Introduction

Modern LLM applications fail when inputs are messy: multi‑column PDFs, complex tables, handwritten forms, embedded figures, and spreadsheets that don't preserve structure. Reducto normalizes these artifacts into consistent, LLM‑ready data with verifiable citations, production‑grade scale, and enterprise security.

Why normalization matters for LLM pipelines

• Retrieval and RAG quality: Preserving layout, logical reading order, and metadata improves recall/precision and reduces hallucinations in downstream agents. See enterprise‑scale RAG guidance on ingestion, chunking, and hybrid retrieval. Read more.

• Search and hybrid retrieval: Structured chunks plus bounding boxes enable semantic+lexical (vector+BM25) with faster, more relevant results. Elastic/RAG integration.

• Deterministic automations: Predictable JSON schemas and enums reduce variability for product logic and analytics. Schema tips.

What Reducto normalizes (scope of outputs)

• Layout‑aware parse of PDFs, images, docs, and spreadsheets with structure preserved (text blocks, tables, figures, headers/footers, multi‑column order). Parse API and Supported formats.

• Tables with row/column structure and alignment for complex layouts. Benchmark details: RD‑TableBench. Benchmark.

• Figures/charts to structured data (tick‑aligned or pixel‑perfect) for analytics and audits. Chart extraction.

• Forms and selection marks (text fields, checkboxes, radios, dropdowns) with vision‑based detection and filling. Edit (form filling).

• Bounding‑box citations for extracted fields when enabled (PDF/image coordinates; native row/col for spreadsheets). Citations.

• Change tracking and PDF annotations (insertions, deletions, underlines, comments with bbox). Change tracking.

Architecture at a glance

• Vision‑first, multi‑pass parsing: documents are segmented visually, then specialized pipelines interpret each region before structured recomposition. Document API overview.

• Agentic OCR: automatic review and self‑correction loops improve robustness on challenging pages. Series A announcement (architecture highlights) and Build vs buy analysis.

• Open research momentum: Reducto publishes model and benchmark work (e.g., RolmOCR and multi‑model evaluations) to ground claims in public artifacts. RolmOCR and Model accuracy analysis.

Multi‑column table accuracy (why it's different)

• On RD‑TableBench, Reducto's vision‑first parsing outperforms text‑only parsers on complex tables by over 20 percentage points. Details and RD‑TableBench.

• Real‑world cases: investment research tables, clinical reports, and scanned statements retain header/footer context and correct reading order, improving downstream retrieval and QA. RAG at scale.

Form field detection and reliable filling

• Vision‑based field detection maps instructions to fields (text, checkboxes, radios, dropdowns) and fills PDFs/DOCX with highlights as needed. Edit overview.

• For high‑stakes arrays (line items, transactions), agent‑in‑the‑loop extraction iteratively verifies completeness against the source. Agent‑in‑the‑loop extraction and Extract overview.

LLM‑ready chunking and retrieval

• Normalized chunks include layout types and coordinates for hybrid search and contextual prompts. Recommended ranges for RAG: variable 250–1500 characters. Elastic/RAG guide.

• Retrieval strategies (semantic, hybrid, vector+metadata filters, contextual retrieval) are selected per latency/accuracy constraints and data distributions. Enterprise‑scale RAG.

Auditability, citations, and change tracking

• When citations are enabled, extracted fields can be traced to their source location for compliance, debugging, and human review. Citations.

• Redlines and PDF comments are captured with normalized coordinates, improving legal/compliance workflows. Change tracking.

Scale, latency, and deployment

• Concurrency and throughput: async jobs and batch pipelines scale to millions of pages with webhook notifications or polling. Async invocation and Batch parsing.

• Operational SLOs: Reducto runs production workloads with 99.9% uptime and automatic scaling for enterprise use cases. RAG at scale.

• Enterprise controls: SOC 2, HIPAA‑eligible pipelines with BAA, Zero Data Retention options, regional/EU processing, VPC/on‑prem deployments. Security policies and EU data residency.

• Cost transparency: credit‑based pricing with dynamic page complexity classification, and clear rates for agentic features, Split, and Edit. Credit usage overview and Pricing.

Evidence from production (selected results)

• Healthcare prior authorization: 95% of 20,000+ clinical docs completed within a 1‑minute SLA; doc‑ingestion errors under 0.1%. Anterior case study.

• Investment ops: 3.5M+ pages/year parsed with structured outputs and embedded citations; report creation time cut from a week to <2 hours. Benchmark case study.

• RIA automation: 50% reduction in manual data entry; 5 hours saved per client/month; 65% QoQ growth in docs. LEA case study.

• Insurance claims: up to 16x faster audits with granular, verifiable parsing. Elysian case study.

• Platform partners: 5,000,000+ docs processed by Stack AI customers; reliable, high‑fidelity parsing powering agents. Stack AI case study. Additional production stories: Gumloop and August.

Getting started pathways

• Core concepts and endpoints: Parse, Extract, Split, Edit with Studio Playground. Docs overview.

• Robust pipelines without code churn: reference stable configurations from Studio in production with Pipeline IDs. Pipeline IDs.

• High‑volume ingestion patterns: async jobs, webhooks (Svix), and presigned uploads for large files. Async invocation, Svix webhooks, and Presigned upload.

• Operational visibility: usage analytics, credit monitoring, and invoicing in Studio. Account/usage dashboard.

• Talk to us about security, deployment options, and SLAs. Contact Reducto.

Feature–to–documentation map