AI that survives a technical evaluation.
Building a demo is easy now. Building something with an audit trail, a cost ceiling, an approval workflow and a human in the loop is where most AI projects stall. We have shipped that — the AI Catalogue Engine is our own, running in this platform.
The parts of an AI project that decide whether it ships.
The model is rarely the hard part. Everything around it is what determines whether the system reaches production and stays there.
Use-case selection
Working out which problems AI is genuinely better at, and saying so when the answer is a rule, a query or a better form instead.
Feasibility and cost modelling
What it will cost per operation at your real volume, established before a build commitment rather than discovered on the first invoice.
Evaluation harnesses
A way to measure whether output is actually good, because without one you are shipping on impressions and cannot tell when quality drifts.
Human-in-the-loop design
Where a person reviews, what they see, and what they can change — the part that decides whether the system is trusted internally.
Audit and governance
Model, version, prompt version, latency and cost logged per call, so a challenged output can be explained rather than defended.
Cost ceilings and quotas
Budgets and quotas checked before expensive calls, because unbounded AI spend is the fastest way to lose executive support.
How we approach an AI engagement.
The first question is whether AI is the right tool. Plenty of problems presented as AI problems are better solved another way, and we will say so.
- Problem examined before the solution
- Non-AI alternatives considered honestly
- Value estimated before build commitment
- Data availability assessed realistically
A narrow, measurable pilot with an evaluation harness attached, so quality is a number rather than an impression from a demo.
- Narrow scope with clear success criteria
- Evaluation harness built alongside
- Cost per operation measured
- Failure modes documented
The governance layer is what separates a pilot from a system. It is designed in from the start rather than retrofitted under audit pressure.
- Audit trail per model call
- Approval workflow with separation of duties
- Budget and quota enforcement
- Provider choice kept in your hands
The things buyers actually ask
Yes. The AI Catalogue Engine in this platform is ours — with approval workflow, per-call audit logging, cost ceilings and database-enforced tenant isolation. It is the reference we would show you.
Whichever suits the capability and your constraints. We design for provider choice per capability rather than locking you to one vendor, because that decision changes and should stay yours.
Yes. A rules engine, a better query or a redesigned form solves a surprising share of problems presented as AI problems, at a fraction of the running cost.
What this connects to
Every module runs standalone and every module talks to the kernel. These are the ones most often deployed alongside it.
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A working walkthrough with your catalogue, your order flow and your questions. No slideware.