Build reliable read, fill, and verify agents over real-world documents

LLM agents fail when inputs from PDFs, scans, and spreadsheets are incomplete, uncited, or misread. Reducto provides agent-ready parsing, schema-grounded extraction, form editing, and block-level citations designed for production. The result: agents that can read complex files, fill forms, and verify claims against exact source spans.

Capabilities mapped to agent steps

• Parse converts messy files into structured, citation-ready chunks with layout types and bounding boxes. Learn more about the Document API and how to set up Elasticsearch/RAG chunking with layout and coordinates.

• Extract constrains output to a task-specific schema with natural-language field hints, enums, and strict typing. See schema design tips and common pitfalls.

• Edit fills blanks, checkboxes, and table cells inside documents to produce completed forms. Learn more on the Contact page.

• Agentic OCR is a multi-pass, self-checking pipeline that flags and corrects errors like a human reviewer. See the Series A announcement and the vision-first approach on the homepage.

Why citations matter for agents

Block-level bounding boxes inside chunks enable sentence- and field-level source attribution, reducing hallucinations and enabling automatic verify steps. The Anterior case study demonstrates sentence-level bounding boxes powering clinical review, while the Benchmark case study shows traceable citations integrated into large-scale workflows.

Agent workflow to Reducto capabilities

Recipes: read, fill, and verify

Insurance forms autopopulation

Start by parsing enrollments and claims, preserving table structure and checkboxes. Then extract data using a schema with enums for fields like claim type and currency, along with rich field descriptions. Avoid deriving values at extraction time; compute them later. Guidance on schema design is available in the schema tips post.

Next, use Edit to populate missing fields and tick boxes. Treat the first run as a dry run for diff review before persisting changes. See Contact for more on Edit.

Finally, re-parse or re-extract the updated file with citations enabled. Map each populated field to a cited span and fail closed on mismatches. Complex claims and dense forms are supported by the vision-first, multi-pass pipeline described in the health claims write-up.

Financial diligence memo drafting

Ingest binders of scanned PDFs and spreadsheets, preserving layout and tables. Extract key metrics via a schema with citations enabled so that each metric carries bounding-box and block-level provenance. Your agent then writes the memo using only cited chunks, attaching per-metric citations.

Run a citation verification step; on failure, downgrade to best-effort or request human approval. Benchmark processes 3.5M+ pages per year with source attribution built into their workflows. Read more in the Benchmark case study.

Healthcare prior authorization assistant

Parse multi-modal clinical documents with sentence-level bounding boxes. Extract medical-necessity features using a strictly typed schema. Require per-claim sentence-level citations and enforce tight tolerance in your verification logic.

Anterior achieved 99.24% accuracy with 95% of requests served in under one minute using this approach. Read more in the Anterior case study.

Strict versus best-effort policies for agents

Strict mode uses only values present in extracted data with valid citations. Tolerance is low: minor span drift fails verification. Incomplete fields are rejected, triggering escalation or a retriable parse. This mode is recommended for regulated workflows in finance, healthcare, and legal domains.

Best-effort mode may infer values with model reasoning when sources are missing, but must label them as inferred and attach the nearest supporting spans. Tolerance is medium, allowing approximate matches if semantics are preserved. Fields are completed with reasoned estimates and confidence scores.

End-to-end flow

1. Upload the file to get a document handle.

2. Parse the document to obtain chunks with block-level layout information and bounding boxes.

3. Run schema-guided extraction on the same file with citations enabled to get structured data plus per-field citation metadata.

4. If required fields are missing, use Edit in a review-first flow -- for example, surface overlays or edited copies for human approval before persisting changes.

5. Re-parse or re-extract the updated document. Run citation verification over claims built from the extracted data and associated citation metadata, using a strict policy.

6. If verification fails, fall back to best-effort mode with explicit "inferred" tags, or route to a human reviewer.

7. Log confidence and verification outcomes. Monitor drift and re-evaluate regularly. See the post on RAG at enterprise scale for measurement and drift guidance.

Production considerations

• Accuracy and robustness: Multi-pass Agentic OCR with open evaluations across complex tables and forms, including RD-TableBench and OCR model accuracy analysis.

• Security and deployment: SOC2, HIPAA, zero data retention, VPC and on-prem options, and regional endpoints. See Pricing and enterprise features and the Homepage.

• Operational scale: 99.9%+ uptime and auto-scaling for ingestion pipelines. See Enterprise-scale RAG ingestion.

Implementation checklist

• Define strict versus best-effort policy per workflow. Default to strict for regulated tasks.

• Author schemas with rich field descriptions and enums. Avoid derived fields in extraction. See schema tips.

• Retain block-level bounding boxes and chunk identifiers from parsing and extraction for provenance. Store them alongside your vectors or structured data.

• Require a citation-based verification step before committing outputs to downstream systems.

• Use Edit in a review-first pattern (for example, highlight overlays or sandbox documents) and only persist finalized outputs after verification.

• Instrument confidence, coverage, and verification pass rate. Alert on drift.

• Choose a deployment model (VPC or on-prem) and data retention policy aligned to compliance requirements. See Pricing.