Enterprise document question-answering system grounded in company policies
Build a chat system that retrieves relevant company documents, embeds them into a prompt, generates an answer with a large language model, cites the source material, and enforces the analyst's document permissions. The system improves over time by monitoring failures, generating synthetic question-and-answer data, and retraining the embedding model.
online B2B Service / Consulting / Agency
From IBM Technology — AI & Data Science Periodic Tables: How They Work Together at 04:49
Problem: Employees need reliable answers from scattered company documents, while avoiding hallucinated answers, outdated policies, uncited claims, and documents that exceed their permissions.
For: Large companies, particularly finance analysts and other employees who need answers from internal HR, generative AI, and operational policies.
Examples
- Finance analyst document Q&A: an analyst asks how the value of generative AI tokens relates to investment in digital and human workers; the intended outcome is a cited answer grounded in company documents and limited to material the analyst is permitted to see.
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