# InfoXtractor (ix)

Async, on-prem, LLM-powered structured information extraction microservice.

Given a document (PDF, image, text) and a named *use case*, ix returns a structured JSON result whose shape matches the use-case schema — together with per-field provenance (OCR segment IDs, bounding boxes, cross-OCR agreement flags) that let the caller decide how much to trust each extracted value.

**Status:** design phase. Implementation about to start.

- Full reference spec: [`docs/spec-core-pipeline.md`](docs/spec-core-pipeline.md) (aspirational; MVP is a strict subset)
- **MVP design:** [`docs/superpowers/specs/2026-04-18-ix-mvp-design.md`](docs/superpowers/specs/2026-04-18-ix-mvp-design.md)
- Agent / development notes: [`AGENTS.md`](AGENTS.md)

## Principles

- **On-prem always.** LLM = Ollama, OCR = local engines (Surya first). No OpenAI / Anthropic / Azure / AWS / cloud.
- **Grounded extraction, not DB truth.** ix returns best-effort fields + provenance; the caller decides what to trust.
- **Transport-agnostic pipeline core.** REST + Postgres-queue adapters in parallel on one job store.