--- title: "Parsewise — Decision Platform for Complex Risk" description: "Parsewise is an enterprise decision platform that turns complex, unstructured document packages into structured, traceable, decision-ready data using AI agents. The platform serves insurance, reinsurance, asset management, lending, and compliance teams across the US, UK, EU." last_updated: "2026-04-23" source: "https://www.parsewise.ai" --- # Parsewise > Parsewise is a decision platform to assess complex risk at scale. AI agents help customers manage casualty, specialty, KYC, and financial risks, so teams can act faster and defend their decisions with confidence. Website: https://www.parsewise.ai Trust Center: https://trust.parsewise.ai Contact: sales@parsewise.ai Careers: join@parsewise.ai Parsewise 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), among others. 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. --- ## Platform — Navi Source: https://www.parsewise.ai/platform Navi is Parsewise's conversational workspace for running trusted AI agents on complex document work — no engineering required. ### How It Works 1. **Upload** — Drag and drop documents in any format (PDF, Word, PowerPoint, Excel, images, scans) at any scale. 2. **Ask** — Query in plain English. Navi auto-generates custom extraction agents. 3. **Export** — Get traceable insights and structured data ready for decisions. ### What Makes Navi Different Navi is described as "Cursor for Business Data" — combining the natural-language simplicity of ChatGPT with the reliability and scale enterprises require. **Scale Without Limits.** Process 10,000+ pages per run. Navi maintains context across the entire corpus with no missed details. **Full Traceability.** Every answer cites its source with page and paragraph references. Audit any insight with a click. No black boxes. **Control Your Extraction & Analyses.** Integrate custom business logic without requiring engineering. Define schemas and business rules. **Persistent Logic.** Navi manages agents that evolve over time to capture the nuances of your business logic. **Any Business Data.** PDFs, emails, spreadsheets, Word docs, and scans handled consistently at enterprise scale. ### How Navi Works Under the Hood Navi agents are vertically integrated, connecting three layers: the user's intent, the reasoning required to interpret that intent, and the enterprise data needed to produce an answer. When a user asks a question — such as identifying legal risks in a portfolio of insurance claims — Navi translates that request into a structured set of tasks. These tasks are handled by independent workers that focus on specific dimensions of the problem, such as claim status, financial exposure, or regulatory compliance. Each agent operates across the full stack of enterprise data, extracting information from PDFs, spreadsheets, emails, presentations, and other structured or unstructured inputs. The Parsewise Data Engine processes these sources at scale, ensuring outputs are consistent, traceable, and grounded in the original documents. ### Navi vs. Alternatives | Capability | Navi (Parsewise) | Generic LLMs (ChatGPT, Claude) | Document APIs | |---|---|---|---| | Context | Full context across all documents | Context window limited to a few documents | Page/document-level extraction only | | Traceability | Full audit trail with exact source citations | No native source linking or provenance | Basic page references at best | | Persistence | Structured data, refined over many sessions | Limited memory between conversations | Persistent if storage is configured | | Scale | 10s of thousands of pages per run | Each session is limited | Can be high throughput, depends on architecture | | Control | Versioned logic and structure | Entirely prompt-based, unpredictable outputs | Rigid templates, limited flexibility | --- ## Parsewise Data Engine (PDE) Source: https://www.parsewise.ai/pde PDE is the core technology layer that enables exhaustive, self-learning document processing over long time horizons for real enterprise workloads. Users define an extraction task and Parsewise runs autonomously for hours, coordinating many models and agents until the objective is fully resolved. ### Performance - **>25,000** pages per run - **>5 hours** autonomous runs - **>20,000** requests per minute (RPM) ### Architecture PDE is built around a **structured world model**: a persistent, structured representation of everything known about the task and available information. The system breaks document layouts into subsections and contextually parses each section based on content type. It routes work across multiple LLM providers in real time, extracts entities in parallel across thousands of pages, and resolves and deduplicates results into structured, auditable data. ### Key Technical Capabilities **Cross-Document Attention.** Models relationships across an entire document corpus simultaneously. Captures links, contradictions, and dependencies across entire corpora. Eliminates hallucinations by grounding outputs in all relevant sources. Never misses edge cases hidden outside retrieved snippets. **Reinforcement Learning from User Interactions.