Document processing and RAG
Make document answers traceable to their sources.
Plan a document workflow around the evidence your team needs to review, not just the fluency of an AI answer.
Extraction and search solve different problems
Extraction converts a document into fields such as an order number, product or quantity. Retrieval-augmented generation (RAG) finds relevant document passages before generating an answer. Choose extraction for structured processing and retrieval for questions whose answers need supporting context.
An example you can try today
The student experience accepts study material and questions about it. The order demo demonstrates a separate structured extraction and validation workflow. These are examples of the underlying patterns, not a claim that an enterprise knowledge system is already deployed for customers.
What a business pilot needs
Agree supported file types, scanned-document/OCR requirements, document volume, access rules and source references. Evaluate a small set of real questions using non-confidential samples first. A useful result must distinguish missing evidence from a supported answer.
How to evaluate quality
Check whether retrieval found the relevant passage, whether the answer is supported, and whether each page reference leads to the right source. Include contradictory documents, missing answers and malicious instructions embedded in files. RAG does not eliminate hallucinations; high-impact conclusions still need human review.
Implementation choices
Python and FastAPI suit parsing and API orchestration; PostgreSQL/Supabase can hold application state. Vector retrieval is a design choice for a scoped workload, not a requirement for every document. Tenant authorization, bounded model usage, retries and deletion behaviour belong in the design before scaling.
Start with a scoped pilot.
Discuss your inputs, acceptance criteria and integration access before agreeing on delivery and price.
Discuss your project →