Challenge

In Q4 2024, Elevate partnered with one of America’s largest, privately held, family-owned companies to support the implementation of a new Contract Lifecycle Management (CLM) system. The engagement required cleansing of legacy contracts, extraction of critical metadata, and end‑to‑end contract migration. The key challenges identified:

• A highly unstructured repository of 98K contract files with inconsistent naming conventions, including DBA/FKA variations
• Significant duplication with subtle redline differences
• Limited, inaccurate, or missing metadata, reduced searchability and CLM readiness
• Poor contract hierarchy, making parent-child relationships unclear across contract families
• Presence of drafts, unsigned versions, and incomplete contract sets
• Incorrect legacy metadata requiring cleanup and accurate mapping for CLM migration

Solution

Elevate deployed its AI-powered Enterprise Legal Management (ELM) platform and Contracts Insights solution to support CLM implementation and contract migration into Ironclad’s (an Elevate partner) CLM platform.

By combining AI-driven data extraction with expert legal review, we delivered clean, structured, and CLM-ready contract data within a centralised, searchable, and governance-ready repository. Key components of the solution included:

• Worked closely with Ironclad to support the CLM rollout, rapidly cleaning priority contracts and extracting metadata within one week to ensure go‑live readiness with minimal business disruption
• Conducted a detailed review of approximately 98,000 files, creating a comprehensive inventory and narrowing the scope to 60,000 in‑scope documents for metadata extraction and clean‑up
• Leveraged Elevate ELM’s OCR and parsing capabilities to automatically extract pre‑trained metadata at upload, with outputs validated by legal subject matter experts to ensure reliability
• Applied a standardised, scalable migration framework, including sample‑based playbook validation, delivery team training, enterprise‑wide contract inventory, removal of drafts and duplicates, and preparation of CLM‑ready metadata
• Combined first‑level AI extraction with lawyer-led substantive review to confirm accuracy, reconcile parent-child relationships, and ensure consistent, trustworthy contract data across document families
• Implemented a robust quality framework featuring SME calibration, defined accuracy metrics, structured feedback loops, and automated AI consistency checks-consistently achieving approximately 99% accuracy