Generative AI for Business

Generative AI for business is useful when a named job gets faster — draft a response, summarise a pack, retrieve a policy — and when the output is checked. We start from the job, the data the model may see, and the cost of a wrong answer. Identity and logging are in the first design. A public chatbot that hallucinates policy is not a launch; it is an incident waiting for legal.

We use hosted or private models depending on residency and the data classification you already have. Retrieval is built when the answer must come from your corpus, with source citations the user can open. Prompts and tools are versioned. Evaluation sets exist before you show it to a wider audience.

Change management is part of the work. The people whose job changes help define 'good'. We keep a human approval step on anything that leaves the building — customer email, published copy, code merged to main — until you explicitly accept the risk.

You get a production path: app or copilot, data connectors, monitoring of cost and quality, and a written use policy. Pilots that cannot pass evaluation are stopped. That is cheaper than a quiet failure in front of customers.