Forward Deployed Engineer

n8n, Make.com and Zapier automation with AI steps and a human approval check — fixed price, documented on handover.

What Is a Forward Deployed Engineer?

A forward deployed engineer is a senior engineer who works inside your team instead of advising from outside it. They map your systems, shape the idea into one testable change, simulate it on your real cases, build it in your own stack, and stay accountable until it runs in production.

The role was created at Palantir and adopted by frontier AI labs for one reason: the hard part of an AI project is almost never the model, it is your systems, your data and the way your people actually work. We deploy that engineer from Dhaka into your repository, your tracker and your standups.

The first two weeks are a fixed-price review. One engineer reads your systems, data and workflows, sits with the people who do the work, and writes down what breaks and why.

You get a written build plan with the failure points named, the first change scoped and a fixed quote attached, whether or not you go further.

Book an ecosystem review Send one broken workflow
2 weeks 01 Ecosystem and idea review $1,500 fixed
2-3 weeks 02 Proof build from $800, one agent
Per agent 03 Production build from $1,500
Monthly 04 Embedded engineer $4,200, 3-month minimum

What does a forward deployed engineer deliver?

Every forward deployed engineer engagement delivers six things: an ecosystem map, a scoped idea you can still say no to, simulation results on your own cases, working code in your repository, a fixed failure log, and a documented handover. You receive a running system, not a slide deck.

Ecosystem and workflow analysis

Your systems, data, queues, owners and handoffs mapped, with the points that actually break named and measured first.

Idea shaping and solution design

Your idea turned into one testable change, with the success test, the cost and the decision you will make written down.

Simulation and evaluation

The change replayed against fifty to a hundred of your past cases and scored by model testing before any live request reaches it.

Build and integration

Agents, APIs and pipelines built in your own repository and cloud, reviewed by your engineers as ordinary pull requests.

Debugging and hardening

Failure modes reproduced, logged and fixed, each one covered by a test and an AI code audit so it cannot return quietly.

Handover and enablement

A runbook, an evaluation suite and working sessions with your team, so the system survives after the engineer leaves.

Not included: a fixed production price before the review is done; hiring, payroll or employer-of-record for the engineer; model, cloud and licence costs, which stay on your own accounts; any certification we do not hold.

Which problems does a forward deployed engineer solve?

A forward deployed engineer is worth hiring when the blocker sits in your own systems rather than in the model. Four situations reach us most often: a stalled AI pilot, an integration nobody owns, a workflow that breaks at the edges, and data that is not ready for AI.

A pilot that will not reach production

The demo worked and production did not. The engineer finds what the demo skipped and closes it, or tells you to stop.

An integration nobody owns

Two systems that must talk, a brittle script between them, and nobody accountable when the queue backs up overnight.

A workflow that breaks at the edges

The happy path works, the exceptions do not, and your people clear them by hand with no review step on record.

Data that is not AI-ready

Gartner found 63% of organisations lack AI-ready data practices. The review tells you that before you build on top of it.

How does a forward deployed engineer go from idea to production?

A forward deployed engineer runs one loop: map the ecosystem, shape the idea with your team, simulate it, build it in your stack, then debug and harden it. A person signs off at the shaping gate and at every release. The sample below shows one idea moving through that loop.

One idea, from ecosystem map to production — with a person signing off at each gate
Map the ecosystemSystems, data, queues, owners and the handoffs that actually break
Shape the ideaScoped with your team into one testable change, written down
Simulate and evaluateReplayed on your real cases before it touches live traffic
Build in your stackYour repo, your pipeline, your cloud — no black box of ours
Debug and hardenFailure modes found, logged, fixed and covered by a test
Merged pull requestsReviewed by your engineers
Evaluation harnessRunning in your pipeline
Runbook and handoverYour team can run it alone

Yellow marks where a person decides. Every change ships as a pull request in your own repository with the evaluation run attached, so nothing reaches production without a named reviewer.

Illustrative example. Yellow marks where a person decides or signs. Nothing reaches production without a named reviewer, and the evaluation run stays attached to the change.

How does a forward deployed engineer engagement work?

