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.
Traditional approach: Junior analysts manually extract key figures, cross-reference across documents, and build spreadsheets. It takes 200+ hours. And still, post-close, you find the revenue number on page 47 was misread.
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,
"snippet": "Total revenue for the twelve months ending...",
"confidence": 0.96
},
"largest_customer_concentration": {
"page": 31,
"snippet": "No single customer exceeds 18% of revenue...",
"confidence": 0.94
}
}
}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.
One transposed digit in the income field. One missed liability. That's a bad loan on your books or worse, a fair lending violation when the examiner asks why this applicant was approved and another wasn't.
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, "line": "1a",
"snippet": "Wages, salaries, tips... 142,000",
"confidence": 0.99
},
"adjusted_gross_income": {
"page": 1, "line": "11",
"snippet": "Adjusted gross income... 189,400",
"confidence": 0.99
}
}
}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.
When a vendor claims you missed the 90-day termination notice, you can't afford to be wrong about what the contract actually says. And when you're negotiating renewals, you need to cite specific clause numbers, not "somewhere in Section 8."
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, "section": "7.2",
"snippet": "...ninety (90) days prior written notice...",
"confidence": 0.97
},
"liability_cap": {
"page": 12, "section": "9.4",
"snippet": "...not exceed fees paid in preceding 12...",
"confidence": 0.95
}
}
}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.
Process too slowly, and customer satisfaction tanks. Process too quickly without proper documentation, and you're exposed to bad faith claims. Every decision needs to be defensible with specific policy language and medical documentation.
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,
"snippet": "Primary DX: M51.16 - Intervertebral disc...",
"confidence": 0.98
},
"total_charges": {
"page": 5,
"snippet": "Total Patient Responsibility: $12,450.00",
"confidence": 0.96
}
}
}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.
Miss a disclosure buried in footnote 17? Misquote a risk factor? That's a compliance violation waiting to happen. And when regulators or clients ask "where did you get that number?", pointing to "somewhere in the 10-K" doesn't cut it.
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" },
"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, "section": "Item 8",
"snippet": "Total revenues were $4.82 billion...",
"confidence": 0.98
},
"forward_guidance": {
"page": 28, "section": "Item 7",
"snippet": "We expect fiscal 2025 revenues...",
"confidence": 0.94
}
}
}Extract insights from SEC filings with citation-backed accuracy for compliant research.
Regardless of your industry, the fundamentals are the same.
What took 10+ minutes of manual cross-referencing now takes seconds with click-to-verify citations.
Every extracted value comes with source proof. No more "the AI said so" explanations.
Full audit trails show who verified what, when. Built for regulated industries from day one.
See how CiteLLM can eliminate the trust gap in your AI-powered document processing.