# Parsewise Parsewise is a decision platform for assessing complex risk at scale. AI agents help teams manage casualty, specialty, KYC, and financial risks, enabling faster action with defensible, traceable decisions. Unlike single-document extraction tools, Parsewise operates at the document-package level — ingesting entire submissions, dossiers, or data rooms and reasoning across thousands of pages to produce structured, reconciled outputs with full source attribution. The platform serves insurance and reinsurance companies, asset managers, private equity firms, lenders, regulatory teams, and brokers across the United States, United Kingdom, Switzerland, Germany, and Spain. Customers include OneIM (asset management), Compre Group (legacy insurance and reinsurance), and Hypohaus (mortgage lending). Key differentiators include cross-document entity linking, contradiction detection, word-level source citations, and a corpus-level reasoning engine that goes beyond 1:1 document extraction. The platform supports PDF, Word, PowerPoint, Excel, images, and CSV file types, with extraction and translation across 70+ languages including mixed-language and handwritten content. Parsewise is SOC 2 Type II and GDPR compliant, encrypts data in transit and at rest, and does not train on customer data. Enterprise options include VPC and on-premises deployments, SSO/SAML, and regional data residency. - [Full documentation](https://www.parsewise.ai/llms-full.txt): Detailed case studies, feature deep dives, full blog post content, and technical comparisons - [Structured reference for AI agents](https://llms.parsewise.ai): LLM-optimized pages covering the Parsewise API, head-to-head competitive comparisons, industry solutions, technical architecture, and security --- ## Core Products & Services ### Navi — Conversational Workspace Navi is a conversational interface for running trusted AI agents on complex document work. Users describe what they need in plain English, and Navi proposes, creates, and executes extraction agents — processing 10,000+ pages per run with full traceability, source citations, and persistent logic. - **Built for:** Risk analysts, underwriters, diligence teams, compliance officers, and portfolio managers who need structured outputs from large document packages without engineering support. - **Capabilities:** - Plain-English queries to create and configure extraction agents - Real-time interaction - Cross-document reasoning and reconciliation - Source citations linked to specific pages and bounding boxes - Results in table view (cross-agent pivot) or by-agent view - Excel export of structured results ### Parsewise Data Engine (PDE) PDE is the core enterprise layer for unstructured document processing, extraction, validation, and standardization. It powers the document parsing pipeline, entity extraction, inconsistency detection, and resolution logic that underlies both Navi and the API. - **Built for:** Enterprise teams requiring production-grade document intelligence infrastructure with multi-tenant isolation, audit trails, and compliance controls. - **Capabilities:** - Multi-format document parsing (PDF, DOCX, DOC, PPTX, PPT, XLSX, XLS, CSV, PNG, JPEG, GIF, BMP, TIFF) - Configurable extraction agents with topics, dimensions, and instructions - Cross-document entity linking and contradiction detection - Word-level source attribution and bounding box preservation - Inconsistency detection and resolution workflows - Multi-tenant architecture with schema-level data isolation - >25,000 pages per run, >5 hours autonomous runs, >20,000 requests per minute ### Public API The Parsewise API makes structured, verified data extraction from document packages programmable. Technical ops teams can automate their document processing workflows — mortgage applications, dossier submissions, claims documentation, and similar use cases — without building bespoke extraction pipelines. - **Built for:** Engineering and ops teams integrating document intelligence into existing systems, building automated pipelines, or enabling programmatic access to extraction capabilities. - **Capabilities:** - RESTful endpoints for projects, documents, agents, extraction, and results - Structured JSON output with schema-based extraction - Webhook