Cooley and OpenAI Build AI System for IPO Filing Work

Cooley said on Sept. 17 it launched an AI-enabled IPO preparation service built with OpenAI and designed for ChatGPT Enterprise. The offering targets Form S-1 drafting, one of the most labor-intensive stages in taking a company public. Cooley lawyers remain responsible for legal analysis and the final filing.

The launch places a major capital-markets practice at the center of a shift from general-purpose legal AI tools toward systems built around a law firm’s own methods, precedents and client information. Cooley developed the product with OpenAI as a design partner and is positioning it as the first capability from Cooley AI, a new services company intended to change how the firm uses technology, staffs matters and prices legal work. The move extends Cooley’s decade-long position near the top of the U.S. IPO market into legal operations and workflow design.

The product uses purpose-built AI agents to assemble an initial S-1 draft from company materials, research, drafting guidance and selected precedent. Cooley says the first draft can be produced in minutes rather than days, giving capital-markets lawyers more time to assess transaction-specific issues and management teams more time to address strategic questions. The system is designed to refresh the filing as deal terms change, while Cooley lawyers review the work throughout the process. Cooley advised on 180 deals globally in 2025, representing more than $51.5 billion in deal volume, according to the firm. Its broader capital-markets pipeline includes IPOs, direct listings, follow-on offerings, convertible notes, SPACs and de-SPAC transactions.

Industry Implications

Cooley’s approach signals that the next competitive layer in legal technology may be proprietary workflow infrastructure rather than access to a standalone chatbot. Law firms with deep specialist data and repeatable processes can use AI agents to package that expertise into client-facing services, potentially narrowing the gap between legal advice and legal operations. For enterprise legal buyers, the appeal is measurable throughput and greater predictability in a high-value process, but the important diligence questions will concern data governance, review controls, auditability and responsibility for errors. Competitors will face pressure to show not only that their lawyers use legal AI, but that their systems improve outcomes without weakening professional judgment.

DreamLegal Perspective

Legal technology vendors should watch whether firms follow Cooley’s model by building domain-specific systems around their own work product, rather than deploying generic tools across practices. Legal operations teams evaluating similar products should measure drafting speed alongside rework, approval cycles, source traceability and lawyer intervention. Cooley’s planned expansion into private companies, funds and M&A will provide an early test of whether a capital-markets workflow can become a repeatable platform strategy. The market should judge these offerings by delivered matter economics and control quality, not by agent counts or model brandi