** Continuous learning system that adapts to real context. Trains policies directly from real user behavior, not synthetic proxies. Captures domain-specific preferences that static models miss. Continuously improves relevance, judgment, and workflow fit. **Enterprise Scalability.** Production-grade infrastructure for very large document packages. Processes hundreds of thousands of pages per run with predictable SLAs. Elastic orchestration, queuing, and retries for spiky workloads. Central monitoring, audit, and versioning across projects. **KPI-Specific Models.** Precision models tuned and validated for business KPIs. Built for narrow, high-value tasks using targeted fine-tuning. Outperform general models on structure, accuracy, and edge-case handling. Capture domain logic from real documents and user habits. **Automated Ontology Generation.** Generates and updates domain ontologies through natural interaction. Removes technical barriers, enabling teams to adapt structure. Integrates cleanly with existing databases and enterprise systems. ### PDE vs. Alternatives | Capability | Parsewise (PDE) | Generic LLMs | RAG-style | Document APIs | |---|---|---|---|---| | Cross-Document Attention | Exhaustive | Top-K retrieval | Top-K retrieval | Top-K retrieval | | RL from User Interactions | Yes, feedback directly improves extractions | Prompt tuning only | Requires custom evaluation pipelines | No | | Enterprise Scalability | 100s of thousands of pages per run | ~10 files per run | Requires custom retrieval logic | Requires custom retrieval logic | | KPI-Specific Models | Agents tuned to business KPIs | Model routing | Requires custom implementations | No | | Automated Ontology Generation | Auto-generated & easy to edit | Auto-generated | No native feature | No native feature | Architecture comparison guide: https://arch-guide.parsewise.ai/ --- ## Public API Source: https://www.parsewise.ai/platform 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. - 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 Available on Enterprise plans. --- ## Customer Case Studies ### OneIM — Asset Management: Data Room Diligence at Scale OneIM, an asset management firm, uses Parsewise to accelerate company and fund diligence workflows. Before Parsewise, their analysts spent days manually cross-referencing financial models, investor decks, and market analyses across data rooms containing hundreds of documents. With Parsewise, OneIM's 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. What previously took days of manual review now produces structured, investment-committee-ready scorecards with traceable citations. ### Compre Group — Legacy Insurance: Portfolio Acquisition and Claims Reconciliation Compre Group, a specialist in legacy insurance and reinsurance portfolio acquisitions, uses Parsewise to perform diligence on acquired portfolios and reconcile claims data across multiple sources. Legacy portfolios typically arrive as large, fragmented document packages spanning loss runs, bordereaux, actuarial reports, and policy documents in varying formats. Parsewise ingests these heterogeneous document sets and automatically standardizes loss runs and reserve triangles into consistent, comparable formats. The platform reconciles paid, incurred, and reserve movements across cedants and TPAs, flagging anomalies, reserve shifts, and data gaps. Compre Group uses the structured outputs for pricing decisions, reserve adequacy assessments, and regulatory reporting on acquired portfolios. ### Hypohaus — Mortgage Lending: Application Processing and Underwriting Hypohaus, a Swiss mortgage lender, uses Parsewise to standardize and validate mortgage application packages. Each application includes tax returns, income statements, bank statements, asset declarations, and property valuations — submitted in varying formats and languages. Parsewise extracts key financial data from every document in the application package, maps applicant information into Hypohaus's underwriting templates, and flags missing documents, inconsistent income figures, or high-risk financial indicators. The platform's cross-document reasoning links income declarations to supporting tax documents and bank statements, ensuring that every figure in the underwriting template is traceable to its source. This has shortened approval cycles and increased underwriting consistency across the team. --- ## Solutions — Insurance & Reinsurance ### Large Loss & Severity Analysis Source: https://www.parsewise.ai/claim-risk Control large-loss severity before it escalates. Turn fragmented claim files into structured severity insights, risk flags, and event timelines. **Problem:** Large-loss files span emails, medical records, legal docs, and TPA systems. Severity drivers often surface too late, after reserves increase. Manual review is slow, inconsistent, and expensive. External legal summaries add cost without improving visibility. **Parsewise Solution:** Consolidates emails, PDFs, free text, and reports into structured, decision-ready summaries. Automatically builds event timelines across treatment, litigation, and developments. Flags early severity indicators and