Our forward deployed engineer engagements run in four steps, each at a published price you can stop on. A two-week ecosystem and idea review, a proof build simulated on your own cases, a production build in your repository, and an optional embedded engineer by the month.

01 2 weeks
Ecosystem and idea review $1,500 fixed. Systems, data and workflows mapped, the people doing the work interviewed, failure points named, and the first change scoped with a fixed quote.
02 2-3 weeks
Proof build From $800 for one agent. Built and replayed on fifty to a hundred of your real cases, ending in a go or no-go report with the measured result.
03 Per agent
Production build From $1,500. The change built in your repository and pipeline, with an evaluation harness, logging, failure handling and a runbook your team can follow.
04 Monthly – optional
Embedded engineer $4,200 a month, $4,800 senior, three-month minimum. One engineer in your standups and your team, working the backlog you set.

What does a first engagement cost?

Published prices, on the cards above and on our price page. Live in about four weeks.

 

Code Your repository, your branches, your review

 

Runtime Your own cloud and model accounts

 

Access Least-privilege accounts you create

 

Reporting Your tracker, a shared channel, weekly notes

Who hires a forward deployed engineer?

Forward deployed engineers suit teams that already have the problem and the data but not the engineer who can cross both. Three buyers hire us most. In each case the engineer reports to a named owner on your side and ships through your own review process.

Companies with a stalled AI project

Budget approved, pilot built, production still out of reach. You get a measured verdict in weeks instead of another roadmap.

Product and platform teams

You need agents and integrations shipped inside an existing codebase, reviewed by your engineers, without hiring for a year.

Software vendors deploying to customers

Your product needs fitting to each customer’s systems. You get an engineer who does that work inside their stack.

Why hire a forward deployed engineer instead of a consultant?

A forward deployed engineer writes the production code and carries the failure; a consultant writes the recommendation and leaves. Gartner forecasts that over 40% of agentic AI projects will be cancelled by the end of 2027, most of them for reasons a document cannot fix. We work code-first, inside your repository.

Without an embedded engineer

!!!!!
  • A slide deck that nobody on the team can deploy
  • A demo that never survives real traffic
  • Hourly billing that grows with the confusion
  • No one accountable when it breaks at 2am

With EICRA

Proof build report – one agent
Cases replayed50 to 100Scope1 workflowEvaluationAttachedCodeYour repositoryVerdictGo or no-go
Illustrative example
  • Working code in your own repository from week three
  • Every claim measured on your own past cases
  • Published prices with a stop point at each step
  • One named engineer accountable to your named owner

Is it safe to give an outside engineer access to your systems?

Giving an outside engineer access is safe when the limits are written first. As a Bangladesh-based company, we sign a mutual non-disclosure agreement (NDA) before reading a line of your code, work only through named least-privilege accounts you create, and leave your code and data inside your own systems.

Reviewed by Eicra Soft Limited, the contracting entity for this service — RJSC registration E67073(4565)/07, BASIS member GE-09-03-330, trading since 2007. The security terms described here are the ones written into our standard engagement agreement; the terms that bind us to you are the ones in the agreement you sign.

Which agreements are signed, and when?

NDA – mutual, signed before we read a line of your code.
DPA – the processor terms required by Article 28(3) of the General Data Protection Regulation (GDPR), for any personal data in scope.
International data transfers – standard contractual clauses or the transfer instrument your jurisdiction requires, signed before personal data moves.
Intellectual property (IP) – all work product assigned to your company, from the first commit.
Certifications – listed only when held; none are claimed on this page or in any proposal.

What access and accountability terms apply?

Access Named least-privilege accounts that you create, in your systems, removed at handover.
Code Your repository and your branches; every change arrives as a pull request your engineers review.
Secrets Your own vault and your own keys; no production credential is ever held outside them.
Accountability One named engineer, one named owner on your side, and written weekly notes with an incident record.
Rework Free when a change misses the agreed test; new scope is priced first as a change request.
Request our NDA and DPA templates

What proof do you get before you pay for a production build?

Before the production budget is committed you get evidence instead of promises. The review ends in a written build plan, the proof build ends in a go or no-go report measured on your own cases, and the first conversation costs nothing. Client case studies with numbers are added here as clients give permission to name them.

