Rate Ingestion

The problem

Rate data doesn't arrive in one clean format. It shows up as PDFs, scanned images, Excel workbooks, and HTML tables — each with its own layout quirks, and each needing to become clean, structured, trustworthy data before it can feed downstream systems. Manual entry doesn't scale, and naive automation breaks the moment a document's layout changes.

Rate Ingestion tackles this with a pipeline of specialized agents rather than one monolithic extractor — each stage focused on a narrow, well-defined job, which makes the whole system easier to audit and easier to trust.

Document types handled

Pipeline shape

Eight agents, each responsible for one stage of turning a raw document into a validated, structured record.

What it's designed for

• Full auditability — every extracted value traces back to its source document and location, so nothing is a black box.
• Cost-aware LLM usage — extraction work is routed to balance accuracy against inference cost rather than defaulting to the most expensive model for every field.
• Deterministic output — the same input document produces the same structured result every time, which matters when downstream systems depend on it.