You’ve had AI pilots, but for one reason or another they never made it to production. You need someone who quickly finds the problem and writes the code to fix it, so your business ends up with a system that works. Our forward deployed engineers join your team for a fixed-price embed of 2–4 weeks and ship the first fix to production before it ends.
An FDE works inside your business and owns an outcome. Their job is to take a broad goal like “we want to use our data better” and turn it into something that runs in production.
Patient and clinical data sits in EHRs, labs and scheduling tools that rarely share a format. Your FDE starts with the workflow that costs clinicians the most time and works.
Trial, quality and supply data lives in validated systems where every change needs revalidation. We automate the reporting and document work around them.
Reconciliation, KYC checks and transaction reviews still eat analyst hours in most finance teams. Your FDE picks the queue with the most manual work and automates it with a full audit trail.
Meter, grid and asset data arrives late, messy and in huge volumes. The first job is often a pipeline that makes it usable for forecasting and AI.
Project data is split between estimating tools, site reports and spreadsheets. Once it’s connected, cost and schedule risks show up before they hit the budget.
Bookings, prices and customer data come from dozens of suppliers and channels. We start where manual handling slows sales or support, like rebooking or rate updates.
Student and billing data sits in separate systems, and reports are built by hand. We’ve solved this with one warehouse for all the data, with AI features on top.
Your FDE takes on the next problems from the list they built during the embed.
The task has grown into a full build. A wider team joins, and your FDE stays as the person who holds the context.
Code, runbooks and documentation go to your team. Support after launch is up to you.
A forward deployed engineer (FDE) is a senior software engineer who works directly inside a client’s business instead of from the vendor’s side. Unlike a regular developer, an FDE doesn’t wait for a ready specification. They work out with your team what needs to be built, write the code themselves, and stay until it runs in production. Palantir made the role known, and today AI companies like OpenAI and Anthropic use FDEs to get their technology working for customers.
A typical day splits between your people and the code. The engineer sits in on the process they’re fixing, for example how invoices get approved or how a report gets built. They check the data behind it, write and test the next piece of the solution, and push changes to production once they pass review. At the end of the day or week, they show you what moved, what’s blocked, and what comes next.
A forward deployed engineer takes full ownership of getting AI into production. That covers the whole path: finding the problems, turning them into specific tasks, writing and testing the code, fixing what breaks, and delivering the first results. In practice, it’s a person who joins your team, hears a concrete need from you, and comes back with something that works.
An augmented engineer works through tasks from your backlog. Someone on your side has to define them, prioritize them, and check the result. A forward deployed engineer does that work themselves: they find the problem, define the tasks, build the solution, and answer for whether it works in production. With staff augmentation you pay for capacity. With an FDE you pay for an agreed outcome.
A consultant analyzes your situation and hands you recommendations, and your team is left to build them. Our FDE writes the code and stays until it runs in production. A platform vendor’s FDE comes to roll out that vendor’s own product, so the solution is built around it. We don’t sell a product, so every choice of tool, model, or cloud depends on your data, systems, and budget.
The embed is a fixed-price engagement of 2–4 weeks, starting from $5,200. The retainer after it is billed monthly and depends on the number of engineers and the outcomes you agree on.
Before the work starts, we agree on two or three measurable outcomes tied to your business. For example: hours of manual work removed, time from request to production, error rate in a process, or cost per AI request. We record the baseline at the start, track progress at every check-in, and report the numbers when the first results go live.
Share where your data or AI work is stuck, and we’ll map the first steps in one call.
Tech label
AI
AI
