Page One, Inc.

In litigation support, transcript review is one of the most time-consuming stages of case preparation. Page One, Inc., a legal services firm, faced this pressure acutely on a fast-paced financial services matter requiring review of 32 deposition transcripts — each ranging from 200 to 300 pages. Manual extraction of key facts, dates, entities, and issue-relevant details from that volume demanded hours of attorney and paralegal time per transcript. At that pace, the sheer scale of the document set created a bottleneck that compressed downstream case strategy work and threatened the firm's ability to meet client timeline expectations.

Page One deployed Relativity's aiR for Case Strategy, a generative AI product built into the RelativityOne platform, to automate fact extraction across the full transcript set. The tool uses large language models to extract structured facts from each document — capturing fact dates, types, related issues, and associated entities — while tagging each finding as either helpful or harmful to the stated case theory to surface contradictory evidence alongside supportive material. Rather than replacing attorney judgment, the system functions as an AI-assisted first pass: attorneys configure a matter overview and key issues, and the platform generates fact chronologies and document-grounded insights that practitioners then refine. Deployment occurred within the firm's existing RelativityOne environment, requiring no separate integration infrastructure.

Page One completed the full 32-transcript review 70% faster than traditional manual methods. Processing time dropped from hours per transcript to minutes — a compression that meaningfully accelerated case strategy development on a time-sensitive matter. Qualitative outcomes were equally significant:

• Consistency: the AI repeatedly surfaced the same core insights attorneys would have identified manually, building practitioner confidence quickly
• Adoption: the team moved from skepticism to active reliance within a single matter, validating the tool on a high-stakes financial services case
• Downstream impact: faster transcript review freed attorney capacity for higher-order analysis earlier in the case lifecycle

• AI fact extraction delivers the largest time savings when applied to high-volume, structurally similar documents — deposition transcripts are a strong fit
• Grounding every extracted fact in a document citation is essential for attorney trust; unsourced AI output will not clear the bar for litigation use
• Tagging facts as harmful or helpful to the case theory prevents confirmation bias and surfaces risk earlier
• Practitioner confidence builds fastest when AI output is validated against a known document set — running a familiar matter first accelerates adoption
• Speed gains at the review stage compound: faster fact extraction shortens the entire path from discovery to deposition preparation