AI implementation: from a promising pilot to a system you can rely on
We take an AI use case through evaluation, hardening, security review and staged rollout, and leave you with a production system that has owners, runbooks and measured results.
A pilot proves an idea can work. Production proves it works every day, for every user, at a known cost, without leaking data. The gap is closed by three gates, each with a written pass condition agreed before we reach it.
The implementation pipeline. Each diamond is a gate with a pass condition written down in advance; a pilot that fails a gate goes back one stage, not into production.
Who this is for
Outcomes we are engaged to deliver
A pilot that needs to become real
Your team or a vendor built something that works in a demo. It needs evals, security, monitoring and an owner before anyone depends on it.
A scoped use case with no team
Strategy is done and the first project is funded. You need a senior team to deliver it and leave your people able to run it. Browse the solutions catalogue for common starting points.
A production system that is drifting
An AI feature went live without gates and is now expensive, inconsistent or risky. We retrofit the pipeline without taking it offline.
Pilot versus production
What actually changes between the two
Teams underestimate the gap because the model is the same in both. Everything around the model is different.
Concern
In a pilot
In production
Data
A hand-picked sample, often exported once
Live connections, access control, retention rules, PII handling, refresh schedule
Quality
"It looked right" on a few examples
Versioned eval set, scored on every change, with a release gate
Security
Shared API key, prompts trusted, tools unrestricted
Ranges, not promises: a single integration with clean data sits at the short end, a multi-system rollout in a regulated business at the long end.
Weeks 1–2
Assess and baseline
Eval set written with your experts, threat model drafted, cost model built, data access confirmed. Pass conditions for all three gates agreed in writing.
Weeks 3–5
Reach the eval gate
Pilot reworked onto the production architecture. Model choice, prompts and retrieval tuned against the eval set until the baseline is beaten with margin.
Weeks 6–8
Harden and pass security
Gateway, guardrails, retries, rate limits, secrets, logging. Prompt injection testing and a security review. Fixes until the gate passes.
Weeks 9–11
Staged rollout
Release behind a flag to one cohort, then widen. Dashboards watched against the error budget. Rollback rehearsed once before it is needed.
Week 12 onward
Operate and hand over
Runbooks, training, monthly review cadence. Production traffic is sampled into the eval set so quality is measured, not assumed.
What goes wrong without gates
Example: a document-extraction pilot is wired straight into an invoicing workflow because the demo looked good. Three months in, a change to the supplier's PDF layout drops accuracy, nobody notices for weeks, and finance reconciles by hand. Every part of that failure is caught by an eval set, a monitoring alert and a named owner. None of them are expensive compared with the clean-up.
How long does it take to get an AI pilot into production?
Typically eight to sixteen weeks from a working pilot, depending on how many systems it touches and how strict your security review is. The gates on this page are what take the time; skipping them is how pilots become fragile production systems by accident.
We already have a prototype from another vendor. Can you take it on?
Yes. We start with a two-week assessment: an eval set, a threat model and a cost model for what exists. Then we tell you what to keep, what to rebuild and what it will take to run. Sometimes the right answer is a rewrite; often it is not.
What does the eval gate actually check?
That the system beats the agreed baseline on a versioned test set covering the real distribution of inputs, including the awkward ones. The threshold is agreed with you before the gate, not after. See fine-tuning and evals.
Who owns the system after launch?
You do, with a named owner on your side from week one. We hand over code, runbooks, dashboards and eval sets, and can stay on through LLMOps monitoring or a retained team if you prefer.
What happens if the pilot fails a gate?
It goes back one stage with a written list of what to fix, and the timeline moves. A failed gate is a cheap outcome compared with a failed launch. We have stopped projects at the eval gate when the data could not support the use case, and said so plainly.
Ready to make AI work in production?
Tell us about your systems, your data and your constraints. We reply within one business day with a straight answer and a suggested next step.