When a single extraction error can cost millions in failed deals, compliance violations, or denied claims, you need more than AI outputs. You need proof.
Your deal team is reviewing 2,000+ documents in a virtual data room. Financial statements, contracts, IP filings, employment agreements. You have 3 weeks to surface every material risk before the LOI expires.
The hard part isn't extracting a value. It's proving it's correct, reconciling it across documents, and surfacing mismatches before they become surprises.
Upload hundreds of financial statements, contracts, and filings. Extract revenue, EBITDA, customer counts, contract terms, and IP assignments across all documents simultaneously.
When you extract "Annual Revenue: $12.4M" from the management presentation, instantly verify it matches the audited financials. Every discrepancy is flagged with citations to both sources.
Generate diligence reports where every finding links directly to source documents. When partners ask "where did this number come from?", the answer is one click away.
{
"schema": {
"revenue_ttm": { "type": "number" },
"gross_margin": { "type": "number" },
"customer_count": { "type": "number" },
"largest_customer_concentration": { "type": "number" },
"recurring_revenue_pct": { "type": "number" }
}
}{
"data": {
"revenue_ttm": 12400000,
"gross_margin": 0.72,
"customer_count": 847,
"largest_customer_concentration": 0.18,
"recurring_revenue_pct": 0.89
},
"citations": {
"revenue_ttm": {
"page": 23,
"bbox": [120, 340, 480, 365],
"snippet": "Total revenue for the twelve months ending...",
"confidence": 0.96
},
"largest_customer_concentration": {
"page": 31,
"bbox": [85, 215, 420, 240],
"snippet": "No single customer exceeds 18% of revenue...",
"confidence": 0.94
}
}
}Great for reconciling decks vs. audited financials and flagging mismatches with citations.
See how CiteLLM can cut your diligence timeline from weeks to days while improving accuracy.
A borrower submits 6 months of bank statements, 2 years of tax returns, and a P&L. Your underwriters need to extract income, verify it against deposits, calculate debt-to-income ratios, and document every step for regulators.
Without a clean trail back to the source, reviews and exams become slow, manual, and risky.
Extract monthly deposits, recurring income patterns, NSF occurrences, and average balances. Every figure traced back to the exact transaction line in the source statement.
Pull AGI, W-2 income, Schedule C revenue, K-1 distributions from 1040s. Cross-reference against bank deposits to verify income claims. Discrepancies surface automatically.
When regulators audit your loan decisions, show them exactly where each qualifying income figure came from. "Page 3, Line 7 of the 2023 1040" isn't just a claim. It's a clickable citation.
{
"schema": {
"filing_status": { "type": "string" },
"wages_w2": { "type": "number" },
"business_income": { "type": "number" },
"rental_income": { "type": "number" },
"adjusted_gross_income": { "type": "number" },
"tax_year": { "type": "string" }
}
}{
"data": {
"filing_status": "Married Filing Jointly",
"wages_w2": 142000,
"business_income": 34200,
"rental_income": 18600,
"adjusted_gross_income": 189400,
"tax_year": "2023"
},
"citations": {
"wages_w2": {
"page": 1,
"bbox": [400, 285, 520, 300],
"snippet": "Wages, salaries, tips... 142,000",
"confidence": 0.99
},
"adjusted_gross_income": {
"page": 1,
"bbox": [400, 520, 520, 535],
"snippet": "Adjusted gross income... 189,400",
"confidence": 0.99
}
}
}Reviewers can click AGI/W-2 wages and jump straight to the exact line on the return.
Integrate CiteLLM to automate income verification while maintaining full audit trails.
Your legal team manages 5,000 vendor contracts. Somewhere in those documents are auto-renewal clauses, liability caps, termination windows and data processing terms. You need to know what they say and you need to know exactly where they say it.
Whether you're managing renewals, negotiating deviations, or responding to disputes, you need clause-level proof, not summaries.
Extract termination terms, payment schedules, liability limits, and IP ownership from any contract format. Each extracted term includes the exact section number and page location.
Build a database of contractual obligations with verifiable sources. When renewal dates approach, you know exactly which clause governs the timeline and can prove it.
Compare executed contracts against your standard templates. Identify non-standard terms and trace each deviation to specific redlined language.
