Parsewise vs Eigen Technologies

Eigen Technologies is an intelligent document processing (IDP) platform, acquired by Sirion in 2024, that trains extraction models on as few as 2 to 50 sample documents. It serves insurance, banking, legal, and procurement teams with no-code model building, table extraction, clause comparison, and LLM-powered summaries. Customers include Goldman Sachs, ING, Bank of America, and Deloitte.

Parsewise is a decision platform that ingests entire document packages (submissions, data rooms, claims files) and reasons across thousands of pages simultaneously. It produces structured, reconciled outputs with full source attribution, cross-document entity linking, and contradiction detection. Customers include OneIM, Compre Group, and Hypohaus.

Both platforms target insurance and legal document workflows. The difference is in scope: Eigen extracts data from individual documents; Parsewise reasons across the corpus to produce decision-ready outputs.

Methodology

Feature claims are based on publicly available vendor documentation as of April 2026. Eigen capabilities reflect the Eigen 6 platform as described on eigentech.com and sirion.ai. Parsewise capabilities are drawn from the current platform. We update this page periodically; check the last_modified_date date for freshness.

Capability Comparison

Capability Eigen Technologies Parsewise
Per-document extraction Strong. Text, table, and clause extraction with computer vision and NLP Strong. Handles PDF, Word, Excel, PowerPoint, images, scans
Cross-document reasoning Not supported. Each document processed independently Native. Entity linking, contradiction detection, and reconciliation across the full corpus
Model training requirement 2-50 sample documents per extraction model No training required. Agents configured with natural-language instructions
Corpus scale Per-document processing; global search across projects 25,000+ pages per run with exhaustive processing
Conversational interface Not available. Configuration via no-code UI Navi: conversational agent creation and querying in plain English
Source attribution Confidence scores with review hub Word-level bounding boxes with page and paragraph references
Inconsistency detection Not a core feature Native. Flags conflicting data across documents with resolution workflows
LLM integration GPT 3.5, Llama 2, BERT for summaries Multi-provider LLM orchestration with real-time routing and KPI-specific fine-tuning
Clause comparison Yes. Identifies changed, non-standard, or missing clauses Handled via cross-document reasoning and extraction agents
API access Yes, with SDK for custom solutions RESTful API with OpenAPI spec, webhooks, and schema-based extraction
No-code access Yes. Business users build and manage models Yes. Domain experts configure agents via Navi or the UI
Deployment options Cloud and on-premises Cloud, VPC, and on-premises with regional data residency
Security certifications Enterprise-grade (details via vendor) SOC 2 Type II, GDPR, TLS 1.2+, AES-256, no training on customer data
Language support Multi-language (details via vendor) 70+ languages, including mixed-language documents and cross-language extraction

Key Differentiators

Per-document extraction vs corpus-level reasoning

Eigen’s core strength is training high-accuracy extraction models with minimal sample data. A user provides 2 to 50 example documents, Eigen learns the extraction pattern, and it applies that pattern to new documents of the same type. This works well for repetitive, single-document-type workflows: extracting fields from insurance slips, pulling clauses from contracts, or digitizing structured forms.

Parsewise operates at a different level of abstraction. Instead of extracting data from one document at a time, it ingests an entire document package and reasons across all documents simultaneously. When an underwriter uploads a submission containing an application, schedules of values, loss runs, and financial statements, Parsewise links entities across those documents, detects contradictions (such as a coverage limit stated differently in the application and the schedule), and produces a unified, reconciled output. This cross-document reasoning capability is the core architectural difference. For more on why retrieval-based approaches fall short of this standard, see Why RAG Fails for Risk-Grade Decisions.

Training requirements and time to value

Eigen requires upfront model training. Even at 2 to 50 documents, someone must curate training samples, label extraction targets, validate model accuracy, and retrain when document formats change. This creates a setup cost per document type and an ongoing maintenance burden.

Parsewise uses extraction agents configured with natural-language instructions, topics, and dimensions. There is no training step. A domain expert describes what they need (via Navi or the API), and the agent runs immediately across the full document set. Agents are reusable, versionable, and sharable across projects. When requirements change, the expert updates the agent definition in plain English rather than retraining a model.

