RAG-Powered Document Intelligence
Cited, source-referenced answers from user-uploaded documents via a tuned retrieval pipeline.
The Challenge
Users upload documents into knowledge bases; AI generates cited, source-referenced answers. Real challenge was the retrieval pipeline itself — chunking strategy for vector embeddings, preventing background job timeouts on large document batches, and keeping citations traceable to exact source pages instead of vague summaries.
Our Approach
We tuned chunk size and overlap around the real document types users uploaded, attached source and page metadata to every chunk in pgvector, and moved ingestion to a background job queue so large batches don’t hit request timeouts.
The Impact
Every answer now traces back to an exact page in an exact source document instead of a vague "according to the document" summary — the difference between an AI feature people trust and one they double-check manually.
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