V7 Go is an agentic platform designed to provide AI agents with structured institutional memory by converting company files into a "Context Graph." This system allows agents to access and act on business context—such as financial reports, spreadsheets, and emails—that is typically scattered across various internal tools and repositories like SharePoint and Google Drive.
V7 Go Platform Provides AI Agents with Institutional Memory via Context Graph
The platform utilizes a tiered model approach to handle different levels of complexity. V7 Go employs GPT-5.6 Luna for high-volume structured information extraction, while GPT-5.6 Terra and Sol are used for complex reasoning and multi-step tool use. Notably, V7 has begun testing GPT-6 Astra for its most demanding graph-query tasks; in tests involving highly complex datasets, GPT-6 Astra achieved 89% accuracy on the hardest queries, compared to 78% for GPT-5.6 Sol.
By providing structured context, V7 Go aims to reduce the need for repetitive searches and lower token costs. The company reports that its technology has enabled asset managers to screen deals 21 times faster and helped financial services teams reduce review times from over 100 hours to under 10 hours. The Context Graph also supports MCP (Model Context Protocol) server querying, allowing users to access the organized data through ChatGPT and other compatible clients.
Sources
- How V7 gives AI agents institutional memory （OpenAI News, 2026-09-21）
- V7