Forward Deployed AI Engineering
The flagship engagement. One senior engineer deploys into your team — your Slack, your repos, your messiest real data — and owns an agentic AI outcome from vague problem statement to system running in production. Palantir invented the model; OpenAI, Anthropic, and AWS now pour billions into it. We've shipped this way for years.
You've seen how the traditional version goes. The big firm sells you a discovery phase, a steering committee, and three layers of management that never touch a keyboard. Six months later you own a beautiful slide deck describing software that doesn't exist. The staff-aug marketplace sells you the opposite failure: a body in your standup working tickets under your management, owning nothing.
Forward deployment deletes both failure modes. The person who frames the problem is the person who writes the code is the person accountable for it running in production. Full context lives in one senior head that sits one seat away from your team — so decisions take minutes, not memos, and nothing is lost in handoff, because there is no handoff.
Week one — embedded, not onboarded
In your Slack, your repos, and your data on day one. Discovery happens inside the real workflow with the people who run it, not in a conference-room requirements workshop.
Weeks two to four — the assumption-killer ships
A working system against real data reaches real users fast, on purpose: bad assumptions die in week three instead of month six.
Weeks four onward — harden to production
Evals, guardrails, observability, security review, human gates where risk lives. The demo becomes a system your enterprise can certify.
Steady state — outcome owned
Live, measured, adopted — and every learning fed back into your roadmap. Accountability doesn't expire at the invoice.
Agentic systems live while comparable projects are still writing statements of work.
You pay for one senior outcome-owner, not an org chart.
Not pilots. Not proofs of concept. Running systems.
How is this different from hiring a contractor?
A contractor works your tickets under your management; you own the outcome and the overhead. A forward deployed engineer owns the outcome: framing the problem, shipping the system, staying accountable until it runs in production. If you're managing them, it isn't forward deployment.
What does the engagement cost?
No recruitment fees, no contract-lock games, no paying for people who never touch a keyboard. One embedded engineer engaged against a defined outcome — scoped on a short call. You get more shipped in a month than a bloated staffing contract delivers in a quarter.
What if our problem is vague?
Vague problems are the specialty. "We bought AI licenses and nothing happened" is where most engagements start. Discovery inside your real workflow finds the highest-value deployable problem — that framing work is part of the job, not a separate billable phase.
Will your engineer work with our existing team?
Embedded in it — that's the model. Your engineers keep full visibility, pair on the parts they'll own long-term, and inherit a system built in your conventions with your CI. We leave capability behind, not dependency.
Ready to skip the kickoff theater and ship?
Tell us about the AI initiative your last three vendors couldn’t close. We’ll scope the outcome on a short call.
Book a deployment →