Vendor-reported figures — source: legaltechnology.com
For investment management firms navigating complex M&A, IPO, and asset transactions, legal due diligence is one of the most resource-intensive bottlenecks in deal-making. At University of Cambridge Investment Management, senior in-house lawyers were responsible for manually reviewing large volumes of contracts — spanning real estate, debt financing, and private asset deals — to identify risk clauses and deviations from preferred positions. A single due diligence task across multiple legal texts took approximately 3 hours to complete. Multiplied across engagements, this created compounding delays that slowed deal timelines and consumed disproportionate senior legal capacity at a critical stage.
Cambridge Investment Management partnered with Robin AI as an early development partner to pilot Robin AI Reports, a contract analysis platform powered by large language models and generative AI. The tool — built on Anthropic's Claude 3 model accessed via Amazon Bedrock — runs inside Robin AI's own secure cloud environment, ensuring that sensitive deal documents never leave a controlled infrastructure. Legal teams can run reports across hundreds of contracts simultaneously, configuring a custom list of 'red flag' criteria to surface clauses that deviate from the firm's preferred positions. Rather than reading and annotating contracts individually, lawyers receive a structured report aggregating risk signals across the full contract set — a workflow shift from manual review to AI-assisted exception management. The firm began using the product in April 2024.
The productivity impact was immediate and measurable. Tasks that previously consumed 3 hours of senior lawyer time now average 30 minutes — an 85% reduction in contract analysis time per engagement. Key outcomes include:
Sam Sturge, Director of Private Assets at Cambridge Investment Management, noted the tool enables the team to conduct legal reviews "in a more accurate way" — suggesting the benefit extends beyond speed to review quality.
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