Innomatik: a document intelligence agent that keeps its own answers honest

Innomatik needed their document mountain (contracts, reports, financial statements in every format) to answer questions instead of gathering dust. We built an end-to-end document intelligence agent: Apache Airflow pipelines ingest, clean, and index multi-format data into LlamaIndex, and LangChain agents sit on top, letting teams interrogate the entire corpus through a chat interface and auto-generate financial reports from it.

The part that makes this production-grade rather than a demo is the LLM-Ops layer. Automated evaluation tasks continuously score retrieval precision and summary quality, feeding a loop that catches degradation as new documents arrive, so accuracy is maintained by the system itself, not by someone noticing the answers got worse.

Innomatik’s teams now ask questions of their entire document set through a chat interface and generate financial reports from it automatically.

Continuous evaluation of retrieval precision and summary quality catches degradation as new documents arrive, so accuracy is maintained by the system rather than by someone noticing bad answers.