Not a consultant who writes slides, an engineer who sits inside your department and ships the agent.
Most enterprise AI stalls in pilots because generic tools don't know the business. The forward-deployed model flips it: you put builders inside the department that owns the problem, so the software is shaped by how the work actually happens.
Inside the department
Our FDEs sit with the people doing the work, follow a task end to end and map where time is lost, where decisions stall and where data is scattered. The output isn't a report, it's a shortlist of jobs an agent can own, ranked by leverage and feasibility.
Build, don't present
Then we build a vertical agent that does one job, wired into the tools and data you already use, and ship it in weeks. A narrow agent in production beats a broad roadmap on a slide, every time.
What an FDE does that a consultant doesn't
The distinction isn't seniority or technical skill: it's responsibility. A consultant hands over a recommendation and the execution risk stays with the client. A forward-deployed engineer hands over something running in production, so the risk stays with them.
- They sit where the work happens, not in a meeting room: what matters becomes visible by watching someone work, not by asking them to describe how they work.
- They write the code that ships, so they pay the cost of their own simplifications instead of passing it to someone else.
- They stay after go-live, when the edge cases surface that no amount of upfront analysis had predicted.
Exceptions aren't noise, they are the process
Ask a department how a task works and you get the clean path. Watch it, and you find that a substantial share of the volume takes a detour: the counterparty handled differently, the field filled in by hand, the confirmation that happens over a phone call. An agent built only for the clean path stops exactly there.
Why shipping early beats shipping complete
A narrow agent in production after a few weeks teaches you more in one week than six months of analysis: where the data is dirtier than expected, where people trust it and where they don't, which approvals were genuinely required and which were habit. None of that comes out of a requirements document.
There's a less technical reason too. A department's trust isn't won by explaining what the system will do, but by letting them watch a task that used to take an afternoon close itself, with the source in plain view and the option to step in. From there, widening the scope is an easy conversation.
It's the model Palantir made famous, applied to AI agents: don't sell a platform and wish them luck — put the people who build next to the people who do the work, until the work actually changes.