Generative AI Development

Your information is useful only if people can find the answer

A customer receives a detailed report but still needs someone to explain it. An employee searches several folders for a policy. A support team rewrites the same explanation for each enquiry. These are practical starting points for generative AI.

We can add a document assistant to a portal, turn a long report into a readable summary or prepare a draft response for someone to review. Ask Joe is our product example for questions about a user's report.

Connect the answer to the right information

Many of these applications use retrieval-augmented generation, usually called RAG. The application finds relevant material before asking the model to compose a response. That gives us a way to connect an answer to your documents and show where the information came from.

The surrounding application matters just as much. A customer must only retrieve their own records. Outdated documents need replacing. If the available information cannot answer a question, the interface needs a useful next step instead of presenting a guess as a fact.

Test the questions your users will actually ask

We build a test set from realistic questions, including ambiguous requests and questions the system should not answer. We review source accuracy, missing information, response time and the cost of each interaction.

Start with one document type or a defined knowledge collection. It makes the results easier to assess before extending the feature across the business. AI-generated answers still need appropriate checking, particularly when they influence a professional decision.