GenAI Engineering

Because a demo is judged on one convincing answer, a production system on the thousand that follow. What separates the two is evaluation: a reference dataset, thresholds agreed in advance, and monitoring that catches drift before users do.

Retrieval-augmented generation grounds each answer in your own documents rather than in the model's memory. You need it as soon as answers must cite a source, follow an internal rule, or reflect content that changes faster than any training cycle.

You cannot remove hallucination, it is a property of probabilistic models. You contain it: ground answers in retrieved content, expose the sources, and classify use cases by how costly a wrong answer would be.

It does when it ships unreviewed: generated code carries close to three times more vulnerabilities than human code. The answer is not to ban it but to industrialise it, with DevSecOps in the chain and an engineer review before every integration.

A first usable deliverable lands in 4 to 10 weeks, after 2 to 6 weeks of framing depending on scope, sector and the level of compliance required. Framing produces an executable specification rather than a deck: that specification is what then acts as the contract between the business, the agents and the engineer review.

On a reference dataset scored before release, then continuously in production: answer relevance, grounding rate, latency and cost per request. Without a baseline measured beforehand, no improvement can be proven afterwards.

Yes. Sovereign or on-premise models handle confidential content, external providers handle the rest, and the routing rule is written at design time. Sovereignty is an architecture decision, not an option toggled at the end.