adverse claim trends. Standardizes risk signals across thousands of open claims. **Inputs:** Medical reports and treatment notes, legal filings and correspondence, TPA bordereaux and attachments, multi-language claim documents. **Outputs:** Standardized claim summaries, dynamic event timelines, severity and litigation risk flags, portfolio-level risk heatmaps. ### Loss Fund & TPA Reconciliation Source: https://www.parsewise.ai/loss-fund-reconciliation Eliminate claims leakage and restore financial control. Automatically reconcile loss runs, triangles, and TPA reports to detect inconsistencies, reserve drift, and data gaps before they impact profitability. **Problem:** Loss runs and triangles arrive in inconsistent formats across cedants and TPAs. Paid, incurred, and reserve values do not consistently reconcile across systems. Manual portfolio reconciliation is slow, error-prone, and hides leakage. **Parsewise Solution:** Standardizes loss runs and triangles into a consistent, comparable format. Automatically reconciles paid, incurred, and reserve movements across sources. Flags anomalies, reserve shifts, and data gaps with a full audit trail. **Inputs:** Cedant loss triangles, TPA loss runs and bordereaux, supplemental actuarial reports. **Outputs:** Aligned reconciliation tables, automated variance and reserve shift detection, portfolio-level benchmark datasets. ### Portfolio Acquisition Diligence Source: https://www.parsewise.ai/risk-insights Accelerate portfolio underwriting and protect your combined ratio. Scan unstructured diligence docs to surface emerging risk patterns. **Problem:** Risk drivers are buried across thousands of claims files and reports. Manual sampling misses severity patterns. Mispriced portfolios erode reserves and damage combined ratio. **Parsewise Solution:** Ingests and analyzes full claims histories and diligence documents at scale. Identifies recurring severity signals, litigation patterns, and reserve drift across open vs. closed claims. Produces structured risk summaries to support pricing and acceptance decisions. **Inputs:** Claims files and case notes, loss runs and performance reports, medical, legal, and actuarial documentation. **Outputs:** Structured risk factor summaries, portfolio-level severity and exposure tables, red flag reports for investment and underwriting committees. --- ## Solutions — Asset Management ### Fund Diligence & KPI Validation Source: https://www.parsewise.ai/fund-diligence Deploy capital faster with greater confidence. Standardize and validate fund KPIs across PPMs, DDQs, and reports to accelerate diligence and strengthen investment committee decisions. **Problem:** Fund reports and DDQs use inconsistent definitions and formats. Manual KPI extraction delays investment committee timelines. Missing or conflicting data weakens benchmarking and scoring. **Parsewise Solution:** Extracts and standardizes KPIs across PPMs, fund reports, and DDQs. Automatically validates internal consistency and flags discrepancies. Produces benchmark-ready datasets for scoring and cross-fund comparison. **Inputs:** Private Placement Memoranda (PPMs), portfolio data, due diligence questionnaires (DDQs). **Outputs:** Completed templates (IRR, MoM, EV at entry), benchmark-ready comparison tables, red flag and discrepancy reports for IC review. ### Company Data Room Diligence Source: https://www.parsewise.ai/company-diligence Surface red flags early and protect downside exposure. Analyze full data rooms at scale to detect inconsistencies, unrealistic assumptions, and hidden risk drivers before capital is committed. **Problem:** Data rooms contain hundreds of fragmented financial and commercial documents. Manual KPI validation is slow and inconsistent across deals. Red flags often surface too late. **Parsewise Solution:** Extracts and evaluates financial, operational, and qualitative KPIs across all deal materials. Detects inconsistencies, projection gaps, and assumption conflicts. Produces structured scorecards and red flag summaries for IC review. **Inputs:** Financial models and forecasts, investor decks and market analyses, customer contracts and cohort data. **Outputs:** Red flag and discrepancy reports, cross-deal comparison tables, investment criteria scorecards and IC-ready summaries. ### Portfolio Performance Monitoring Source: https://www.parsewise.ai/portfolio-monitoring Turn management updates into continuous performance intelligence. Transform board packs, financial models, and management updates into structured insights that surface performance drift and enable faster intervention. **Problem:** Performance metrics are scattered across decks, models, and updates. Manual consolidation delays cross-portfolio comparison. Emerging risks and performance drift surface too late. **Parsewise Solution:** Extracts and standardizes KPIs across all portfolio communications. Detects performance shifts, inconsistencies, and early warning signals. Produces benchmark-ready tables and red flag summaries. **Inputs:** Board packs and quarterly reports, financial models and forecasts, market analyses and operating updates. **Outputs:** Standardized KPI datasets, portfolio-level