Service
AI implementation services
Implementation is the part where a decision becomes a system that people use on a Monday. We design the workflow rather than bolting a model onto the existing one, build to a defined quality target, connect it to the systems it has to live beside, and instrument it so the effect can be measured rather than asserted.
The business problem
The most expensive place to be with AI is a successful pilot. The demonstration worked, so the assumption forms that what remains is deployment. Six months later it is still a pilot.
The gap is rarely technical. A pilot is a controlled experiment with chosen inputs, forgiving users and no obligation to be available at nine on a Monday. Production is an operational commitment with a name attached. Most of the distance between them is work the pilot was designed not to do.
The other half of the problem is process. A model that drafts a document, inside a workflow that still requires the same three approvals, has moved effort rather than removed it. We set this out in how to move an AI pilot into production.
When this is useful
Where implementation work is the right next purchase.
A pilot that will not promote
It works in a demonstration and nobody can say precisely what production requires.
A decision already made
The workflow and the target are agreed, and you need people who have shipped this class of system.
No capacity to staff it
The plan exists but the engineers are committed elsewhere.
A system that needs redesigning
Something live that automated a step without changing the process around it.
Adoption stalled
The system works and people are not using it, which is usually a workflow problem rather than a training one.
Cost surprises at volume
Consumption costs that were fine in the pilot and are not fine now.
What we will do
Seven pieces of work, in roughly this order.
- Move from strategy to production. Convert the recommendation into a specification: the workflow, the quality target, the escalation path and the measurement method.
- Design the real workflow. Including the approvals, handoffs and exceptions that actually exist, because those are what produce the cycle time.
- Build the production system. Smallest useful version first, evaluation set built before optimisation, model treated as a replaceable component.
- Connect the existing systems. The records, repositories and platforms the workflow already depends on. Detail on the integration page.
- Test reliability. Held-out real cases including the unhappy path, load and cost at real volume, and behaviour when the system is unsure.
- Measure the outcome. Against the baseline agreed before the build, on the same dashboard as availability and cost per decision.
- Support adoption. Documentation written for the people doing the work, a named owner, and a review at the point where enthusiasm normally fades.
What you will receive
All of it in your repository, under your licence, from the first commit.
- A working production system with a defined service level for latency, availability and quality.
- An evaluation set of real cases with agreed correct answers, including the cases that should be refused.
- Monitoring on outputs as well as uptime: answer quality, refusal rate and cost per decision.
- An escalation path for the cases the system should not decide alone.
- A measured before-and-after against the agreed baseline.
- Handover evidence: your engineer changing the system unaided, plus the documentation that made it possible.
Expected business outcomes
Which of these applies depends on the workflow, and it should be agreed before the build rather than claimed after.
Cycle time reduced
Elapsed time from request to resolution, where speed changes an outcome rather than just feeling better.
Capacity absorbed
More volume through the same team, where the volume exists.
Error rate reduced
Fewer wrong decisions in a process where a wrong one has a known cost.
Predictable unit cost
A known cost per decision at real volume, and at three times it.
A system people use
Adoption because the workflow changed, not because usage was mandated.
Operable without us
Your team changing the system safely, with the evaluation set to prove the change was safe.
How the engagement runs
Sequential, with the measurement fixed before anything is optimised.
Frequently asked questions
Do you work with our engineers or instead of them?
With them, by preference. Handover is part of the engagement, and it works better when your people have been in the code from the start.
What if we already have a pilot?
That is the common starting point. The work is then establishing what promotion actually requires, which is usually process and measurement rather than model quality.
How long does an implementation take?
It depends on the workflow and the state of the data. The diagnostic is what makes that estimate honest, which is why we do not quote a build before it.
Which stack do you use?
Yours where possible. We keep the model a replaceable component and prefer open protocols at the seams, so the choice can change later without a rebuild.
Who owns the code?
You do, including prompts, evaluation sets and configuration. It lives in your repository from the first commit.
Do you stay on afterwards?
Only if you want us to. The engagement is designed to end with your team changing the system unaided.
Relevant insights
Start a conversation.
If something works in a demonstration and has not reached production, that gap is most of what we do. Tell us the workflow and the number.