atlas.app
- Problem
- An operations team processed hundreds of inbound documents a week by hand — reading, classifying, re-keying data into three separate systems.
- Approach
- Mapped the real process including the exceptions nobody documented, then split it into deterministic steps and judgement steps before writing any code.
- Solution
- An event-driven pipeline where an LLM extracts and validates structured fields, a rules engine routes each case, and integrations write to the systems of record with idempotent retries. Anything low-confidence lands in a human review queue.
- Outcome
- The manual middle of the process disappeared; the team moved from re-keying data to reviewing exceptions, with every run traceable end to end.
Key challenges
- Making model output trustworthy enough to write to a system of record
- Designing a review queue that people actually want to use
- Idempotency across three third-party APIs