The challenge
Generative AI is the most accessible technology we have ever worked with — and the easiest to fail with. A prototype against a public model takes an afternoon. A solution that answers reliably from your data, respects permissions, stays within cost boundaries and is actually used — that is engineering.
The mistake most make is skipping evaluation. Without metrics you don't know when the solution is good, when it regressed after a model swap, or whether it was ever better than the current routine.
Our approach
We build three things: a solution, an evaluation harness and a decision basis.
- Scoping. We pick a use case with a clear value chain — often knowledge search over internal documents, draft generation or support assistance — and define what "good" means.
- Architecture and prototype. RAG, copilots or direct model integration. We work with your existing systems and permission models, not in parallel to them.
- Evaluation and production path. Metrics, test cases and recurring evaluations. The pilot ends with a business conclusion: scale, adjust or stop.
A typical pilot runs 6–8 weeks on your own data. We hand over code, knowledge and tooling so your team can run it onwards — we are consultants, not a dependency to rent forever.
Frequently asked questions
Do you send our data to external AI services?
Only when you want that and it is legally defensible. We help you choose model and hosting based on your risk profile — from external APIs with zero-retention agreements to open models in your own environment. Data sensitivity, compliance and cost drive the choice, not fashion.
How do you measure whether a GenAI solution is good?
Through evaluations built on your real questions and answers. We build an evaluation harness early: representative test cases, metrics for correctness and groundedness, and recurring runs. 'Feels good' is not a metric.
What happens after the pilot?
You know whether the value exists. If it does, we help you productionise: operations, governance, training. If it doesn't, you've spent bounded time on an honest answer instead of an endless proof of concept.