Enterprise RAG document intelligence
Knowledge search for companies with large document archives · Interactive prototype with sample data, solo
Updated
Before
Staff searched shared drives and opened PDFs one by one to find a single clause.
After
Plain-language answers, each citing its page, so staff open the source instead of searching drives.
- 1Person:Admin
Uploads documents into a collection.
Software:PipelineSplits them into chunks, embeds them and stores them in the index.
- 2Person:Employee
Asks a question in plain language.
- 3Software:Search
Finds passages by meaning and keywords, then ranks them.
- 4Software:Answer engine
Writes the answer only from those passages, with page citations.
Person:EmployeeOpens the cited page to check the source.
Always on
- An answer without a source is not shown.
- The accuracy dashboard tracks quality for each collection.
- Each collection is visible only to its own team.
Problem
Staff opened files one by one to find an answer. Keyword search missed the meaning, and a plain AI answer showed no source.
Diagnosis
Search has to match meaning and exact terms together. And an answer nobody can check is an answer nobody uses.
Options I weighed
How does the system find the right passages?
Chosen
Hybrid search, meaning plus keywords
Catches paraphrases and exact terms like clause numbers. Cost: two indexes to tune.
Rejected
Vector search only
Missed exact codes, names and clause numbers.
How do answers stay trustworthy?
Chosen
Answer only from retrieved pages, always cited
With no relevant page found, the system says so instead of guessing.
Rejected
Free-form AI answers
They sound sure even when they are wrong, and nobody can check them.
Solution
Collections split into chunks, answers written from retrieved passages with page citations, and an accuracy dashboard for quality over time. Built on sample documents.
- Interface
- Next.js
- React
- Tailwind CSS
- Radix UI
- Recharts
- Framer Motion
- Delivery
- Cloudflare Pages