A pipeline for document data — built for production, not demos.
Five stages, five guarantees: layout-faithful ingest, schema-correct extraction, deterministic validation, full audit, and delivery to the systems you already run.
Extract to your shape, not ours.
Define the data your downstream systems need. Bring your own JSON Schema, or compose one in our editor. Structora extracts to that contract — versioned, reusable, and consistent across millions of documents.
- 200+ pre-built schemas for credit, leases, M&A, and SEC filings
- Full JSON Schema support, including conditionals and references
- Schema versioning with diff and migration tools
- Per-field prompts, examples, and acceptance criteria
Catch the conflicts before a human does.
Rules engine reconciles values across exhibits, schedules, redlines, and amendments. Every check is deterministic, cited, and replayable — so when something goes wrong, you can prove what changed.
- Cross-document reconciliation across families & versions
- Mathematical checks (totals, schedules, accruals) baked in
- Custom rules in plain English or JSON Logic
- Conflicts surfaced with side-by-side source spans
Every field traced to its source span.
No black box. Hover any value and see the page, paragraph, and exact characters it came from — with model confidence, reviewer history, and a tamper-evident trail. Built for compliance teams that ask hard questions.
- Span-level citations on every extracted field
- Per-field confidence with thresholds you control
- Tamper-evident audit log with hash chain
- Reviewer workflows with sign-off & dual control
Drop into the systems you already run.
REST and streaming APIs. Native SDKs for Python, TypeScript, and Go. Webhooks, batch, and direct connectors to Snowflake, Databricks, S3, SharePoint, iManage, and NetDocuments. Self-hosted deployments for regulated environments.
- Async batch + streaming for low-latency workflows
- SDKs in Python, TypeScript, Go (fully typed)
- VPC and on-prem deployments for regulated data
- Connectors for warehouses, DMS, and storage
# Extract a credit agreement to your schema from structora import Client client = Client(api_key="sk_live_…") result = client.extract( document="s3://deals/2026/northstar.pdf", schema="credit_agreement.v3", rules=["jurisdiction_consistency", "commitment_match"], callback_url="https://halcyon.app/wh/structora", ) for field in result.fields: print(field.path, field.value, field.confidence) print(field.cite.page, field.cite.span)
Stream to where the work happens.
Structured data is only useful when it lands in the system that drives a decision. Push to your warehouse, your document management system, your portfolio book, or a custom destination — without rebuilding pipelines.
A quick honest comparison of the three approaches we hear from prospects most often.
| Capability | Generic LLM + RAG | Legacy IDP vendors | Structora |
|---|---|---|---|
| Custom schemas you control | Brittle | Vendor-defined | First-class, versioned |
| Span-level citations | No | Bounding boxes only | Per-field source spans |
| Cross-document validation | No | Limited | Deterministic engine |
| Confidence you can trust | Token-level only | Opaque scores | Calibrated, auditable |
| VPC / on-prem deployment | DIY | Yes | VPC, on-prem, air-gap |
| Time to first production schema | Weeks | Months | Days |