How V7 gives AI agents institutional memory

V7 Go transforms company files—spreadsheets, emails, and internal tools—into a structured Context Graph that connects entities, relationships, and evidence. This memory allows AI agents to avoid rediscovering context on every request, reducing redundant searches and token costs while improving retrieval accuracy within workflows. The system integrates with repositories like SharePoint and Google Drive, automatically identifying companies, funds, and people, and linking facts to existing records with cited sources.

The platform supports complex, multi-step workflows such as private equity deal screening and insurance underwriting by using models like GPT-5.6 Luna for extraction and GPT-5.6 Terra or Sol for reasoning and tool use. Agents can now complete workflows spanning 50–100 steps in minutes with 99.9% accuracy, maintaining an auditable trail of every decision. V7 also incorporates GPT-6 Astra for the most demanding graph-query tests, achieving 89% accuracy on its hardest benchmarks.

V7 Go’s Context Graph has demonstrated measurable efficiency gains, including a 69% improvement over the official baseline on the HERB benchmark for enterprise information retrieval and a 38% reduction in hallucinations on unanswerable queries. Customers report significant time and cost savings, such as financial services teams cutting review time from over 100 hours to under 10, saving $12,000 per task, and insurance teams reducing errors in claims processing by 13.5%.

The platform’s MCP server enables querying and ingestion of the Context Graph from clients like ChatGPT, while workflows can also be designed through MCP in Codex. This integration has reduced the time to create medium-length workflows from about one hour to 20 minutes. V7’s longer-term vision includes proactive workflows that flag inconsistencies when facts in the Context Graph change, ensuring analyses remain current and reliable.