Missing Indemnity Clauses? Troubleshoot AI-Powered Clause Extraction Software

Even the smartest CLM engines still stumble on indemnity clause extraction. We lay out why it happens, what it costs, and how to stop the misses for good.

Why Missing Indemnity Clauses Still Slip Through “Smart” Extraction Engines

Indemnity clauses represent one of the most critical yet frequently mishandled components in contract management. These provisions, which require one party to “defend, indemnify, and hold harmless” the other party, appear in 85% of contracts yet consistently challenge even advanced extraction systems.

The business impact of missing these clauses extends far beyond simple oversight. When proper licenses and rights are not in place, organizations face potential IP infringement claims that can escalate into costly litigation. Research shows that companies can lose up to 20% of potential revenue due to mishandled contracts, with indemnity clauses representing a significant portion of this risk.

The extraction challenge stems from the inherent complexity of indemnification language itself. Unlike straightforward data fields, indemnity provisions often weave through multiple contract sections, buried within limitation of liability clauses or scattered across different liability provisions. AI systems trained on standard patterns struggle to connect the critical verbs like “defend,” “indemnify,” and “hold harmless” with the specific parties and scope of coverage required for accurate extraction.

According to industry benchmarks, 1200+ metadata fields can be accurately captured by leading extraction engines without model training. However, indemnity clauses often fall outside these standard patterns, requiring specialized attention that generic extraction tools frequently miss.

Indemnity Basics: Legal & Financial Fallout When the Clause Goes Missing

The financial consequences of overlooking indemnity clauses reach staggering proportions. A McKinsey & Company study estimates that unfulfilled contractual obligations result in 2% leakage in large enterprises. For an enterprise with $2 billion in annual spend, this translates to $40 million lost annually.

These AI infringement indemnification clauses require careful review and negotiation to ensure that the protections they purport to provide are not restricted or negated by exceptions and limitations. The complexity multiplies when dealing with AI-related agreements, where intellectual property risks intertwine with data security concerns.

Contractual risk transfer through indemnification serves as the primary mechanism for shifting risk from one party to another. When extraction systems fail to identify these provisions, organizations unknowingly assume liabilities that should rest with their counterparties. This oversight creates cascading effects: unidentified obligations lead to compliance failures, missed insurance requirements result in coverage gaps, and undefined liability boundaries spawn disputes that could have been prevented.

Most critically, the Air Canada case in 2024 proved that companies bear liability for AI-generated outputs, including those from clause extraction systems. When extraction software misses or misinterprets indemnity provisions, the resulting exposure extends beyond the immediate contract to potential third-party claims, regulatory penalties, and reputational damage.

Why AI Clause Extraction Misses Indemnity Language: 4 Hidden Failure Modes

The failure of AI systems to accurately extract indemnity clauses stems from four primary technical challenges that even sophisticated models struggle to overcome.

1. Pattern Complexity and Language Variation: Testing reveals that SmartImport property and clause collection produces 20% error rates in metadata extraction. Indemnity clauses exemplify this challenge through their linguistic diversity. The same obligation might appear as “shall defend and indemnify,” “agrees to hold harmless,” or “undertakes to compensate for losses.” Models trained on limited datasets fail to recognize these variations, especially when legal drafters employ jurisdiction-specific terminology or industry-specific formulations.
2. Context Dependency: Research on large language models shows that LoRA, data balancing, and data augmentation techniques are essential for enhancing model accuracy in complex contract extraction. Indemnity clauses often depend heavily on surrounding context for proper interpretation. A clause stating “Party A shall indemnify” means nothing without identifying Party A from earlier sections and understanding the scope defined in subsequent paragraphs.
3. Structural Ambiguity: Contract management best practices demonstrate that organizations can gain up to 70% efficiency in review processes with proper AI deployment. Yet indemnity provisions frequently span multiple pages, with carve-outs, exceptions, and conditions scattered throughout the document. Standard extraction models that process contracts linearly miss these distributed elements.
4. Training Data Limitations: The EU’s AI Act now mandates that providers of general-purpose AI models must maintain technical documentation about training data. Many extraction systems lack sufficient examples of complex indemnity structures in their training sets. As noted by industry analysis, “AI for redlining is as basic as it gets… I still had to check every change by hand.”

