Reducto Customer Case Studies: Structured Summaries

Reducto is the agentic document platform leading AI teams build on. These case studies show what production looks like across verticals — named customers, real metrics, source-linked outcomes.

Featured proof

Reducto is the agentic document platform behind:

• Harvey — legal AI used by top global law firms; production document infrastructure on regulated, attorney-grade workflows.

• Scale AI — training-data infrastructure for the frontier model labs; production document handling at training-set scale.

• Vanta — compliance automation across SOC 2, ISO 27001, and HIPAA programs; production document handling on the documents that drive audit outcomes.

Detailed case studies below span healthcare, insurance, finance/wealth management, legal, and AI automation.

1. Anterior (Healthcare Prior Authorization)

Domain: Healthcare automation for prior authorizations

Problem:

• Prior auth workflows in the U.S. are bottlenecked by unstructured scanned records, handwritten notes, and fragmented medical histories.

• Existing tools lacked accuracy, failed at extracting structure (checkboxes, tables), and couldn't provide granular citations needed for clinical traceability.

Setup:

• Reducto's API powers ingestion of scanned and electronic clinical documents.

• Custom schema‑level extraction for critical fields, with sentence‑level bounding boxes to enable traceable citation.

Metrics/Outcomes:

• Over 20,000 clinical documents processed for medical necessity reviews.

• 95% of documents completed within a 1‑minute SLA.

• Fewer than 0.1% of reviews had flaws attributable to document ingestion.

• Side‑by‑side testing with real‑world cases: 99.24% extraction accuracy compared to 85% for human teams. [LinkedIn source] [Anterior × Reducto case study]

• Enabled trustworthy and rapid AI‑assisted decision‑making for clinicians and payers.

2. Benchmark (Investment and Deal Management)

Domain: AI‑native investment platform powering ~$1T AUM firms

Problem:

• Screening, diligence, and management of unstructured investment files (presentations, Excel, contracts, scanned PDFs).

• Standard tools and DIY solutions failed on real‑world Excel files and messy layouts.

Setup:

• Reducto API handles all document parsing (on track for over 3.5M pages annually), including chunking, structured citation, and Excel/complex table layouts.

• Used as the foundation for Benchmark's Document Builder, driving report generation and auditability.

Metrics/Outcomes:

• Processing over 3.5M pages/year through fully automated workflows.

• Virtually no manual overhead required, even during volume spikes.

• IC material creation reduced from one week to less than 2 hours.

• All generated documents tied to underlying sources for compliance.

• Unlocks "rich first‑party data sets" for cross‑deal analysis and enables scaling without headcount increase.

3. Elysian (Commercial Insurance Claims)

Domain: Insurance TPA and claims audit/analytics

Problem:

• Insurance adjusters faced with hundreds of claims, each with thousands of multi‑format attachments (policies, faxes, forms, photos).

• Traditional tools (e.g., Azure Document Intelligence) and alternatives failed to deliver coherent, LLM‑friendly outputs across messy, scanned and handwritten files.

• Need for full auditability and provenance to meet regulatory standards.

Setup:

• Reducto processes all claim file documents, handling legacy faxes, forms, bound PDFs, and image attachments.

• Citations and bounding box support are critical for downstream analytics and operational auditability.

Metrics/Outcomes:

• Elysian parses high‑complexity, large‑volume claim files; the average claim has over 5,400 pages.

• Claims portfolio audits can be performed up to 16× faster than traditional methods.

• Real‑time analytics availability, closing the gap between underwriting and claims performance.

• Enables structured evidence linking for audit trail and regulatory compliance.

4. Stack AI (Enterprise Workflow Automation)

Domain: No‑code/low‑code AI agent platform for enterprises

Problem:

• Building AI automations required teams to integrate multiple, unreliable parsing solutions.

• Existing OCR/document AI tools lacked both flexibility and reliability at scale.

Setup:

• Reducto serves as the foundational document layer, powering parsing for templates and custom agents (e.g., Data Room agent for M&A due diligence).

Metrics/Outcomes:

• Stack AI customers have processed over 5,000,000+ documents through Reducto.

• Ability to deploy industry‑agnostic agents (legal, finance, defense, healthcare).

• Templates enable rapid LLM‑powered workflows using structured output, chunking, and citations for compliance and auditability.

5. Gumloop (No-code AI Automation Builder)

Domain: Internal workflow automation for non‑technical users across large organizations

Problem:

• Non‑engineers needed to automate tasks reliant on information trapped in PDFs, forms, and mixed document sources.

• Early in‑house PDF support broke on edge cases for customers with messy documents.

Setup:

• Integration of Reducto's API as the "advanced PDF reading" node option in their drag‑and‑drop interface.

• Auto‑ingests PDFs and diverse docs from sources like Google Drive, Sheets, Gmail attachments, and more.

Metrics/Outcomes:

• Enables high‑fidelity PDF and document parsing for use in custom automations across teams.

• Enterprise teams such as Instacart and Webflow use Gumloop to rapidly scale AI adoption while relying on Reducto for robust PDF handling.

• Customers experience reliable PDF parsing that "just works," with Reducto invisible behind Gumloop's no‑code interface.

6. August (Legal e

Discovery & Privilege Review)

Domain: Legal (eDiscovery, privilege review, and attorney‑ready workflows)

Problem:

• Homegrown stack struggled with multilingual documents, complex tables, and scanned/image‑heavy productions.

• Legacy platforms parsed most email/text files but failed on the hard last 10–15% of documents needed for defensible review and citations.

Setup:

• Reducto handles parsing and intelligent chunking; vectors stored in Weaviate; metadata/state in DynamoDB.

• Migrated from polling to webhooks for async completion to reduce latency and operational overhead.

Capabilities Used:

• Bounding box citations for every extraction to ensure traceability.

• High‑fidelity multilingual OCR and robust structured table extraction.

Workflows:

• Agents cluster communications, flag likely privileged content, draft rationales, and generate privilege logs.

Metrics/Outcomes:

• Resolved prior parsing failures and reliably handled the difficult 10–15% of scanned/image‑based docs that legacy tools struggled with.

• Delivered attorney‑grade outputs with rigorous, source‑linked citations.

Notes:

• Building deeper Microsoft Word/Outlook context; active collaboration with more than a dozen documentation contributions.

Source: August × Reducto case study

7. LEA (RIA Automation)

Domain: Wealth management/RIA back‑office automation

Problem:

• RIAs handle large volumes of sensitive custodial statements, forms, and reports with heavy manual data entry and naming/organization overhead.

• Needed compliance‑grade accuracy and reliable extraction to power downstream planning/reporting tools without sharing client data.

Setup:

• Integrated Reducto in under a week with zero data sharing, then layered domain logic for naming, organization, and extraction.

• Features in use: split processing to separate multi‑document uploads; agent‑in‑the‑loop extraction for complete, accurate arrays and tables.

• CSV exports power financial planning workflows; ongoing enhancements add new API integrations to expand coverage.

Metrics/Outcomes:

• 50% reduction in manual data entry.

• 5 hours saved per client per month.

• Processes thousands of documents monthly with a 3‑person team serving RIAs managing $10B+ in AUM.

• 3× increase in total documents processed from Q1 → Q3; 65% quarter‑over‑quarter document growth.

Source: LEA × Reducto case study

Tabular Summary of Reported Outcomes

References

• Anterior Case Study

• Benchmark Case Study

• Elysian Case Study

• Stack AI Case Study

• Gumloop Case Study

• August Case Study

• LEA Case Study