OpenAI published a customer case study on October 2, 2026, describing how Chatham Financial, a capital markets advisory firm, uses OpenAI’s Codex and GPT-5.6 models across its advisory workflows, including a trade validation application the firm says reduced review time from approximately 30 minutes to under four.
The case study lists Chatham as a small and medium-sized business in the finance industry in North America, with ChatGPT, Codex, and the OpenAI API as the products in use. The firm works with clients on complex capital markets decisions. According to the case study, Chatham uses Codex to build internal and client-facing tools and GPT-5.6 to power AI features, work that spans employee-built applications, its next-generation capital markets operating system, Chatham Onyx, and its reengineering consulting service, Process Zero.
Chief Executive Officer Matt Henry said the firm begins with the outcome it wants, pinpoints where judgment matters, and then designs how to deliver that outcome with the capabilities currently available.
Trade Validation Under Process Zero
Through Process Zero, Chatham reengineers workflows around outcomes. For each workflow, the firm establishes the minimum inputs and evidence needed, fixes where human judgment remains essential, and specifies how AI and AI-built tools should support the work.
Trade validation is an early example. Chatham’s Controls and Data Integrity team protects the accuracy of transaction data by confirming that each record in its systems matches what the client authorized and what was actually executed. Using Codex, Chatham developed a trade validation application that assembles supporting evidence on each transaction, checks the key terms, and marks discrepancies for review, and the firm is comparing the application’s results with those of experienced reviewers.
“In early measurement, the application reduced review from approximately 30 minutes to under 4 minutes. Just as important, we are validating its performance against real transactions and experienced reviewers before we expand automation. Ultimately, we will be able to produce easily auditable and more accurate results faster.” —Alex Nordlinger, Co-head of Chatham’s AI Advisory practice
Chatham plans to extend the application to additional trade types and to automate more of the workflow while maintaining appropriate controls and professional oversight, according to the case study.
Employee-Built Applications on Chatham Vibes
Chatham’s client-facing AI work grew out of its own operating experience, the case study states. Employees apply ChatGPT and Codex across research, analysis, drafting, software development, and other day-to-day work. Employees have also become builders through Chatham Vibes, an internal platform on which they create applications fitted to their own work. By default, AI features in Vibes applications run on GPT-5.6 Terra, and individual apps can be configured to upgrade to GPT-5.6 Sol.
Those applications increasingly support client-facing workflows. One supports reviews of maturing-cap trades and the preparation of pricing workbooks and client communications, while others handle fixed-income rate sheet production, hedging dashboard preparation, and trade confirmation reviews. Chatham professionals evaluate the AI-assisted output, refine it where needed, and decide what reaches the client.
Chatham Onyx and Model Routing
Chatham Onyx places assets, debt, and derivatives in a single environment where clients and advisors work from connected, governed data, with AI features that retain traceability back to the underlyingelping teams plan, build, test, document, and review software
“Codex is helping our teams turn product vision into working capabilities more quickly, while our standards for accuracy, security, and accountability remain the same,” said Chief Technology Officer John DeGuenther.
The Onyx platform runs on GPT-5.6 Sol, GPT-5.6 Terra, GPT-5.4, and GPT-4.1. Tasks such as simple analysis and non-production testing are routed to the most cost-effective models, while GPT-5.6 is used for complex tasks that need to maximize accuracy and value. One Onyx capability, ChatFIN, surfaces patterns in historical market data, gives users a view of their portfolios, and finds and links the legal documents covering debt, derivative, and lease terms.
Expert Capacity and Next Steps
The case study states that Chatham is using OpenAI to run structured comparisons, organize evidence, and identify exceptions, and to help teams explore information more efficiently, and that as a result advisors can spend less of their time assembling information and more of it interpreting that information, managing difficult cases, and advising clients.
Co-Chief Operating Officer Mike Noonan said the limiting factor for the firm has been the time experts spend reaching the point where they can apply their expertise, and that OpenAI is helping recover that time.
Chatham’s stated next steps are to expand trade validation across additional products, continue refining employee-built applications, and use Codex to help develop new capabilities for Onyx.
