Enterprise AI adoption 2025
Using company knowledge to answer tenders
An AI application that brings company knowledge closer to the people who need to turn it into a reliable response.
The situation
Tender and bid responses require teams to find, compare and reuse information distributed across a large body of enterprise knowledge. The challenge was not simply generating text: it was making the source material easier to navigate while retaining human review.
My role
I designed and built the application and its retrieval architecture, connecting the language model to controlled company sources while keeping review and final ownership with the people responsible for the submission.
The decision
The solution paired a language model with a controlled search of company sources. Relevant material is retrieved before the answer is generated — an approach known as retrieval-augmented generation (RAG).
The role of the system
The application supports the response process by reducing the distance between a question and the material needed to answer it. Review and ownership stay with the people responsible for the final submission.
The outcome
The system materially reduced the time needed to prepare tender and bid responses. Quantified internal results are deliberately not published here until cleared for external use.