50 to 100 cases

Every proof build is replayed against fifty to a hundred of your own past cases before a verdict is given.

2 weeks

The ecosystem and idea review is fixed-price and time-boxed to two weeks, and ends in a written build plan.

1 named engineer

One named engineer owns the work and the failures, and reports to a named owner on your side.

Case studies: client results with numbers are added here as clients give permission to name them. Ask on the call for examples in your own stack.
Free 30-minute workflow review Bring one workflow that keeps breaking. You get a written note on where it fails and what fixing it would cost, whether or not you hire us.

Send one broken workflow

Common questions about forward deployed engineers

How much does a forward deployed engineer cost?

A forward deployed engineer is priced per step, not per hour. The ecosystem and idea review is $1,500 fixed for two weeks, a proof build starts at $800 for one agent, a production build starts at $1,500 per agent, and an embedded engineer is $4,200 a month, or $4,800 for a senior engineer, on a three-month minimum. Every figure is published on our price page.

What is a forward deployed engineer?

A forward deployed engineer is a senior engineer who works inside your team instead of advising from outside it. The role was created at Palantir, where the title is forward deployed software engineer, and was adopted by frontier AI labs for the same reason: the hard part of an AI project is not the model, it is the customer’s own systems, data and workflow. The engineer maps that ecosystem, shapes the idea into a testable change, simulates it, builds it in your stack, debugs it and hands it over.

How is a forward deployed engineer different from a consultant?

A consultant delivers a document and leaves. A forward deployed engineer writes the production code, owns the pull request, carries the failure when it breaks and stays until your team can run it without us. You are not buying a recommendation, you are buying a working change in your own repository with a named engineer behind it.

Gartner says most enterprises will abandon vendor-built forward deployed engineering. Why would this be different?

Gartner predicts that by 2028, 70% of enterprises will abandon agentic AI built through vendor forward-deployed engineering, trapped by soaring costs and unable to evolve it on their own. The named causes are weak scope, premium fees for ordinary work, no knowledge transfer, and no exit terms. Each of those is answered in the contract here. Scope is one testable change, written down and signed off before any build. Price is published per step, not quoted per hour. The code is yours from the first commit, in your repository and your cloud. Handover and a runbook are a step of the work, not an afterthought, and you can stop at the end of any step. If your team cannot run it alone at the end, the engagement has failed its own test.

Do your engineers work on-site or remotely?

Remotely by default, from Dhaka, Bangladesh, working in your repository, your tracker and your standups with a daily overlap window agreed in the contract. On-site work is possible for a discovery week or a go-live, priced separately, with travel and visa lead time quoted before it is booked.

What happens in the first 30 days?

Weeks one and two are the ecosystem and idea review: the engineer reads your systems, data and workflow, sits with the people who do the work, and writes down what breaks and why. By the end of week two you have a build plan and a fixed quote. Weeks three and four are the proof build, simulated on your real cases, ending in a go or no-go report.

Who owns the code and the intellectual property?

Your company does, from the first commit. The code lives in your repository, the agents and pipelines run in your own cloud accounts, and the prompts, evaluation sets, runbooks and documentation are yours. At handover our access is removed and we keep no copies.

What if the idea turns out not to work?

You stop, and you keep the evidence. The proof build is designed to fail cheaply: it ends in a go or no-go report with the measured result on your own cases. Gartner forecasts that over 40% of agentic AI projects will be cancelled by the end of 2027, and a fixed-price proof step is how you find out before the production budget is committed, not after.

What do we need ready before an engineer starts?

Three things: a named owner on your side who can make decisions, read access to the systems in scope through least-privilege accounts you create, and a real sample of the work to be automated, such as 50 to 100 past cases. A signed non-disclosure agreement comes before any of it. If your data is not ready, the review says so in writing rather than building on top of it.

How long does it take to get to production?

About two to three months from the first call: two weeks for the ecosystem and idea review, two to three weeks for the proof build on your real cases, then the production build per agent. You can stop at the end of any step, and each step has a published price.

Start with a two-week ecosystem and idea review at a fixed price.Prefer to talk first? Call +880 1917 746550 or email support@eicra.com.
Book an ecosystem review Send one broken workflow