Wilson Sonsini

Contract review is one of the highest-stakes, highest-volume tasks at major law firms — and one of the hardest to delegate to AI. Wilson Sonsini, a firm with more than 1,000 attorneys across 19 offices, needed a reliable AI solution capable of applying complex legal playbooks accurately enough to support production attorney work. Existing AI tools were falling well short: most solutions achieved only around 50% accuracy on sophisticated commercial playbooks. The firm set a deliberate 90% accuracy threshold as the minimum bar for adoption — reflecting how risk-averse the legal industry is when it comes to tools that could affect client outcomes. Falling below that bar meant continued reliance on slow, expensive manual review.

Wilson Sonsini partnered with Dioptra, a Y Combinator-backed legal AI startup, to deploy an AI contract review agent powered by large language models and generative AI. Dioptra's agent applies agentic AI frameworks to execute complex legal playbook logic — handling negotiation, due diligence, and compliance review use cases. It produces in-line redlines and issue detection outputs alongside transparent reasoning explanations, giving attorneys visibility into how each conclusion was reached. The deployment was integrated into Wilson Sonsini's newly launched Neuron Commercial service, a fixed-fee commercial contracting offering targeting cloud services companies. Dioptra's prior experience building reliability tooling for AI and ML engineers informed its approach to precision and auditability — two properties that proved critical for attorney adoption in a trust-first environment.

Dioptra exceeded Wilson Sonsini's 90% accuracy threshold across all review categories tested in production:

• 95% accuracy on first-party contract review
• 92% accuracy on third-party contract review
• 94% accuracy on issue detection (due diligence and compliance use cases)

David Wang, Wilson Sonsini's CIO, confirmed "consistently 92%+ accurate" performance across all categories — a result that unlocked full production deployment. Chris Brookhart, the firm's project lead, noted that the agent's advanced logical reasoning on complex clause interactions went beyond what he anticipated. The deployment now supports attorney review of customer agreements under the Neuron Commercial service, enabling clients to close revenue deals faster at predictable fixed-fee pricing.

• Set a hard accuracy threshold before evaluating vendors. Wilson Sonsini's 90% benchmark created an objective adoption gate — and forced vendors to prove performance rather than promise it.
• Transparency is not optional in legal AI. Attorneys adopted the tool partly because it explained its reasoning; conclusions without justification would not have cleared the firm's risk bar.
• Fixed-fee packaging aligns AI economics with client value. Wrapping AI-assisted review into a predictable-price service (Neuron Commercial) made the efficiency gains tangible to clients, not just internal.
• Prior AI infrastructure work compounds. Dioptra's reliability engineering background directly enabled the precision required for legal deployment — domain expertise alone was insufficient.