An AI engineer inside your business.
We embed with your team, map how work really happens, and deploy production AI agents inside the tools you already use. Measured against time, cost, risk, and revenue. Not chatbots. Not AI theatre.
Short answerWhat is a forward deployed AI engineer?
A forward deployed AI engineer is an engineer who embeds inside a client's business and ships production AI systems from within their team, tools, and workflows, rather than consulting from the outside. Palantir pioneered the model, and it is now how OpenAI and Anthropic deploy AI into large enterprises. Engine AI applies the same discipline to New Zealand operators with roughly ten to 150 staff. Every engagement starts with a paid AI Readiness Audit: one to two weeks mapping how the work really happens, including the exception paths and the knowledge that lives in one person's head. We then ship one production slice in one to three weeks, on the systems you already run, with evals, traces, and human approval gates built in. Results are measured against three numbers: time saved, risk reduced, and revenue unlocked. Once live, agents keep improving under Agent-as-a-Service.
The bottleneck is not the model.
Every business can now buy the same AI models. What you cannot buy off the shelf is deployment judgment: which workflows are worth rebuilding, where AI belongs and where it does not, how the exceptions really work, and how to make agents reliable enough to trust in production on the systems people already live in.
That is why most AI pilots fail. Tools sprawl. Things work once, then don't. The knowledge stays in one person's head. There are no evals, no traces, and no owner, so token spend climbs while nothing on the business moves.
Palantir built a business on closing that gap. OpenAI and Anthropic now hire forward deployed engineers as the standard way to get AI working inside enterprises. The bottleneck is the bridge between the business and the model. Engine AI is that bridge.
The engagement
Map. Ship. Prove. Expand.
Every engagement starts with a paid AI Readiness Audit: one to two weeks, desk-side where it helps and deep remote otherwise, mapping one to three priority workflows, the exception paths, and the knowledge that lives in one person's head. It ends with an operating map, a priority matrix, and a fixed-price proposal for the first build.
Then we ship one production slice in one to three weeks as a Sprint Build Card: a working agent inside the tools you already run, no rip-and-replace. It runs in shadow mode first, passes a golden eval set, and goes live with human approval on the steps that matter.
Prove it, then expand. Live agents can run under Agent-as-a-Service: monitored, iterated, and measured monthly against time, risk, and revenue, with direct access to the people who built them.
The trust package
What ships with every slice.
Clients do not buy prompts. They buy trust under load. Every production slice ships with the evidence to earn it.
Operating map
How the work really happens, exceptions included. Proof we understood the job before we built anything.
Golden evals
A pass/fail test set the agent must clear before go-live. “41 of 50 passed” language, not vibes.
Traces
Every material agent action is logged and inspectable. You can always see what it did and why.
Human gates
Irreversible, regulated, or judgment calls stay with your people. The agent proposes; a human approves.
Runbook and rollback
How it fails, who escalates, how to pause or reverse it. Designed for exceptions, not just the happy path.
Scoreboard
Hours returned, errors down, dollars unlocked. If we cannot measure it, we do not build it.
Not a contractor. Not a consultancy.
Staff augmentation
Staff augmentation gives you a person and keeps the responsibility with you: you direct the work, you own the outcome, and when they leave, the knowledge leaves with them.
Consultancy
Traditional consulting gives you recommendations and keeps the distance: the strategy lands, the consultants leave, and your team is left to build it with a document.
Forward deployed
A forward deployed engineer is the third option. Embedded like a hire, accountable like a firm. The work happens inside your business, the outcomes are owned by ours, and what gets built stays working after we step back.
The honest small print.
The audit is paid, and that is deliberate: free discovery gets you a sales pitch, a paid audit gets you real access and a map you can act on. Senior people do the work; nothing is delegated to a bench, and capacity is limited to a small number of concurrent engagements because the founders do the deploying.
The hard rules: scope change is a new card. Demo or it is not done. Shadow mode before autonomy on anything high-risk. Built on your existing systems first. If the fit is wrong, we will say so during discovery and point you at a cheaper first step, like the free Brain Score.
Where this is not the right fit.
- Pure “teach us AI” training engagements with no build (we deliver working systems)
- Staff-augmentation requests: a body in a seat, directed task by task
- Businesses that want to rip out their ERP or CRM as the first move
- Teams with no operator willing to open up the real workflows
Want an engineer on the inside?
We'll pick the workflow, scope the audit, and give you a flat number before anything starts.