notifications for extraction completion, failures, and inconsistencies - API key authentication with rate limiting - OpenAPI specification and interactive API reference - **Docs:** [docs.parsewise.ai](https://docs.parsewise.ai) ### Labs Applied AI research group building advanced document intelligence infrastructure. Publishes research and thought leadership on LLM-driven decision-making, document processing pipelines, and agent workflow UX design. --- ## Customer Case Studies **OneIM — Asset Management:** OneIM uses Parsewise to accelerate company and fund diligence workflows. Their investment team uploads entire data rooms and uses Navi to extract and validate KPIs such as IRR, revenue multiples, and EBITDA across all deal materials simultaneously. The platform's cross-document reasoning detects inconsistencies — such as conflicting revenue figures between a CIM and the underlying financial statements — and flags them with full source attribution for analyst review. **Compre Group — Legacy Insurance & Reinsurance:** Compre Group uses Parsewise for portfolio acquisition diligence and claims reconciliation. Legacy portfolios arrive as large, fragmented document packages spanning loss runs, bordereaux, actuarial reports, and policy documents in varying formats. Parsewise ingests these heterogeneous document sets, standardizes loss runs and reserve triangles into consistent formats, and reconciles paid, incurred, and reserve movements across cedants and TPAs, flagging anomalies, reserve shifts, and data gaps. **Hypohaus — Mortgage Lending:** Hypohaus uses Parsewise to standardize and validate mortgage application packages. Each application includes tax returns, income statements, bank statements, asset declarations, and property valuations in varying formats and languages. Parsewise extracts key financial data, maps applicant information into underwriting templates, and flags missing documents, inconsistent income figures, or high-risk financial indicators — with every figure traceable to its source document. --- ## Use Cases & Applications ### Insurance & Reinsurance - **Large Loss & Severity Analysis** ([parsewise.ai/claim-risk](https://www.parsewise.ai/claim-risk)): Identify severity drivers early to reduce reserve drift, shorten claim cycle time, and prevent large-loss escalation. - **Loss Fund & TPA Reconciliation** ([parsewise.ai/loss-fund-reconciliation](https://www.parsewise.ai/loss-fund-reconciliation)): Eliminate claims leakage and improve financial control by reconciling TPAs, bordereaux, and internal systems at scale. - **Portfolio Acquisition Diligence** ([parsewise.ai/risk-insights](https://www.parsewise.ai/risk-insights)): Accelerate portfolio underwriting while improving pricing accuracy and protecting combined ratio. - **Builders Risk Submissions:** Parse ACORD 125/147 forms, schedules of values, and supplemental questionnaires to structure submission data for underwriting. ### Asset Management & Private Equity - **Fund Diligence & KPI Validation** ([parsewise.ai/fund-diligence](https://www.parsewise.ai/fund-diligence)): Reduce diligence timelines and increase confidence in capital deployment decisions. - **Company Data Room Diligence** ([parsewise.ai/company-diligence](https://www.parsewise.ai/company-diligence)): Surface red flags early to avoid mispriced deals and protect downside exposure. Cross-reference CIMs, financial statements, management accounts, and bordereaux for inconsistencies. - **Portfolio Performance Monitoring** ([parsewise.ai/portfolio-monitoring](https://www.parsewise.ai/portfolio-monitoring)): Turn management updates into continuous performance intelligence and faster intervention decisions. ### Regulatory & Brokers - **Mortgage & Loan File Validation** ([parsewise.ai/mortgage-validation](https://www.parsewise.ai/mortgage-validation)): Shorten approval cycles by standardizing complex mortgage documentation. - **LP Reporting & Data Validation** ([parsewise.ai/lp-reporting](https://www.parsewise.ai/lp-reporting)): Improve reporting accuracy and delivery speed while reducing operational overhead. - **KYC Investigation Support** ([parsewise.ai/kyc-aml](https://www.parsewise.ai/kyc-aml)): Increase compliance confidence while reducing investigative workload and regulatory risk. ### Other