: auth reality, guardrails, basic evals | A full enterprise programme management office |
| Runbook and handoff | 24/7 on-call, indefinitely |
| Feedback into reusable patterns or open tools | Unlimited revisions with no change control |
If you want ticket-only capacity, say so — that is a different product (FDE vs freelance).
How the tiers map to the loop
**AI Architecture Sprint — from $2,000.** Prove the risky path and cost the build. You get a thin vertical slice on live or faithful staging data, a written risk list covering SSO, corpus quality and security, a go or no-go, and a costed plan you own either way. Right when you are stuck in demo mode, or genuinely unsure whether an agent is the correct tool.
**Proof-of-Value Build — $8K–$20K.** One production-shaped path operators can run. You get the working path, a golden set and scorecard snapshot, a runbook, and a written list of known limitations. The gate is the eval ladder before handoff.
**Production Build — from $25K.** More workflows, deeper integrations, stronger evals, and support through security review. Phased, with fixed fees per phase rather than an open meter.
**Fractional AI Lead — from $5K/month.** Embedded leadership with **named monthly outcomes** — for example two workflows, an eval refresh, and office hours. A retainer without named outcomes turns into unpaid product management, so I do not sell one.
How scope becomes a number
I estimate from **risk surfaces**, not lines of code:
1. **Identity complexity** — SSO, roles, multi-tenant boundaries 2. **Data mess** — corpus quality, systems of record, refresh cadence 3. **Action surface** — read-only chat versus tools that write or spend 4. **Compliance shape** — audit, policy, retention answers 5. **Eval depth** — golden set size, shadow period 6. **Stakeholders** — one founder, or security plus legal plus ops
More red flags means more hardening time, which means the upper end of a band or an extra phase. This is the same argument as the 80% nobody demos: the model call is the cheap part.
**Payment hygiene I prefer:** a deposit to start, a milestone at the slice demo, and the balance on handoff artifacts — not "pay in full after endless polish."
What inflates price fast
No staging and no backup discipline on the CMS or data. "Must use our seven tools in week one." Write or spend actions with no approval workflow. Security review started after UI freeze. No operator owner for handoff. A golden set the client will not help build.
I would rather re-scope than underprice a fantasy timeline.
What I need to quote in one pass
The business outcome in one sentence. Your stack — auth, data, tools. Whether it is read-only or write and spend. The deadline, and why it is the deadline. Who operates it afterwards. Any compliance constraint. And a link to the current demo or docs if one exists.
Send that through contact. A vague "build us an agent" gets a sprint recommendation, not a fake fixed bid.
Do you work hourly?
Sometimes, for advisory overflow inside an active engagement. The default is scoped packages so incentives stay on handoff quality rather than hour padding.
Can we start small?
Yes — that is what the architecture sprint is for. Many good projects earn the right to a larger build after a thin slice on real data proves the risky path.
Do you replace our full-time team?
No. Forward deployed work embeds and leaves leverage. It works best with a counterpart on your side who will own the system after handoff.
What happens to a fixed fee when requirements change?
Change control. Small moves fit inside a buffer; larger moves reopen scope explicitly. Unlimited "one more thing" is how fixed fee dies, and pretending otherwise helps nobody.

I ship production AI for startups and teams — agents, RAG, automations — on a decade of design & Webflow craft.
About me →Keep going.
How I evaluate agent quality before handoff
A practical FDE playbook for evaluating AI agents before handoff — smoke tests, golden sets, retrieval checks, tool-call validation, guardrails, and a go-live scorecard that is not vibes on demo day.
The 80% of FDE work nobody demos
Most Forward Deployed Engineering is not the AI demo — it is SSO, legacy data, security review, and day-2 ops. The failure modes that kill production agents, and how to harden before handoff.
FDE vs Solutions Engineer vs Freelance Developer
Forward Deployed Engineer vs Solutions Engineer vs freelance developer — who owns production code, who embeds with operators, and which role you actually need for AI in a real customer stack.
