AI Deployment

Forward Deployed Engineering: Place the Intelligence. Prove It. Hand It Over.

Every organisation can now buy intelligence. Very few can place it. We embed one engineer inside your business for ninety days to decide where intelligence belongs — and to prove it in production.

The Premise

Intelligence stopped being the advantage

Frontier capability is now a purchase decision, not an engineering achievement. When the same models are available to you, your competitor and your supplier on the same commercial terms, the model cannot be what separates you. The advantage moved downstream — into which decisions intelligence touches, under what limits, with what evidence.

Everyone can buy it

A capable model is a subscription and an API key. The barrier that used to take a research division is now a procurement line.

So it cannot be the moat

Anything universally available is table stakes. Advantage never survives contact with universal access.

The advantage moved downstream

It sits in placement: which decisions intelligence touches, under what limits, with what evidence.

Someone inside your business has to decide where intelligence belongs — and, just as importantly, where it does not. That person is the forward deployed engineer. The role only works when one person carries the commercial, technical and governance judgment together, and can be held to account for the trade-offs between them.

The Role

Organisations do not run on their process maps

The documented procedure describes the intended path. The real path includes the spreadsheet nobody owns, the approval that happens in a chat thread, and the exception the same three people have always handled by memory. Automating the documented version produces a system that fails on contact with Monday.

What the engineer is there to do

Trace the work as it is performed — not as it is written down. Every handoff, every re-keying, every wait state, every exception path and who absorbs it.
Draw the line — decide which steps should be handled by software, which by a model, which by a person, and which should simply stop being done.
Build the connection — working software over the systems you already run. No migration, no replacement programme, no new platform to adopt first.
Produce the evidence — measured behaviour against real cases, with failures named and kept, so trust is earned from data rather than demonstration.
Hand it over — transfer the system and the reasoning behind it to your team, and leave.

The four honest outcomes

For every step in an audited workflow, only four answers are defensible. Naming the fourth is what keeps a deployment credible.

Deterministic software — when inputs and rules are predictable. Cheaper, faster and more reliable than a model. Reach for this first.
An agent — when the objective is clear but the inputs, the route or the actions vary case to case.
A person, retained — when the decision carries real ambiguity, personal consequence, or an outcome that cannot be reversed.
Nothing at all — when the step exists only because an earlier system made it necessary. Removing work beats automating it.

The Engagement

One engineer. Ninety days. Three gates.

Each phase has to earn the next. If the evidence at a gate doesn't support continuing, we say so and you stop — having spent a third of the budget rather than all of it.

Days · Observe

The real workflow, mapped — not the documented one

Days · Prove

Evidence before autonomy, graded against real cases

Days · Embed

Live in production, then handed over to your team

Gate 1 · Day 30

Observe — establish what is actually true

Workflow tracing, exception mining and use-case scoring against value, feasibility and risk. Output: one selected workflow, a measured baseline, and a written statement of what the system will never be permitted to do.

Gate 2 · Day 60

Prove — turn uncertainty into evidence

The use case is built to production standards, then deliberately tested to failure against a graded set of real cases. Output: a working system with a published evaluation report and a defensible recommendation.

Gate 3 · Day 90

Embed — production, then handover

The system goes live under full human review, with autonomy widened only as observed behaviour justifies it. Output: live in production, monitored, with a complete evidence pack.

What You Receive

Deliverables written to be used after we leave

Nothing in this list depends on gnaan.ai remaining engaged to retain its value. Everything we build is committed to infrastructure you control, from the first week.

Day 30 · Operating Map

Current-state and future-state workflow side by side, with every handoff, exception and system dependency named — plus a use-case assessment and a boundary statement for what the system may never do.

Day 60 · Evaluation Report

A reference implementation held in your repository, a graded case set you own, and a published evaluation report showing pass behaviour, named failure categories, and unit economics.

Day 90 · Evidence Pack

Production deployment integrated with your systems, an operations runbook, a governance evidence pack, and a transfer certificate confirming your team can operate and extend the system.

The gnaan.ai Difference

Governed on the way in, not audited on the way out

Most AI deployments are governed retrospectively: the system is built, then someone is asked to document it for an audit. We generate the record as a by-product of building, because reconstructing intent months later is the single most expensive thing an assurance programme ever does.

The evidence pack, assembled continuously

AI system inventory entry
Risk classification & rationale
Data lineage & permitted use
Human oversight design
Evaluation results & failure log
Change and release history
Incident and escalation procedure

ISO/IEC 42001

Deliverables are structured to slot into an AI management system — system inventory, impact assessment, objectives, operational controls and performance evaluation — so a later certification effort inherits real records instead of starting from a blank template.

EU AI Act

Classification is done at Gate 1, before anything is built. Transparency, human oversight, logging and technical documentation are treated as design inputs, considerably cheaper than treating them as remediation.

Working Together

Three ways to start

Entry Point A

Full 90-day deployment

The complete arc: observe, prove, embed and transfer. Appropriate where a workflow is already suspected and a business owner is in place.

Entry Point B

30-day observation only

Phase one as a standalone commitment. Receive the operating map, the use-case assessment and a costed recommendation, then decide independently whether to continue.

Entry Point C

Assurance on work in flight

Where a build is already underway, the same evaluation and governance discipline applied to an existing system before it is widened or signed off.