Smart assistant that answers from the user's documents

The problem

The information you need usually exists, but it's buried in hundreds of PDF pages nobody reads. Keyword search fails because the user doesn't know the term the document uses. General AI models answer from general knowledge and may invent details out of nowhere, which is worse than not answering. The solution is to build the answer from the document itself, with its source attached to every answer for verification.

How it works

1. UploadThe user uploads documents in any format, and the system extracts their text.
2. SegmentationThe text is split into coherent chunks, keeping the page numbers.
3. IndexingEach chunk is converted into a numeric representation that carries its meaning, then stored.
4. The questionThe question is converted into the same representation, and the closest chunks are found.
5. AnswerThe model writes the answer from the retrieved chunks only.
6. The sourceEvery answer comes with the page and paragraph numbers it was built on.

Project features

• Citing the source with every answer — what separates a trustworthy tool from one that makes things up.
• The system admits it didn't find the answer instead of inventing one, a behavior the defense committee values.
• It works on the user's own documents, not general knowledge.
• An impressive live demo: upload the university's regulations and ask questions in front of the committee.
• Marketable to any organization with manuals and regulations: companies, universities, and law firms.

Expected challenges

These are the main obstacles to expect during the build, laid out clearly so you know them before you start.

• Scanned Arabic documents need OCR, whose quality varies.
• Splitting the text in the wrong place breaks the meaning and loses the answer.
• A vague question retrieves unrelated chunks.
• Tables and images inside documents are hard to extract.
• Making sure the model doesn't invent anything outside the retrieved chunks.

The consultation gives you a solution for each one.

Expected committee questions

Prepare your answers in advance: these questions come up often in defenses of similar projects.

1. What is retrieval-augmented generation (RAG), and why is it preferred over training the model on the documents?
2. How are documents chunked, and how does chunk size affect answer quality?
3. How is the model prevented from inventing an answer that isn't in the documents?
4. How are scanned Arabic documents handled?

Practice the defense, question by question