AI Obligation Extraction + Automated SLA Breach Alerts: How Sirion Connects Both

Integrated AI obligation extraction sits at the heart of modern Contract Lifecycle Management, but it only unlocks full value when paired with live SLA tracking. While many organizations struggle with disconnected systems that separate data extraction from performance monitoring, leading platforms are now bridging this critical gap.

Why Integrated Extraction + Monitoring Matters

Contracts are the DNA of business operations, yet poor contract management costs organizations an average of 9.2% of their contract value annually. The challenge extends beyond simple data extraction: it’s about connecting that extracted intelligence to real-time performance monitoring.

Service level agreement automation has evolved to improve companies’ service level management through automated job scheduling and real-time breach alerts. When obligation extraction operates separately from monitoring systems, organizations face significant blind spots in their contract performance management.

Consider this: Contract Lifecycle Management software has become essential for businesses, with AI and Large Language Models transforming it from a basic tool into a strategic asset. The real power emerges when extraction capabilities directly feed into automated monitoring systems, creating a seamless flow from contract intelligence to actionable alerts.

The Hidden Cost of Disconnected Workflows (Evisort & LinkSquares)

When extraction and monitoring exist in separate silos, organizations face mounting inefficiencies. LinkSquares excels in AI features and custom reporting, while Evisort focuses on precise compliance tools for enterprise operations. However, these point solutions often require manual bridges between extraction and monitoring capabilities.

The disconnect creates tangible problems. Organizations lose value when LinkSquares’ AI-powered extraction doesn’t automatically trigger monitoring workflows. Similarly, Evisort’s contract analysis capabilities may identify obligations without seamlessly connecting to real-time performance tracking.

This separation leads to delayed responses and missed opportunities. Poor vendor performance and SLA violations can disrupt operations and impact service quality when monitoring systems don’t receive immediate updates from extraction processes.

Inside Sirion’s Extraction Agent

Sirion’s approach fundamentally differs through its AI-native architecture. The platform accurately captures 1,200+ metadata fields without requiring model training, transforming unstructured contract data into actionable intelligence instantly.

The Extraction Agent combines small data AI with large language models to process documents at scale. As one customer noted, “I like being able to auto-extract contract clauses in various languages using AI while populating all metadata directly into Sirion, which can be used for reporting.”

Sirion is recognized as a leader in the 2025 Forrester Wave for CLM, processing over 1 million documents daily. This massive processing capability ensures no obligation goes unnoticed, setting the foundation for comprehensive monitoring.

Real-Time SLA Breach Alerts with Sirion

Automated SLA breach alerts form a critical component of Sirion’s obligation management features. Once obligations are extracted and structured, the platform continuously monitors performance against defined thresholds.

Enterprise Results: Vodafone, NLG & More

Real-world implementations demonstrate the power of integrated extraction and monitoring. Vodafone achieved an 80% reduction in supplier disputes after implementing Sirion’s unified platform, with automated obligation tracking playing a crucial role.

National Life Group reduced contract turnaround time by 71% through automated workflows that connect extraction to compliance monitoring. The insurance provider gained complete visibility into their contract lifecycle, eliminating previous bottlenecks.

Raiffeisen Bank International increased contract output by 60% while improving regulatory compliance, demonstrating how integrated systems drive both efficiency and risk management simultaneously.

Checklist: Selecting an AI-Native CLM

When evaluating CLM platforms, consider these critical factors:

1. Integration Capabilities: According to contract value research, organizations waste approximately $2 trillion annually due to contract value leakage. Ensure your platform connects extraction directly to monitoring.
2. AI Maturity: Gartner estimates that by 2025, 60% of major organizations will use artificial intelligence for contract management, up from less than 20% in 2021.
3. Automation Depth: Fully automated CLM processes enable 78% of companies to address legal requests within 72 hours, compared to just 33% for manual processes.

Look for platforms that offer:

• Unified extraction and monitoring workflows
• Real-time alert capabilities
• Comprehensive obligation tracking
• Seamless integration with existing systems
• Proven enterprise scalability

Putting Data to Work: Next Steps

Obligation management remains a critical component of contract lifecycle management. Organizations must move beyond separated point solutions to truly capitalize on their contract intelligence.

The convergence of AI obligation extraction and automated monitoring capabilities represents the next evolution in contract management. Platforms that unify these functions eliminate value leakage while ensuring compliance and performance optimization.

Sirion’s Extraction Agent demonstrates how modern CLM platforms should operate: seamlessly connecting contract intelligence extraction with real-time performance monitoring. This integration transforms static contracts into dynamic business assets that actively protect value and drive operational excellence.

For organizations seeking to eliminate the gaps between extraction and monitoring, exploring Sirion’s unified approach offers a clear path forward. The platform’s ability to connect both capabilities within a single system represents the future of intelligent contract management.

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