Terminal X’s Eval Agent grades outputs against a 54-criterion rubric, catching 25% more deviances from a fund’s mandate with GPT‑6.1 Sol.

Higher detection rate when agent answers fall outside a fund’s mandate on GPT‑6.1 Sol vs. next-best model

Higher agent agreement with human reviewers on GPT‑6.1 Sol vs. next-best model

An investment firm’s edge is built deal by deal, but the judgment behind each decision usually lives in the heads of a few senior investors. When they leave, the firm takes on what Terminal X⁠(opens in a new window) calls memory debt: the firm may keep the documents, but it loses the reasoning.

Terminal X’s Capital Memory operating system is designed to pay that debt down. The company’s forward-deployed engineers, former finance and investment professionals at JPMorgan and Blackstone, sit with each customer’s investment teams, design the firm’s agentic architecture, and encode how the firm invests into agents the firm owns, deployed on-premise or in its own cloud.

OpenAI models, including GPT‑6.1 Sol and GPT‑6 Luna, handle the reasoning inside those agents. And Terminal X’s Eval Agent grades the agents’ output against the firm’s standards. GPT‑6.1 Sol helped the Eval Agent catch about 25% more recommendations that strayed from a fund’s mandate and ran the same workflows at approximately 30% lower cost per query than the next-best model.

For each customer, Terminal X builds agents designed to invest the way the firm’s best investors do. Terminal X engineers spend weeks onsite with the customer, reverse-engineering the firm’s trusted memos and capturing processes that were never written down, such as how the credit desk screens a borrower or which sources the co-investment team pulls before a memo.

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