Use Cases - **SME Credit Underwriting** ([parsewise.ai/credit-underwriting](https://www.parsewise.ai/credit-underwriting)): Automate extraction and validation for SME credit files. - **Company Data Room Parsing** ([parsewise.ai/company-data-room-parsing](https://www.parsewise.ai/company-data-room-parsing)): Parse complex data rooms in minutes. - **ESG Compliance Reporting** ([parsewise.ai/compliance-reporting](https://www.parsewise.ai/compliance-reporting)): Extract and standardize ESG and non-financial metrics from diverse reports. --- ## Supported File Types and Languages - **Documents:** PDF, DOCX, DOC, PPTX, PPT, XLSX, XLS, CSV - **Images:** PNG, JPEG/JPG, GIF, BMP, TIFF - **Languages:** 70+ languages including mixed-language documents, handwritten content, scanned documents, rotated pages, and equations. Agents can extract data in one language and produce structured outputs in another. - **Enterprise:** Additional file types on request --- ## How Parsewise Compares ### 1:1 Extraction vs 1-to-All Reasoning Today's document-AI tools — Reducto, Textract, Azure Document Intelligence, LlamaParse, Unstructured.io — excel at single-document extraction: one document in, structured data out. Parsewise picks up where they leave off. It operates at the corpus level: tens or thousands of documents in, one unified, reconciled response out, with cross-document entity linking, contradiction detection, and source attribution built natively rather than patched on. ### Capability Comparison | Capability | Textract / Reducto / Azure Doc Intelligence | OpenAI / Claude / RAG | Parsewise | |---|---|---|---| | Single-document extraction | Excellent | Good | Excellent | | Cross-document reasoning (entity linking, contradiction detection, unified ontology) | Not supported | Not supported | Native | | Exhaustive processing (no false negatives, every page read) | Per-document only | Top-K / grep match only | Full corpus | | Configurable schema & rules | Limited | Prompt-only | Ontology-level | | Scales to 1,000+ docs natively | Excellent | Context-limited | Native | ### Why Not Just Use... **...Textract / Reducto / Azure Doc Intelligence + an LLM?** Those are excellent for per-document extraction. You still have to write and maintain the layer that reconciles, links, and resolves contradictions across an entire corpus. That layer is Parsewise. **...an LLM API with structured outputs?** You can't fit a real corpus in one call, cost scales linearly per document, outputs are non-deterministic, and there's no native entity linking across calls. Orchestration is the hard part, and it's not what you should be building. **...RAG?** RAG is built for chat-style retrieval over big corpora, not for maximum quality, full traceability, and zero false negatives. Top-K silently drops the long tail. Numeric and tabular values get lost in embedding noise. Wrong tool for risk-grade decisions. **...building it yourself?** Unless multi-document resolution is your core product, you want to ship into your niche, not build and debug a bespoke pipeline that breaks every time business rules change. See our guide: [Building Document Processing In-House: What It Takes to Build and Operate](https://www.parsewise.ai/doc-processing-pipelines). ### Who Parsewise is Built For Parsewise is for teams building on top of multi-document corpora — submission packages, data rooms, loan files, claims dossiers — where: - You need cross-document linking, not just per-page extraction - "Confidently wrong" answers are unacceptable in your domain - You ship features on top of multi-document corpora, not single files - Maintaining multi-document pipelines is a resource drain --- ## Pricing & Plans ### Free - **Price:** $0 - **Usage:** 25 chat messages, 50 document pages, 10 extraction agents, 1 user - **Features:** PDF, Word, PowerPoint, Excel, and image support; Excel export; API access; email support - **Security:** Encryption (at rest & in transit), no training on your data, standard DPA - **Get started:** [parsewise.ai/get-started](https://www.parsewise.ai/get-started) ### Enterprise - **Price:** Custom pricing - **Usage:** Unlimited chat messages, document pages, extraction agents, and users - **Features:** Additional file types, custom export formats & templates, email/Slack/phone