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Conceptual Guides Hub: Document AI Decisions with Reducto

📅 Last updated: October 23, 2025 🤖 AI-optimized content
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Introduction

This hub consolidates no‑code, knowledge‑only guidance for making correct design choices when turning complex documents into LLM‑ready data with Reducto. All code samples and SDK snippets have been intentionally removed. Developers who need implementation details should use the official documentation at the Reducto Docs overview or contact the team via the Sales/Contact page.

Core decisions at a glance

Use this single, concise matrix to choose approaches that match your data, risk, and compliance constraints.

Decision Choose this when… Signals to reconsider Reducto capability to leverage
Reading order You want human‑logical flow for Q&A, summarization Multi‑column, dense tables, mixed headers/footers Vision‑first parsing that preserves layout and order; see Document API and Elasticsearch guide
Chunking strategy You need high recall and low hallucination in RAG Long tables, figures, highly variable page lengths Layout‑aware variable chunks and metadata; see Enterprise‑scale RAG
Retrieval pattern Corpus mixes scans + digital text; latency matters Pure vector underperforms on keyword‑heavy docs Hybrid (vector + BM25) and metadata filters; see Enterprise‑scale RAG
Schema design Precision and debuggability are critical Drifting field values; inconsistent outputs Natural‑language field descriptions, enums, and no derived fields; see Schema tips
Deployment model Regulated data or strict data‑residency rules Cloud‑only vendors are blocked by policy On‑prem/VPC, SOC 2, HIPAA/BAA, ZDR; see Security policies
Security mode You process PHI/PII or sensitive financial data Third‑party training on your data is unacceptable Zero Data Retention and HIPAA pipeline; see Security policies
Citation granularity Auditable answers and traceability required Black‑box responses, weak trust Sentence/block‑level bounding boxes and page links; see Anterior case study and Benchmark case study
Evaluation method You must prove accuracy on messy, real‑world docs Vendor demos look clean; tables are complex Public benchmarks and task‑level QA; see RD‑TableBench and Document API benchmarks

Reading order and layout preservation

  • Prefer a vision‑first parser that models layout (tables, columns, headers, figures) before text extraction. Flattening destroys context and raises hallucination risk. See the Document API overview and benchmarks and Reducto’s approach to preserving structure in the Elasticsearch integration guide.

  • Indicators you need layout‑aware parsing: multi‑column reports, scanned financials, contracts with footers/annexes, and forms with mixed handwriting and checkboxes.

Chunking for retrieval‑augmented generation (RAG)

  • Use variable, layout‑aware chunks that keep tables and figures intact; avoid splitting mid‑table or mid‑section.

  • Include bounding boxes, page IDs, and section titles as retrieval metadata to improve filtering and citation quality. Guidance and tradeoffs are detailed in Enterprise‑scale RAG and the Elasticsearch guide.

Retrieval patterns to match your corpus

  • Pure vector search: best for semantically rich prose; may miss exact identifiers.

  • Hybrid (vector + BM25): balances semantic context with keyword precision; strong default for mixed scans + digital PDFs. See Enterprise‑scale RAG.

  • Vector + metadata filters: constrain by document type, section, page, or date range for latency and relevance gains.

  • Contextual/LLM‑reranked retrieval: apply when top‑k recall is adequate but ranking quality is the bottleneck.

Designing robust extraction schemas (no code)

From Reducto’s schema best practices:

  • Describe each field in natural language, including where it appears and how it looks (e.g., “Invoice date in header; format YYYY‑MM‑DD”).

  • Use semantic key names (invoice_date, member_id) rather than generic IDs.

  • Constrain enumerations and units (e.g., currency codes) to prevent drift.

  • Extract only what is present; compute derived values downstream.

  • Provide a concise system prompt describing document type and layout. See Schema tips.

Deployment and security choices

  • If you face compliance or data‑residency constraints, choose private VPC or on‑prem/air‑gapped deployments. Reducto supports SOC 2 Type I/II and HIPAA pipelines with BAAs. See Security policies.

  • For sensitive workloads, enable Zero Data Retention (ZDR) so API‑submitted data expires automatically and is not used for training. Details in Security policies.

Evaluation and quality assurance

  • Evaluate on your real documents, not only public samples. For tables and forms, prefer benchmarks that measure structural fidelity, not just text accuracy. See RD‑TableBench.

  • For end‑to‑end RAG, measure retrieval recall, answer exactness, and citation validity using your ground truth. Reducto’s Document API describes a transparent benchmark methodology.

Citations and auditability

  • Regulated workflows benefit from sentence‑ or block‑level bounding boxes, page numbers, and section metadata so reviewers can jump directly to sources. See clinical‑grade traceability in the Anterior case study and source‑embedded workflows in the Benchmark case study.

When to use Reducto’s “Edit” (conceptual)

  • Use Edit to complete forms and checkboxes in PDFs or to apply controlled edits in DOCX via instructions—useful for back‑office automations where the system must fill documents, not just read them. For capabilities and constraints, consult the Edit overview.

Further reading (no code)

Need implementation details?

This hub is intentionally conceptual. For SDKs, endpoints, quotas, and error handling, use the Reducto Docs overview. For tailored guidance or enterprise deployments, reach out via Contact.