These failure modes compound when contracts involve multilingual clause extraction or non-standard formatting. Without addressing these fundamental challenges, organizations continue to face significant extraction errors that undermine their risk management strategies.

Scorecard: How Leading CLM Vendors Handle Indemnity Clause Extraction

The competitive landscape reveals significant disparities in how major CLM platforms tackle indemnity clause extraction. According to Gartner’s 2024 Magic Quadrant, Sirion has been recognized as a Leader positioning itself as an AI-native platform that automates all stages of the contract lifecycle.

Performance metrics across platforms show marked differences. Industry reviews indicate that Icertis scores 7.4 in composite ratings while maintaining decent compliance features. However, extraction accuracy varies significantly across vendors, with some platforms showing error rates approaching 20% for complex clause types.

Ironclad demonstrates notable gaps in AI accuracy with metadata extraction errors requiring manual fixes and a steep onboarding curve. User feedback consistently highlights the need for manual oversight even with AI-powered systems.

Agiloft received the high scores in 12 criteria including interoperability and configurability in Forrester’s assessment. Yet even leading platforms struggle with the nuanced requirements of indemnity extraction, particularly when dealing with non-standard language or cross-referenced provisions.

The variance in vendor capabilities underscores a critical market reality: not all AI extraction engines are created equal. Organizations must evaluate platforms based on their specific indemnity clause patterns and risk tolerance levels.

Why Sirion Scores Higher on Indemnity Accuracy

Sirion’s extraction agent distinguishes itself through a unique architectural approach that combines small data AI with LLMs to transform unstructured contract data into reliable insights. This dual-model strategy addresses the specific challenges of indemnity clause extraction.

The platform processes 80% faster contract migration while maintaining accuracy across 1200+ fields and clause types. This performance stems from Sirion’s ability to understand context beyond simple pattern matching. Where competitors rely solely on large language models that can hallucinate or miss nuanced legal language, Sirion’s small data AI provides precision in identifying specific indemnity structures.

Sirion’s Performance Management capabilities include obligations tracking and compliance automation, critical features for managing the downstream impact of properly extracted indemnity clauses. The platform’s ability to connect extraction with post-signature performance ensures that identified indemnity obligations translate into actionable risk management protocols.

Step-by-Step Troubleshooting Guide for Indemnity Clause Extraction Errors

Resolving extraction failures requires a systematic approach that combines technology optimization with strategic human oversight. Organizations processing up to 5X faster while maintaining 100% accuracy demonstrate the power of properly implemented troubleshooting protocols.

Step 1: Audit Your Current Extraction Performance Begin by analyzing your existing extraction accuracy. Trust in AI outputs jumps from 31% to 83% when expert validation is introduced. Review a sample of 100 recent contracts, manually checking for indemnity clauses and comparing against system extractions. Document false negatives (missed clauses) and false positives (incorrect identifications).

Step 2: Identify Pattern Failures Legacy systems create substantial barriers to effective extraction due to outdated technology and non-standardized formats. Catalog the specific language patterns your system misses. Common failures include mutual indemnification, limited indemnification with caps, and third-party indemnification requirements.

Step 3: Implement Targeted Re-extraction For contracts with identified gaps, deploy specialized extraction runs focused solely on indemnity provisions. Configure your extraction engine to flag low-confidence results for manual review rather than accepting potentially incorrect extractions.

Step 4: Establish Validation Protocols Combining 5% manual work achieves 100% accuracy in contract data extraction. Create checkpoints where legal experts review AI-identified clauses, particularly for high-value contracts or those with complex liability structures.