comparison tables, early warning and variance reports. --- ## Solutions — Regulatory & Brokers ### Mortgage & Loan File Validation Source: https://www.parsewise.ai/mortgage-validation Shorten approval cycles and increase underwriting capacity. Standardize complex mortgage documentation into lender-ready formats to accelerate decisions without increasing headcount. **Problem:** Mortgage applications include fragmented tax, income, and asset documents. Manual data mapping slows underwriting and increases error risk. Specialist capacity limits application throughput and growth. **Parsewise Solution:** Extracts key financial data from tax returns, pay stubs, and asset statements. Maps applicant information directly into lender-specific underwriting templates. Flags missing, inconsistent, or high-risk financial indicators. **Inputs:** Personal and business tax returns, income statements and pay stubs, asset and liability statements. **Outputs:** Completed lender evaluation templates, standardized financial profiles for portfolio systems, red flag reports highlighting gaps and inconsistencies. ### LP Reporting & Data Validation Source: https://www.parsewise.ai/lp-reporting Deliver faster, more accurate LP reporting with less operational effort. Transform fragmented GP updates into standardized, validated reports that strengthen investor trust and reduce reporting risk. **Problem:** GP updates vary widely in structure, terminology, and detail. Manual KPI consolidation creates delays and reconciliation risk. Inconsistent reporting undermines LP confidence. **Parsewise Solution:** Extracts fund- and portfolio-level metrics across all GP materials. Standardizes and validates KPIs for consistency and comparability. Generates LP-ready reporting packages with built-in traceability. **Inputs:** Quarterly letters and updates, portfolio company summaries, fund financials and cap tables. **Outputs:** Standardized cross-fund performance tables, validated KPI datasets for internal systems, structured quarterly reporting packages for LP distribution. ### KYC Investigation Support Source: https://www.parsewise.ai/kyc-aml Increase compliance confidence and reduce investigative burden. Turn fragmented KYC and AML data packs into structured, validated profiles that strengthen regulatory defensibility and accelerate reviews. **Problem:** KYC investigations rely on large, heterogeneous document sets. Manual review is slow, inconsistent, and difficult to scale. Missed inconsistencies and red flags create regulatory exposure. **Parsewise Solution:** Extracts and standardizes identity, ownership, and financial data across documents. Reconciles information to detect inconsistencies and hidden risk signals. Produces audit-ready profiles with full traceability. **Inputs:** Identity documents and proof of address, corporate registries and beneficial ownership records, financial statements, transaction histories, sanctions and PEP reports. **Outputs:** Completed KYC/AML profiles aligned to regulatory standards, red flag and inconsistency reports, structured customer datasets for compliance systems. --- ## Solutions — Other ### SME Credit Underwriting Source: https://www.parsewise.ai/credit-underwriting Automate extraction, validation, and standardization of SME credit files to accelerate underwriting decisions and reduce manual effort. ### Company Data Room Parsing Source: https://www.parsewise.ai/company-data-room-parsing Parse complex data rooms in minutes, not weeks. Automate extraction and standardization to surface risks and enable faster, more confident outcomes. ### ESG Compliance Reporting Source: https://www.parsewise.ai/compliance-reporting Extract ESG and non-financial metrics from diverse reports. Standardize and validate metrics against compliance templates. Auto-complete reporting formats for faster submission and review. --- ## Supported File Types and Languages ### File Types Parsewise supports a wide range of document formats through a unified processing pipeline: - **PDF files** — Text-based and scanned PDFs, including complex layouts, multi-column flows, and forms - **Microsoft Word** — .docx, .doc - **Microsoft Excel** — .xlsx, .xls, .csv - **Microsoft PowerPoint** — .pptx, .ppt - **Images** — PNG, JPEG/JPG, GIF, BMP, TIFF - **Additional formats** — Enterprise customers can request support for additional file types All formats are handled by the same extraction pipeline, preserving structure, tables, figures, and reading order regardless of input format. ### Language Support Parsewise agents support over 70 languages for extraction and translation, including mixed-language documents. This includes: - All major European languages (English, German, French, Spanish, Italian, Portuguese, Dutch, etc.) - Asian languages (Chinese, Japanese, Korean, etc.) - Arabic, Hebrew, and other right-to-left scripts - Handwritten content, scanned documents, photos, rotated pages, and equations Agents can extract data in one language and produce structured outputs in another, supporting multilingual document packages common in cross-border transactions and regulatory filings. --- ## Feature Deep Dive ### Cross-Document