The concrete workflow
V7 Go connects company documents and data to a context graph so an AI agent can query relationships and the evidence behind them. In a case study published by OpenAI, V7 says asset managers used its system to screen deals 21 times faster, turning a full-day process into about 15 minutes.
The case describes a workflow that reads a deal document, extracts financials, terms, management information and risk fields, then produces a screening note with citations. V7 also reports that a financial-services team reduced one review from more than 100 hours to under 10, saving an estimated $12,000 in expert time per task. Those are customer-reported outcomes, not independent controlled trials.
Why memory changes the agent
A model can reason, but it does not automatically know which fund report is current or how an entity is named across three systems. V7’s Context Graph connects entities, facts and original documents so the agent can retrieve stable organizational context instead of repeating dozens of searches on every request.
V7 uses smaller models for high-volume extraction and stronger ones for reasoning-intensive steps. On its hardest internal graph-query set, it reports 89% accuracy with GPT-6 Astra, versus 78% with GPT-5.6 Sol. That comparison indicates why model selection and retrieval structure both matter to a long workflow.
What an operator should take from it
The strongest design principle here is evidence continuity. A useful agent needs to show which original file supports a claim, especially if the result feeds a financial or insurance decision. A source-linked graph can help, but records also need updates, access controls and checks when a source changes.
For another business, the question is which repeated workflow burns expert time because context is scattered. Start with one bounded process and measure completion time, citation accuracy, correction burden and cost. V7’s results suggest that better organization of existing knowledge can be as important as upgrading the model.
Source published 2026-09-21. Coverage is based on the maker’s announcement and demonstration.