{
"schema": {
"effective_date": { "type": "date" },
"term_length_months": { "type": "number" },
"auto_renewal": { "type": "boolean" },
"termination_notice_days": { "type": "number" },
"liability_cap": { "type": "string" },
"governing_law": { "type": "string" }
}
}{
"data": {
"effective_date": "2024-01-15",
"term_length_months": 36,
"auto_renewal": true,
"termination_notice_days": 90,
"liability_cap": "12 months fees",
"governing_law": "Delaware"
},
"citations": {
"termination_notice_days": {
"page": 8,
"bbox": [72, 445, 510, 470],
"snippet": "...ninety (90) days prior written notice...",
"confidence": 0.97
},
"liability_cap": {
"page": 12,
"bbox": [72, 320, 490, 345],
"snippet": "...not exceed fees paid in preceding 12...",
"confidence": 0.95
}
}
}Built for clause-level verification, especially liability, renewals, and termination language.
Extract and track obligations across thousands of agreements with citation-backed accuracy.
A claimant submits medical records, police reports, repair estimates and policy documents. Your adjusters need to extract diagnosis codes, treatment costs, coverage limits and deductibles. Then make a determination that can withstand appeals and litigation.
Speed matters, but so does explainability, especially when decisions are appealed, audited, or litigated.
Pull diagnosis codes, treatment dates, provider information, and itemized charges from clinical documentation. Every extracted data point linked to the source record.
Extract coverage terms, exclusions, deductibles, and limits from policy documents. Cross-reference claims against specific policy language to justify coverage decisions.
When claims are disputed, produce decision documentation that cites exact policy sections and medical record references. Turn "we followed our guidelines" into verifiable proof.
{
"schema": {
"patient_name": { "type": "string" },
"date_of_service": { "type": "date" },
"primary_diagnosis": { "type": "string" },
"diagnosis_code": { "type": "string" },
"total_charges": { "type": "number" },
"provider_name": { "type": "string" }
}
}{
"data": {
"patient_name": "John Smith",
"date_of_service": "2024-03-15",
"primary_diagnosis": "Lumbar disc herniation",
"diagnosis_code": "M51.16",
"total_charges": 12450,
"provider_name": "Metro Spine Center"
},
"citations": {
"diagnosis_code": {
"page": 2,
"bbox": [145, 180, 420, 195],
"snippet": "Primary DX: M51.16 - Intervertebral disc...",
"confidence": 0.98
},
"total_charges": {
"page": 5,
"bbox": [300, 485, 510, 500],
"snippet": "Total Patient Responsibility: $12,450.00",
"confidence": 0.96
}
}
}Useful for appeals and audits: every extracted fact points back to the record.
Process claims faster while building bulletproof documentation for every decision.
Your analysts are reading through 200-page 10-Ks, quarterly filings and proxy statements. They need to extract revenue guidance, risk factors, executive compensation and material changes. Then cite exact page and section references in their research reports.
Filings are long, dense, and high-stakes. Analysts and compliance teams need key metrics, disclosures, and precise references for research notes, investment memos, and client reporting.
Extract revenue figures, segment breakdowns, risk factors, and management guidance from annual and quarterly filings. Every data point linked to exact page, section, and exhibit references.
Don't miss critical disclosures buried in financial statement footnotes. Extract lease obligations, contingent liabilities, and accounting policy changes with precise citations.
Generate analyst reports where every claim links back to source filings. Build investor presentations with verifiable citations that withstand scrutiny.
{
"schema": {
"total_revenue": { "type": "number" },
"revenue_growth_yoy": { "type": "number" },
"primary_risk_factors": { "type": "array", "items": { "type": "string" } },
"forward_guidance": { "type": "string" },
"ceo_compensation": { "type": "number" },
"material_weaknesses": { "type": "boolean" }
}
}{
"data": {
"total_revenue": 4820000000,
"revenue_growth_yoy": 0.124,
"primary_risk_factors": ["Supply chain", "Regulatory"],
"forward_guidance": "$5.1-5.3B expected",
"ceo_compensation": 12400000,
"material_weaknesses": false
},
"citations": {
"total_revenue": {
"page": 42,
"bbox": [72, 380, 450, 405],
"snippet": "Total revenues were $4.82 billion...",
"confidence": 0.98
},
"forward_guidance": {
"page": 28,
"bbox": [72, 290, 520, 315],
"snippet": "We expect fiscal 2025 revenues...",
"confidence": 0.94
}
}
}Great for research notes where every number and statement must be source-linked.
Extract insights from SEC filings with citation-backed accuracy for compliant research.
Different industries, same requirement: if a model drives a workflow, humans need a fast way to verify and defend outputs.
Click any extracted value to jump to the source text. No more manual searching through documents.
Every field comes with evidence you can inspect. No more "the AI said so" explanations.
Maintain traceability across documents, reviewers, and decisions. Built for regulated industries.
If your workflow depends on PDFs, citations turn extraction into something reviewers and auditors can trust.