Reasoning Unlike 1:1 extraction tools that process documents individually, Parsewise reasons across an entire corpus simultaneously. It links entities across documents, detects contradictions (e.g., a misstated EBITDA across three documents), and produces a unified, reconciled output. - **Why it matters:** Catches inconsistencies that single-document tools miss; produces a single source of truth from multi-document packages; reduces manual cross-referencing. ### Extraction Agents Users configure extraction agents with topics, dimensions, and natural-language instructions. Each agent defines what data to extract, how to validate it, and what inconsistencies to flag. Agents can be created via Navi (conversational) or the API (programmatic). - **Why it matters:** Reusable extraction logic that adapts to different document types; no templates or pre-defined schemas required; agents can be shared across projects. ### Source Attribution & Traceability Every extracted value is linked back to its source document, page, and word-level bounding box. Users can trace any data point to its origin for audit, compliance, or review purposes. - **Why it matters:** Defensible decisions with full provenance; audit-ready outputs; reduces review time by enabling direct source verification. ### Inconsistency Detection & Resolution When conflicting data is found across documents (e.g., different values for the same entity), Parsewise flags the inconsistency and provides resolution workflows with supporting evidence from each source. - **Why it matters:** Catches data quality issues automatically; provides structured evidence for resolution; reduces risk of decisions based on conflicting data. ### Intelligent Document Parsing Documents are parsed to extract text, tables, figures, and structure. The system handles complex layouts, merged cells, multi-column flows, handwritten content, and scanned documents. The parsing pipeline breaks document layouts into subsections and contextually processes each section based on content type. - **Why it matters:** Handles real-world document complexity; preserves structure for downstream extraction; supports mixed-format document packages. --- ## 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. **...Claude Code or other agentic tools?** Grepping through documents leads to false negatives, and deep agent-driven analysis is slow and expensive at corpus scale. Parsewise gives you deterministic, traceable, schema-shaped output instead of a chat transcript. **...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). --- ## Parsewise Labs Source: https://www.parsewise.ai/labs Parsewise Labs is a specialized applied AI research group building advanced abstraction layers that let experts command large-scale document intelligence with the precision of engineered systems. The team develops core data technologies such as the Parsewise Data Engine (PDE), translating complex real-world information into structured, traceable data world models. The mission is to push the frontier of enterprise-grade AI infrastructure by creating modular, governable components that organisations can rely on for high-stakes decision-making at scale. ### Blog Posts **Navi: A New Interface to Parsewise** (March 2026) Source: https://www.parsewise.ai/introducing-navi Navi is a conversational guide to the Parsewise Data Engine that makes the platform's power accessible to domain experts from day one. Instead of forcing users to learn the platform first, Navi helps them start with what they already know: the questions they want answered and the data they need from their documents. Users describe what they want to analyse, and Navi proposes, creates, and executes specialised extraction agents. The post illustrates three real customer workflows: (1) an insurance claims analyst who uploads hundreds of claims documents and asks Navi to flag the three financially or legally riskiest open claims — Navi creates agents for claim status, reserve amounts, litigation status, and risk indicators, then synthesises results into a structured table; (2) a private markets analyst who uploads a data room and asks for an investment-ready company profile — Navi extracts financial performance metrics, market analysis, competitive landscape, and customer unit economics, flagging inconsistencies across documents; (3) a mortgage broker who uploads application files and gets a structured financial summary of income, expenditures, and deposit sources in minutes. Behind the scenes, the Parsewise Data Engine processes every page and returns structured outputs with citations linking directly to the original document, enabling users to control how sources are combined by editing their agents. **Empowering Business Experts: UX Design for Agent-Driven Workflows** (March 2026) Source: https://www.parsewise.ai/ux-design-for-agent-workflows This post details how Parsewise redesigned its user experience to consolidate agent configuration, extraction progress, and consistency review into a single view. The key design principle is that the best infrastructure is invisible — domain experts should focus on their decisions, not on operating software. Changes