Where OCR Falls Apart on Real COI Documents

Traditional OCR technology for insurance verification struggles with the complexity of real Certificate of Insurance documents. Here’s where OCR fails—told the way real auditors experience it.

1. Multi-Page Document Continuation

Anyone who has reviewed a COI has seen this happen: the Description of Operations fills the box on the ACORD 25, then quietly continues onto an ACORD 101. A human naturally reads both pages together and understands which entities receive Additional Insured status and under what conditions. OCR, on the other hand, treats them like two strangers at a bus stop—related, but refusing to acknowledge each other.

2. Compressed Multi-Policy Tables

Brokers often squeeze multiple coverages (Professional, Pollution, Crime, Cyber) into one tight row with separate policy numbers, dates, and limits. To a human, it’s obvious there are four distinct policies sitting together inside the same box. To OCR, it looks like a single noisy paragraph. It merges fields, misassigns limits, or drops coverages entirely.

3. Additional Insured Entity Name Variations

Picture a vendor COI that lists: “Acme Construction LLC dba Acme Interiors” with an address beneath it. Any trained insurance auditor immediately recognizes this is not the same as “Acme Construction Inc.”—and that the difference can determine whether coverage applies in a claim. OCR just sees a long line of characters and misses the nuance that separates a compliant submission from a risky one.

4. Endorsement Interpretation

Endorsements are an entirely different world. They include nuanced legal wording about Ongoing vs. Completed Operations, Primary & Non-Contributory requirements, Waiver of Subrogation, Endorsement Schedules, and Exclusions buried deep in policy forms. A human reads that language and understands how it changes coverage in real scenarios. OCR simply pulls the text and stops there—it has no way to interpret what any of it means for compliance.

Bottom Line: OCR extracts text. AI-powered Certificate of Insurance verification interprets meaning—the same way a trained compliance auditor does.

How AI-Powered COI Tracking Works: Beyond “Better OCR”

Anyone who has spent time inside a COI inbox knows the real challenge isn’t pulling the data—it’s understanding what it means in context. AI-Powered Certificate of Insurance tracking software doesn’t just read text—it understands insurance documents contextually and visually.

OCR vs. AI: Technical Comparison

Capability ❌ Traditional OCR ✅ AI-Powered Verification
Text Extraction Accurate on clean documents Accurate even on complex layouts
Multi-Page Context Treats pages separately Maintains context across ACORD 25 + ACORD 101
Table Recognition Flattens to text, loses structure Preserves rows, columns, visual relationships
Entity Matching Character-level only Understands entity variations (LLC vs. Inc.)
Endorsement Interpretation Cannot understand meaning Interprets insurance terminology
Compliance Decision None—returns data only Makes verification decisions
Fraud Detection No capability Metadata and pattern analysis
Learning Over Time Static Improves with account-specific rules
Feels like 🫣 Reading a document with your eyes half-closed 🤓 Having an insurance specialist reading it with you

Real AI Capabilities in COI Verification

1. Automatic Document Organization
AI agents automatically organize large PDFs into a clean Table of Contents—identifying ACORD 25 pages, ACORD 101, and every endorsement.

Result: Users can instantly navigate a 200-page upload without manual sorting.

2. Endorsement Type Recognition
AI recognizes endorsement types and understands their implications:

  • Whether a form satisfies Additional Insured requirements
  • Whether Completed Operations are included
  • Whether Waiver of Subrogation applies
  • Whether Primary & Non-Contributory provisions exist

Result: Instead of returning codes, AI interprets their purpose in your compliance context.

3. Visual Table Understanding
AI sees table structure the way a trained insurance auditor does—preserving alignment even in compressed layouts with multiple coverages per row.

Result: Correct assignment of policy numbers, dates, and limits to specific coverage types.

4. Relationship Understanding
AI connects related information across:

  • Description of Operations → Additional Insured entities
  • Endorsements → Coverage modifications
  • Policy forms → Exclusions and limitations

Result: Comprehensive compliance assessment, not just data extraction.

Why “AI OCR” Is Different from Traditional OCR

While traditional OCR passively converts text, AI-powered Optical Character Recognition actively engages with content to:

Extract → Pull text from documents

Categorize → Tag insurance-specific fields

Contextualize → Understand relationships

Verify → Make compliance decisions

This is why automated Certificate of Insurance verification can deliver instant compliance assessments that would take human insurance auditors 15-30 minutes per document.

How Jones Uses AI to Move from Extraction to True Verification

The real breakthrough in COI technology isn’t faster extraction—it’s the ability to make accurate insurance decisions at any scale without compromising on speed. Extraction turns documents into text. Verification determines whether the insurance complies.

Most systems stop at extraction. They leave everything that matters—Additional Insured checks, Completed Operations, Waivers and PNC language, endorsement interpretation—to the user. Jones uses AI to close that gap.