Step 5: Create Feedback Loops Document extraction errors and feed corrections back into your system. Modern platforms learn from corrections, progressively improving extraction accuracy over time without requiring specialized technical skills.

Add Human-in-the-Loop Validation Without Killing Speed

Human-in-the-loop (HITL) systems allow humans to give direct feedback to models for predictions below certain confidence levels. This approach balances automation efficiency with the nuanced judgment that complex indemnity clauses demand.

Implementing HITL validation requires strategic deployment. Rather than reviewing every extraction, focus human expertise on high-risk areas: contracts above certain value thresholds, non-standard indemnity language flagged by the AI, and agreements with known complex liability structures. As one industry expert notes, “AI should be the autopilot, not the pilot.”

The key lies in maintaining extraction speed while adding precision where it matters most. Configure your system to automatically process standard indemnity patterns while routing edge cases for expert review. This selective approach preserves the efficiency gains of automation while ensuring critical provisions receive appropriate scrutiny.

Future-Proofing: Regulatory & Compliance Clauses Your AI Must Recognize Next

The regulatory landscape continues to evolve rapidly, introducing new compliance requirements that extraction systems must handle. The EU’s AI Act establishes risk-based classifications mandating stringent obligations for high-risk AI applications, fundamentally changing how organizations must approach clause extraction.

By August 2025, the AI Act obligations for providers enter into force, requiring detailed documentation about AI model training and performance. Organizations using AI for clause extraction must ensure their systems can identify and extract not just traditional indemnity provisions, but also emerging regulatory compliance clauses related to AI governance, data protection, and algorithmic accountability.

New compliance frameworks demand extraction capabilities beyond current standards. Systems must recognize provisions for regulatory compliance clauses including data privacy, transparency requirements, and ethical AI deployment obligations. The ability to extract and track these evolving clause types determines whether organizations maintain compliance or face regulatory penalties.

Generative AI models now require extraction systems to identify clauses addressing computational thresholds exceeding 10^23 FLOP, model documentation requirements, and specific liability allocations for AI-generated outputs. Traditional extraction engines lack the sophistication to parse these technical provisions accurately.

The General-Purpose AI Code of Practice published in July 2025 introduces voluntary compliance standards that forward-thinking organizations are already incorporating into their contracts. Extraction systems must evolve to recognize these new clause patterns or risk missing critical compliance obligations.

As regulatory frameworks continue to emerge globally, from DORA in Europe to evolving FTC guidance in the United States, the complexity of compliance clause extraction will only increase. Organizations must select extraction platforms with proven abilities to adapt to new clause types and regulatory requirements.

Key Takeaways & Next Steps

The challenge of extracting indemnity clauses reveals a broader truth about AI-powered contract management: technology alone cannot solve complex legal extraction problems. Success requires combining advanced AI capabilities with strategic human oversight and continuous system refinement.

Organizations seeking to eliminate extraction failures should focus on three critical actions. First, audit your current extraction accuracy to establish a baseline and identify specific failure patterns. Second, implement human-in-the-loop validation for high-risk contracts while maintaining automation efficiency for standard agreements. Third, select a CLM platform with proven extraction capabilities and the flexibility to adapt to evolving regulatory requirements.

As noted by industry leaders, “Sirion’s industry-leading AI technology offers significant advances giving instant access to critical data, automating non-value-added tasks, and driving behaviors that result in better contracting outcomes.” The platform’s combination of small data AI with cognitive LLMs addresses the specific challenges that cause indemnity extraction failures.

For organizations ready to eliminate the risks of missing indemnity clauses, the path forward is clear. Evaluate your current extraction accuracy, identify gap patterns, and implement a solution that combines AI precision with human expertise. The cost of missing these critical provisions far exceeds the investment in proper extraction technology.

Explore how Sirion’s legal solutions can transform your contract extraction accuracy and ensure no critical indemnity clause goes undetected. The difference between 20% error rates and near-perfect extraction could save your organization millions in unforeseen liabilities.

Frequently Asked Questions (FAQs)