include: unified table experience with sticky headers and consistent sort behavior across Projects, Documents, Agents, and Results; real-time status indicators showing extraction progress that transitions smoothly into consistency charts; inline agent editing without page navigation; a streamlined sidebar with three items (Documents, Agents, Results) matching the workflow sequence; and dual result views — an aggregate "Table" view for cross-agent analysis and a "By Agent" view for deep dives. The platform routes work across multiple LLM providers in real time, extracts entities in parallel across thousands of pages, and resolves and deduplicates results into structured, auditable data. **Building Document Processing In-House: What It Takes to Build and Operate** (January 2026) Source: https://www.parsewise.ai/doc-processing-pipelines A comprehensive guide for engineering teams evaluating whether to build or buy document processing infrastructure. The post maps out the full pipeline: exploration, ingestion, cleaning, extraction target configuration, extraction, storage, resolving and structuring data, validation, export, and continuous improvement. Key technical challenges include: supporting diverse file types with different representations; building LLM infrastructure with retries, fallbacks, rate limits, provider differences, and model lifecycle management; and the limitations of RAG for high-stakes extraction (semantic similarity is inherently non-deterministic). The post highlights that the hardest challenges are on the business side — defining extraction targets, resolving multi-document results (handling missing values, duplicates, and inconsistencies), and keeping business rules in sync with IT over time. Maintenance costs include infrastructure, product support, LLM engineering, and ongoing business rule configuration. The post concludes that these challenges are why Parsewise exists: to handle the end-to-end process and empower business users to do refinement work independently. **The Core Loop: Why LLMs Haven't Revolutionized Decision-Making (Yet)** (January 2026) Source: https://www.parsewise.ai/core-loop This post introduces the "core loop" of knowledge-work decision-making: read, recombine, write. While LLMs mirror this loop at the token level, four challenges prevent naive applications from transforming enterprise decisions: scale (processing >10,000 pages per decision), reliability (minimising false negatives), workflow coordination (orchestrating document routing, extraction validation, cross-referencing, retries, and lineage), and expert control (integrating domain know-how without requiring engineering). The post argues that chat-based RAG systems fail on scale and reliability, while API-first tools solve per-document extraction but leave workflow coordination and expert control to the customer. The critical distinction is between lower-level document processing (invoices, standardised forms — largely solved by APIs) and higher-level processing (evaluating data rooms, mapping mortgage applications, converting complex loan files — requiring human expertise and cross-document reasoning). The post draws an analogy to cloud computing: just as AWS abstracted away infrastructure while preserving human control, the next abstraction layer must orchestrate LLM-based document intelligence while keeping business experts in control. **Software 3.0: What Is Needed for the LLM Operating System** (December 2025) Source: https://www.parsewise.ai/software-3.0 Building on Andrej Karpathy's concept of Software 3.0, this post examines what is needed for LLMs to become the next computational layer of abstraction, following the progression from assembly to low-level languages to service abstractions (AWS, Vercel). The post argues that the key insight from platforms like Palantir is enabling business experts to modify structured data transformations directly, without going through IT — and that this is now becoming possible for unstructured data transformations. The post quantifies the gap between LLM compute and traditional compute: speed (~500 requests/second vs. trillions of operations/second), cost (orders of magnitude more expensive), and integration (fragile state management vs. mature infrastructure). Current automation tools like Zapier and N8N assume sequential, single-threaded workflows, while large-scale parallelised workflows remain trapped in custom enterprise solutions with strong IT dependencies. Parsewise Labs is building toward this vision by supporting unstructured data workflows that require ultimate reliability, powered by the Parsewise Data Engine that abstracts away peripheral complexity and is programmable by business users. --- ## Pricing Source: https://www.parsewise.ai/pricing ### Free — $0 - Up to 25 chat messages - Up to 50 document pages - 10 extraction agents - 1 user - PDF, Word, PowerPoint, Excel, Images - Excel export - Email & Slack support - Encryption (at rest & in transit), no training on customer data, standard DPA ### Enterprise — Custom pricing - Unlimited chat messages, document pages, agents, and users - Additional file types - Custom export formats & templates - Email, Slack & Phone support - Encryption (at rest & in transit), no training on customer data, standard DPA - VPC & on-prem deployments - Regional data residency - SSO & SAML authentication - API access - Auto-ingestion (SharePoint, Google Drive & more) - DB connectors (PostgreSQL, JDBC & more) - Custom ingestion & export integrations - SLA & white-glove onboarding - Custom decision modules - Model fine-tuning ### FAQ **How is pricing calculated?