Additional Insured Verification

Consider Additional Insured verification. OCR can list names, but it can’t understand who they apply to, which coverage they modify, or how provisions like Ongoing vs. Completed Operations connect. In Construction, clients often require long lists of Additional Insured entities.

A single character error—LLC instead of Inc.—can invalidate coverage entirely. Jones understands these relationships. It compares entities character-by-character against client requirements, interprets the context of the Description of Operations, and highlights missing provisions that human reviewers might miss. This allows auditors to focus on nuanced judgment instead of repetitive manual checking.

Compliance Snapshot

AI agents power the Compliance Snapshot. The moment a COI is uploaded, users see an instant summary: which coverages pass, which fail, which endorsements are missing, and where gaps appear. No waiting. No uncertainty.

Across hundreds of customer deployments, the AI Compliance Snapshot has reduced review turnaround time by about 60%—down to 1.5 days on average, with many teams finally getting same-day approvals.

Vendor Experience

For vendors, the experience is striking from the moment they upload a COI: seconds later a preliminary audit appears showing what gaps their client cares about. It’s like seeing the future of compliance—and it gives them a chance to fix everything before the client ever sees the document.

AI Support Chat

For vendors, the experience is striking from the moment they upload a COI: seconds later a preliminary audit appears showing what gaps their client cares about. It’s like seeing the future of compliance—and it gives them a chance to fix everything before the client ever sees the document.

It replaces vague corrections with clear, confident next steps. If anything needs to be escalated, the Jones Customer Support team is on call.

In early testing, AI Support Chat reduces vendor-broker back-and-forth by up to 40 percent, cutting days of delay from the compliance process.

Beyond Basic Automation

AI agents can also detect fraudulent or manipulated COIs using metadata and pattern analysis, scan entire policy packets instantly for exclusions like Labor Law and Action Over, and identify inconsistencies across brokers or submissions.

These capabilities are already in development at Jones, where the focus is on building reasoning systems that go well beyond what OCR can handle.

Natural-Language Risk Assessment

Finally, natural-language risk assessment lets users interact with Jones the way they’d speak to an expert auditor. They can ask whether an owner has Completed-ops coverage, whether an exclusion affects a job, or whether a waiver is blanket or scheduled, and AI answers based on the actual document pages.

Our insurance auditing team often describe the AI agent as a second set of eyes, one that never gets tired and never misses a detail. Jones doesn’t just read COIs. It understands them, reasons across documents, guides corrections, and helps organizations make faster, more accurate decisions at scale.

Why ChatGPT Can’t Verify Certificates of Insurance

With the rise of general-purpose AI tools, many teams wonder whether they can simply upload a COI to ChatGPT and get a compliance answer.

Questions like ‘How can AI help in managing insurance compliance for Property Managers?‘ are common—but the answer depends on which AI you’re using.

ChatGPT can read PDFs, understand language, and generate detailed explanations. But in practice, general-purpose AI cannot accurately verify Certificate of Insurance compliance, and it won’t be able to for a long time.

The Problem with Generic AI for Insurance Verification

1. No Insurance Training Data

ChatGPT and Claude are trained on broad internet text. Jones is fine-tuned on millions of COIs, thousands of endorsements, and more than 50,000 verification rules.

Example: ChatGPT might confirm Additional Insured coverage exists because it sees “Acme Construction” listed—missing that the policy says “Acme Construction Inc.” while the contract requires “Acme Construction LLC.” In a claim, this mismatch means zero coverage.

2. No Client-Specific Requirements

ChatGPT cannot apply Rexford’s Additional Insured rules or distinguish Manhattan Construction’s PNC requirements from SavCon’s. Jones learns and applies account-specific rules to validate coverage across three layers: what the COI claims, what the policy actually says, and what the contract requires.

Example: ChatGPT sees the coverage for ‘Additional Insured per attached CG2010’ and confirms compliance. But Jones cross-checks all three layers and catches the gap: the COI claims blanket Additional Insured coverage, the actual endorsement only covers Ongoing Ops (excluding completed work), and the contract requires both Ongoing and Completed Ops coverage.

Without that cross-check, the compliance report looks solid while carrying a critical gap that could leave the client exposed in a claim.

3. Hallucination Risk

Generic AI often “guesses confidently” when unsure. In insurance, a hallucinated answer about coverage is as dangerous as no answer.

Example: ChatGPT might say “Waiver of Subrogation is included” when the endorsement section is completely blank—hallucinating compliance based on what it thinks should be there, not what actually is.

What Insurance-Specific AI Understands

Requirement ❌ Generic AI
(ChatGPT)		 ✅ Insurance-Specific AI
(Jones)
ACORD Form Structure No training Millions of examples
Endorsement Logic Guesses meaning Interprets 1,000+ endorsement types
Entity Matching Character-level only Understands LLC vs. Inc. implications
Client Rules Cannot learn Learns account-specific requirements
Edge Cases Not trained 50,000+ curated edge cases
Verification Accuracy ~60-70% 95%+ with expert review

Where COI Verification Is Going Next

COI verification is moving from a manual, document-heavy chore to something far more intuitive. Today, the moment a vendor uploads a COI, AI can already read it the way an experienced auditor does—interpreting endorsements, aligning names, checking requirements, and surfacing gaps instantly. What once took hours now happens in seconds.

