A Terminal X bemutatta a Capital Memory rendszert, amellyel a pénzügyi alapelemzéseket és a befektetési folyamatokat automatizálja a cégek saját adatain az OpenAI esettanulmánya szerint. A rendszer a GPT-6.1 Sol és GPT-6 Luna modellekre építve rögzíti a vezető befektetők döntési logikáját, így megakadályozza a szaktudás elvesztését a munkatársak távozásakor.
A platformba épített Eval Agent a cég saját szempontrendszere alapján automatikusan értékeli az elemzési javaslatokat. A GPT-6.1 Sol modell segítségével a rendszer 88,6 százalékos egyezést ért el a tapasztalt humán bírálók ítéleteivel, és 25 százalékkal több olyan javaslatot szűrt ki, amely eltért az adott alap befektetési mandátumától.
A nagyobb kontextusablak és a jobb tokenhatékonyság révén a munkafolyamatok futtatása 30 százalékkal alacsonyabb lekérdezési költséggel valósítható meg. A Terminal X eddig több mint 1700 vállalati tudásbázist épített ki és 1 millió egyedi munkafolyamatot futtatott le.
Az eredeti szöveg (OpenAI)
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
Annualized savings for one customer on due diligence and investment workflows
Higher agent agreement with human reviewers on GPT‑6.1 Sol vs. next-best model
Lower cost per query with 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.
Next, those processes are encoded in hundreds of agents, customized by asset class and strategy, with sub-agents for each investment stage. GPT‑6.1 Sol handles the stages that require judgment and a huge context window: synthesizing the investment thesis, identifying key considerations, and assessing fit with the firm’s mandate. GPT‑6 Luna runs the high-volume steps where token efficiency matters more than depth.
The agents work inside each firm’s private data room, a dedicated environment for enterprises to share and manage sensitive investment data. The agents process documents in English, Korean, Japanese, and Chinese, and store what they learn as a firm Memory: a versioned record of how one desk makes one kind of decision, including its past analyses, committee decisions, workflow instructions, and house views.
Engineers turn each Memory into a repeatable workflow on Terminal X’s platform, where every production query is auditable with its full trail: the steps taken, sources retrieved, model used, and the reasoning.
According to Terminal X, customers see significant savings as a result of this approach, including one private equity firm that reported $2.4 million in annualized savings from automated due diligence and investment memo workflows.
To check agents’ answers against the firm’s own standard, Terminal X built an Eval Agent. It’s an orchestrated workflow on GPT‑6.1 Sol and other OpenAI models designed to read every agent output the way a VP reads an analyst’s work.
The Eval Agent checks each output against a 54-criterion rubric. It asks: Are the numbers, comps, consensus figures, and quotes pulled from the right sources? Does the narrative hold? Does the recommendation tie to the house view?
Each check scores pass or fail. Every fail includes a one-line reason and a better source or corrected text and becomes a Memory. For example, a missed source, a wrong comparable, or a thesis that drifts from the house view is encoded as a new workflow instruction or a new test case for that desk.
“Every answer gets eval’d, and whatever the analyst corrects teaches the
