AI document-processing pipeline
Invoices, forms and contracts read automatically into structured data, with people reviewing only the uncertain ones.
- Python
- LLM APIs
- FastAPI
- PostgreSQL
Example project · New build
Support assistant over a company knowledge base
An assistant that answers from your own manuals and tickets, cites its sources, and says when it doesn't know.
This is a representative example of a project type we take on — a typical situation, approach and scope — not a description of a specific client engagement. Timelines are indicative; yours will depend on the details.
The situation
Answers exist — in manuals, past tickets and a wiki — but finding them takes longer than asking a colleague. A generic chatbot isn't acceptable because it confidently invents answers.
How we approach it
- Index the real sources and keep them in sync as they change.
- Retrieve first, then answer only from what was retrieved, with citations.
- Test against a bank of real questions, including ones with no good answer.
- Launch to internal staff before customers, and log everything for review.
What gets delivered
- Content ingestion and search index
- Assistant with cited answers
- Evaluation suite
- Feedback and review tools
Related
Similar examples
Customer portal for a field-services business
A self-service portal where customers book visits, approve quotes and pay invoices — replacing phone calls and emailed PDFs.
- React
- TypeScript
- Node.js
- PostgreSQL
Booking and scheduling platform
Online booking with real availability, deposits, reminders and calendar sync, built around one business's actual rules.
- Next.js
- TypeScript
- PostgreSQL
- Stripe
Does this sound like your project?
Tell us how yours differs. The details are where the real plan comes from.