The workflow is changing fast. Clients get a clear AI Compliance Snapshot. Vendors receive their own preliminary audit before the client even sees the document. And with AI Support Chat, they get precise, document-grounded instructions on how to fix every issue.

The long back-and-forth that once defined COI compliance is finally disappearing.

This shift isn’t just about efficiency. It’s about putting clarity where it belongs. Instead of forcing people to interpret messy documents, Jones combines AI speed with insurance expert review to interpret them directly—catching inconsistencies, identifying exclusions, and learning the unique rules of each account.

Human insurance auditors stay involved, but now they focus on judgment, not paperwork.

Imagine where this is heading. A vendor uploads a COI. AI understands it within seconds, shows what needs to be fixed, and guides them through the update. Compliance becomes faster, clearer, and far less frustrating for everyone involved.

The future of COI verification is simple: documents come in, clarity comes out.

No bottlenecks, no guesswork, no surprises.

We’re not fully there yet—but we’re closer than most people think.

And Jones is helping lead the way by combining deep insurance expertise with AI models built specifically for insurance compliance.

The Jones AI Difference

ChatGPT can read a COI. Jones can verify it.

Our models are trained with insurance knowledge using:

  • Millions of real COIs across Construction, Real Estate, and vendor compliance
  • Thousands of endorsement variations
  • 50,000+ curated verification rules and edge cases
  • Account-specific compliance requirementsIndustry-specific risk patterns

Result: Accuracy at scale that generic AI simply cannot deliver.

Why This Matters: When you’re managing insurance compliance for a $50M Construction project or $5BN Real Estate portfolio, “mostly accurate” isn’t good enough. You need AI built specifically for insurance verification.

[FAQ] Common Questions About AI-Powered COI Verification

1. What is the difference between AI and OCR for Certificate of Insurance verification?

OCR extracts text from COI documents without understanding the meaning. AI-powered verification interprets the content contextually, understanding endorsements, entity relationships, and compliance requirements the same way a trained auditor does. While OCR returns data, AI agents make compliance decisions.

2. Why does OCR fail on Certificate of Insurance documents?

OCR fails on COIs in several ways: It cannot maintain context across multi-page documents like ACORD 25 and ACORD 101, it struggles with compressed multi-coverage tables, it cannot distinguish important entity name variations such as LLC vs. Inc., and it cannot interpret endorsement language or legal requirements.

3. Can ChatGPT verify Certificate of Insurance compliance?

No. ChatGPT can read COI text, but it cannot accurately verify compliance because it lacks: Training on insurance-specific documents, Understanding of endorsement logic and edge cases, Knowledge of client-specific requirements, and the 50,000+ verification rules needed for accurate assessment. Generic AI often hallucinates answers in insurance contexts, which makes it dangerous to rely on for compliance decisions.

4. How does AI-powered COI tracking work?

AI-powered COI tracking combines multiple capabilities: Visual document understanding that preserves table structure and layout, Natural language processing to interpret endorsement language, Entity recognition to match Additional Insured requirements character-by-character, Relationship mapping across ACORD forms, Endorsements, and requirements, and Compliance decision-making based on millions of training examples and account-specific rules.

5. What is the difference between AI OCR and traditional OCR?

Traditional OCR passively converts text. AI OCR actively engages with content to extract, categorize, contextualize, and verify. AI OCR uses machine learning to understand document layout, preserve table structure, interpret insurance terminology, and make compliance decisions—capabilities traditional OCR cannot match.

6. How accurate is AI for Certificate of Insurance Verification?

Insurance-specific AI agents trained on millions of COIs achieves accuracy rates of 95 percent or higher when paired with expert review. This compares to roughly 60 to 70 percent accuracy for generic AI tools like ChatGPT, and 70 to 80 percent accuracy for traditional OCR alone on complex documents. The key is domain-specific training using tens of thousands of real verification rules and edge cases.

7. What COI compliance checks can AI automate?

AI agents can automate: Additional Insured entity verification (character-by-character matching), Coverage limit validation, Expiration date monitoring, Endorsement interpretation including Waiver of Subrogation and Primary & Non-Contributory, Fraud detection through metadata and pattern analysis, Exclusion identification, Multi-page document continuation, and Compliance gap flagging with corrective guidance.

8. Why is specialized AI better than ChatGPT for insurance verification?

Specialized AI is trained on millions of insurance documents with thousands of endorsement variations and more than 50,000 curated compliance rules. It understands insurance terminology, entity matching requirements, endorsement logic, and client-specific rules. ChatGPT has none of this domain-specific training and often produces confident but incorrect answers when processing COIs.