For Developers

🤖

For LLMs

The API for
multi-document processing

Turn documents into a single structured response.
Cross-document entity linking, contradiction detection, bounding boxes and UI toolkit to show it all included.

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Who This Is For

Level up your pipeline, agent, or manual process

How Parsewise Compares

Single-doc parsers and RAG solve are limited in their scope.
Parsewise goes beyond: cross-document reasoning with full traceability and no false negatives.

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Why Parsewise

Exhaustive. Not approximate.

Scale Without Limits

Process 10,000+ pages per run. Parsewise maintains context across your entire corpus. No missed details.

Full Traceability

Every answer cites its source with page and paragraph references. Audit any insight with a click. No black boxes.

PDF

PDF

XLSX

DOC

DOC

DOC

Any File Type

PDFs, spreadsheets, Word docs, scanned images handled consistently across heterogeneous document sets.

Cross-Document Entity Linking

"John Smith, borrower" in Doc A is the same entity as "J. Smith, DOB 1990" in Doc C. Parsewise resolves and links them natively into one unified ontology.

Loan Amount

Number

Interest Rate

%

Risk Flag

Boolean

Maxim

Contradiction Detection

When sources disagree, you see the conflict, the candidates, and the chosen value, not a confident-sounding hallucination. Specify your definition or manually override.

Scale Without Limits

Scale Without Limits

Process 10,000+ pages per run. Parsewise maintains context across your entire corpus. No missed details.

Process 10,000+ pages per run. Parsewise maintains context across your entire corpus. No missed details.

DOC

Full Traceability

Full Traceability

Every answer cites its source with page and paragraph references. Audit any insight with a click. No black boxes.

Every answer cites its source with page and paragraph references. Audit any insight with a click. No black boxes.

PDF

PDF

XLSX

DOC

DOC

DOC

Any File Type

Any File Type

PDFs, spreadsheets, Word docs, scanned images handled consistently across heterogeneous document sets.

PDFs, spreadsheets, Word docs, scanned images handled consistently across heterogeneous document sets.

Greg

Cross-Document Entity Linking

"John Smith, borrower" in Doc A is the same entity as "J. Smith, DOB 1990" in Doc C. Parsewise resolves and links them natively into one unified ontology.

Loan Amount

Number

Interest Rate

%

Risk Flag

Boolean

Maxim

Contradiction Detection

When sources disagree, you see the conflict, the candidates, and the chosen value, not a confident-sounding hallucination. Specify your definition or manually override.

Loan Amount

Number

Interest Rate

%

Risk Flag

Boolean

Maxim

How Builders Use Parsewise

INSURANCE & REINSURANCE

INSURANCE & REINSURANCE

Live with

Submission Triage at Scale

Submission Triage at Scale

Underwriting teams pipe broker submissions into Parsewise to extract exposure, loss runs, and schedules, turning 100-page dossiers into structured risk records before a human ever opens the file.

Underwriting teams pipe broker submissions into Parsewise to extract exposure, loss runs, and schedules, turning 100-page dossiers into structured risk records before a human ever opens the file.

ASSET MANAGEMENT & PE

ASSET MANAGEMENT & PE

Live with

Data Room Diligence

Data Room Diligence

Diligence teams stream entire data rooms (50– 500 docs) through the API to validate KPIs, surface red flags, and reconcile contradictory disclosures, all returned as JSON ready for the IC memo.

Diligence teams stream entire data rooms (50– 500 docs) through the API to validate KPIs, surface red flags, and reconcile contradictory disclosures, all returned as JSON ready for the IC memo.

MORTGAGE & LENDING

MORTGAGE & LENDING

Live with

Loan File Validation

Loan File Validation

Lenders post complete loan packages (applications, W-2s, bank statements, appraisals) and get back DTI, LTV, income verification, and a list of missing documents, with page-level citations for every value.

Lenders post complete loan packages (applications, W-2s, bank statements, appraisals) and get back DTI, LTV, income verification, and a list of missing documents, with page-level citations for every value.

Beyond Structured JSON

The API is just the start.
Parsewise provides the full toolkit to configure, consume, verify, and iterate.

Flexible output formats

Get results as JSON, CSV, or Excel. Fill DOCX, PDF, and XLSX templates deterministically from extracted data.

Flexible output formats

Get results as JSON, CSV, or Excel. Fill DOCX, PDF, and XLSX templates deterministically from extracted data.

Out-of-the-box prompts

Start with built-in extraction definitions. Parsewise suggests ongoing improvements as it processes more of your data.

Out-of-the-box prompts

Start with built-in extraction definitions. Parsewise suggests ongoing improvements as it processes more of your data.

Ad-hoc corpus queries

Run follow-up questions on an already-processed document corpus without re-ingesting or re-extracting.

Ad-hoc corpus queries

Run follow-up questions on an already-processed document corpus without re-ingesting or re-extracting.

Coding tools & web search

Agents can write and run code, and search the web to backfill missing values and verify extracted data.

Coding tools & web search

Agents can write and run code, and search the web to backfill missing values and verify extracted data.

Bounding-box endpoints

API endpoints return word-level coordinates so you can build your own UI with highlighted source regions.

Bounding-box endpoints

API endpoints return word-level coordinates so you can build your own UI with highlighted source regions.

Web app for business users

Non-technical team members can configure schemas, review results, and analyse further, all from the browser.

Web app for business users

Non-technical team members can configure schemas, review results, and analyse further, all from the browser.

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Enterprise-grade security

Built from the ground up with your data protection top of mind.
Review our certifications and policies in the Trust Center.

Built from the ground up with your data protection top of mind. Review our certifications and policies in the Trust Center.

Encrypted in transit and at rest

No training on customer data

FAQ

Why not Textract / Reducto / Azure Doc Intelligence + Claude Code?

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.

Why not just use 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.

Why not 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.

Why not 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.

Why not build it ourselves?

Same reason you're not building Excel. Unless multi-document resolution is your core product, you want to ship into your niche, not vibe-code and vibe-debug a bespoke pipeline that breaks every time business rules change. We wrote a full guide on what it takes to build and operate a document processing pipeline in-house.

Move from reactive large-loss management to proactive severity control.

The future of risk decisions, today. Submit your email and we'll reach out.