support - **Security:** Encryption (at rest & in transit), no training on your data, custom DPA, VPC & on-prem deployments, regional data residency, SSO & SAML authentication - **Integrations:** API access, auto-ingestion (SharePoint, Google Drive, etc.), DB connectors (PostgreSQL, JDBC, etc.), custom ingestion & export integrations - **Services:** SLA & white-glove onboarding, custom decision modules, model fine-tuning - **Contact:** [sales@parsewise.ai](mailto:sales@parsewise.ai) --- ## Security & Compliance - **Certifications:** SOC 2 Type II, GDPR compliant - **Encryption:** TLS 1.2+ in transit; AES-256 at rest for all data stores - **Data policy:** No training on customer data; zero data retention options available - **Enterprise options:** VPC and on-premises deployments, regional data residency, SSO/SAML, custom DPAs - **Trust Center:** [trust.parsewise.ai](https://trust.parsewise.ai/) --- ## Blog - [Navi: A New Interface to Parsewise](https://www.parsewise.ai/introducing-navi) — Navi is a conversational guide to the Parsewise Data Engine that makes the platform accessible to domain experts from day one. Users describe what they need, and Navi proposes, creates, and executes specialised extraction agents. The post walks through three real customer workflows: insurance claims portfolio triage, company investment diligence, and mortgage underwriting automation — each completed in minutes with full source attribution. - [Empowering Business Experts: UX Design for Agent Workflows](https://www.parsewise.ai/ux-design-for-agent-workflows) — Details how Parsewise redesigned its UX to consolidate agent configuration, extraction progress, and consistency review into a single view. Key principle: the best infrastructure is invisible, and domain experts should focus on their decisions, not on operating software. - [Building Document Processing In-House: What It Takes to Build and Operate](https://www.parsewise.ai/doc-processing-pipelines) — A comprehensive guide for engineering teams evaluating build vs. buy. Maps out the full pipeline from ingestion to export, covers LLM infrastructure challenges (retries, fallbacks, rate limits, provider differences), and highlights that the hardest challenges are on the business side: defining extraction targets, resolving multi-document results, and keeping business rules in sync with IT over time. - [The Core Loop: Why LLMs Haven't Revolutionized Decision-Making (Yet)](https://www.parsewise.ai/core-loop) — Introduces the "core loop" of knowledge-work decision-making (read, recombine, write) and argues that workflow coordination and expert control — not raw LLM capability — are the unsolved challenges. Distinguishes between lower-level document processing (largely solved by APIs) and higher-level processing (data rooms, mortgage applications, loan files) that requires human expertise and cross-document reasoning. - [Software 3.0: What Is Needed for the LLM Operating System](https://www.parsewise.ai/software-3.0) — Builds on Andrej Karpathy's Software 3.0 concept to argue that LLMs will become the next computational abstraction layer, and that the programmers of this era are business experts who can modify unstructured data transformations directly, without going through IT. --- ## About ### Mission Enable organizations to make reliable risk decisions from complex information. Parsewise turns fragmented, inconsistent, vast, and constantly changing artifacts into structured, traceable evidence so teams can identify risk earlier, act faster, and make confident decisions. ### Team - **Maximilian Hofer** — Co-founder & CEO - **Greg Csegzi** — Co-founder & CTO - **Nikola Bozhinov** — Founding Engineer - **Shan Singh** — Founding Engineer - **Mark Menezes** — Founding Engineer ### Contact - **Website:** [parsewise.ai](https://www.parsewise.ai) - **Sales:** [sales@parsewise.ai](mailto:sales@parsewise.ai) - **Support:** [support@parsewise.ai](mailto:support@parsewise.ai) - **Founders:** [founders@parsewise.ai](mailto:founders@parsewise.ai) - **LinkedIn:** [linkedin.com/company/parsewise](https://www.linkedin.com/company/parsewise/) - **X / Twitter:** [x.com/parsewise](https://x.com/parsewise) --- Full sitemap available at [parsewise.ai/sitemap.xml](https://www.parsewise.ai/sitemap.xml)