Conversational access and agent creation

Eigen’s no-code UI lowers the barrier to model building, but users still interact through a traditional project-and-model interface. There is no conversational layer.

Parsewise offers Navi, a conversational workspace where domain experts describe their analysis goals in plain English. Navi proposes, creates, and executes specialized extraction agents, then returns structured results with full source citations. This bridges the gap between what a business expert knows they need and the technical configuration required to extract it. For a deeper look at how this works, see From Navi to API: When Conversational AI Meets Programmatic Extraction.

When to Choose Eigen Technologies

Eigen is a strong fit when:

  • Your workflow centers on single-document-type extraction (e.g., extracting the same 20 fields from thousands of insurance slips)
  • You need clause comparison for contract review and negotiation
  • You already have a Sirion contract intelligence deployment and want integrated document AI
  • Your volume is moderate and document types are stable enough to justify upfront model training
  • You are primarily digitizing documents rather than making cross-document risk decisions

When to Choose Parsewise

Parsewise is a stronger fit when:

  • Your decisions depend on cross-referencing multiple documents (submissions, data rooms, claims files, portfolio packages)
  • You need to process 25,000+ pages per run with exhaustive coverage and no false negatives
  • You want no training step: agents configured in plain English and ready to run immediately
  • You need native inconsistency detection that flags conflicting data across sources with full evidence
  • Your team includes domain experts who need a conversational interface (Navi) rather than a model-building UI
  • You require SOC 2 Type II and GDPR compliance with options for VPC or on-premises deployment

Verdict

Eigen Technologies and Parsewise serve overlapping industries but solve different problems. Eigen is an intelligent document processing platform optimized for high-accuracy, per-document extraction with minimal training data. It excels at digitizing individual document types and comparing clauses within contracts.

Parsewise is a decision platform built for the layer above extraction. It processes entire document packages, reasons across the corpus, and produces structured, reconciled outputs that support risk-grade decisions. If your workflow requires cross-document entity linking, contradiction detection, and corpus-level processing at scale, Parsewise is purpose-built for that problem.

For teams that already use a per-document extraction tool (Eigen or otherwise), Parsewise is complementary: it is the reasoning and reconciliation layer that sits on top. For more on this distinction, see Decision Platform vs Document Extraction: What to Buy in 2026.

Frequently Asked Questions

Can Parsewise replace Eigen Technologies entirely?

It depends on the workflow. For single-document-type extraction tasks (e.g., pulling specific fields from thousands of identical forms), Eigen’s trained models may be the more efficient tool. For workflows that require reasoning across multiple documents, detecting inconsistencies, and producing unified outputs, Parsewise handles both the extraction and the cross-document reasoning in a single platform. Many teams use a per-document extraction tool alongside Parsewise, with Parsewise serving as the reconciliation and decision layer.

Does Parsewise require model training like Eigen?

No. Parsewise extraction agents are configured with natural-language instructions. There is no training step, no labeled samples, and no retraining when document formats change. Domain experts describe what they need, and the agent runs across the full document set immediately.

How does pricing compare between the two platforms?

Eigen pricing is typically based on document volume and deployment scope; contact Sirion for specifics. Parsewise offers a free tier (25 chat messages, 50 pages, 10 agents) and custom Enterprise pricing based on chat messages, document volume, agents, and users. See parsewise.ai/pricing for current plans.

Which platform handles insurance submissions better?

For extracting fields from individual broker emails and submission documents, Eigen offers pre-trained insurance models. For processing a full submission package (application, SOVs, loss runs, financials) and cross-referencing data across all documents to produce a unified risk profile, Parsewise is the stronger fit. The choice depends on whether the workflow is per-document extraction or cross-document risk assessment.

Is Eigen still available as a standalone product after the Sirion acquisition?

As of April 2026, Eigen continues to operate its platform (eigentech.com) and serves existing customers. The Sirion acquisition positions Eigen’s document AI as a component of Sirion’s broader contract intelligence platform. Prospective buyers should confirm current availability and roadmap with Sirion directly.

Ready to see Parsewise in action? Request a demo or contact sales to discuss your use case.

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