** Based on chat messages, document volume, number of agents, and number of users. **What counts as a document page?** Single pages in a PDF, Word, or PPT file. Other file types are transformed into page equivalents. **What are extraction agents?** An agent works like a human data analyst at scale. It reads every document, extracts relevant facts, runs multi-step analyses, and builds its own working memory. You can generate, modify, and reuse agents. **Can we upgrade or downgrade?** Yes. For Enterprise, contact sales@parsewise.ai. **Is our data used to train models?** No. Customer data is not used to train models. **What security and compliance standards?** GDPR and SOC 2 Type II compliant. Visit trust.parsewise.ai for policies and certificates. Custom deployments available for enterprise customers with strict data privacy requirements. **Can Parsewise parse handwriting or non-English languages?** Yes. Supports scanned documents, photos, handwritten notes, rotated documents, and equations. Agents can extract and translate across over 70 languages, including mixed-language documents. **What file types are supported?** PDF, DOCX, DOC, PPTX, PPT, XLSX, XLS, CSV, PNG, JPEG/JPG, GIF, BMP, TIFF, and more. Enterprise customers can request additional format support. --- ## Security & Compliance Source: https://trust.parsewise.ai Parsewise is built for regulated industries where data security and auditability are non-negotiable. - **Certifications:** SOC 2 Type II and 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 - **Standard DPA:** Available for all customers - **Enterprise security options:** - VPC and on-premises deployments for customers requiring data to remain within their own infrastructure - Regional data residency (EU, US, and other regions on request) - SSO and SAML authentication - Custom DPAs and SLAs - Audit trails and versioning across all projects and extractions Full policies, certificates, and compliance documentation are available at the Trust Center: https://trust.parsewise.ai --- ## About Source: https://www.parsewise.ai/about-us ### 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. ### Values - **Trust Through Transparency** — Trust is earned through clarity. Operate openly with customers and design systems that make decisions explainable and defensible. - **Product Over Hype** — Substance over demos. Solving real operational problems, not showcasing technology for its own sake. - **Enterprise Discipline** — Understanding the constraints of regulated industries: security, governance, and auditability at every step. - **Built for Subject Matter Experts** — Domain expertise matters. Augment specialists, not replace them, and help teams make better decisions at scale. ### Team - **Maximilian Hofer** — Co-founder & CEO (https://www.linkedin.com/in/mwhofer/) - **Greg Csegzi** — Co-founder & CTO (https://www.linkedin.com/in/gergely-csegzi/) - **Nikola Bozhinov** — Founding Engineer (https://www.linkedin.com/in/nikola-bozhinov-035b1b69/) - **Shan Singh** — Founding Engineer (https://www.linkedin.com/in/shantnus/) - **Mark Menezes** — Founding Engineer (https://www.linkedin.com/in/markmenezes11/) ### Customers and Industries Parsewise serves customers across insurance and reinsurance, asset management and private equity, mortgage lending, regulatory compliance, and brokerage. Customers are based in the United States, United Kingdom, Switzerland, Germany, and Spain, and include organizations such as OneIM, Compre Group, and Hypohaus. --- ## Getting Started ### How to Get Started 1. **Free tier:** Sign up at [parsewise.ai/get-started](https://www.parsewise.ai/get-started) for immediate access. Upload documents and start querying with Navi in minutes. 2. **Enterprise:** Contact [sales@parsewise.ai](mailto:sales@parsewise.ai) to discuss custom pricing, deployment options (cloud, VPC, on-premises), integrations, and onboarding. A Parsewise representative will help scope your use case and configure the platform for your workflows. 3. **Demo:** Request a live demo via the website to see the platform in action with your own documents. ### What to Expect During Onboarding Enterprise customers receive white-glove onboarding, including: use-case scoping, document package configuration, agent design and tuning, integration setup (API, auto-ingestion, DB connectors), and ongoing support via dedicated Slack channels, email, and phone. ### Contact Information - **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) - **Security:** [security@parsewise.ai](mailto:security@parsewise.ai) - **LinkedIn:** [linkedin.com/company/parsewise](https://www.linkedin.com/company/parsewise/) - **X / Twitter:** [x.com/parsewise](https://x.com/parsewise)