V7 turns company files into agent context, with GPT‑6 Astra reaching 89% accuracy on its hardest graph-query tests.

Today’s models can reason through complex tasks, but they don’t automatically understand the underlying business context of those tasks. Which fund report is current? How is the same entity named across three systems?

That context lives in documents, data rooms, spreadsheets, emails, and internal tools: scattered, unresolved, and invisible to agents. For teams in finance, insurance, and real estate, retrieval accuracy within workflows is non-negotiable.

After building a widely used computer vision accessibility app together, Rizzoli and Edwardsson started V7⁠(opens in a new window) in 2018 to help companies teach AI systems how their businesses work. V7 Go is an agentic platform to build mission critical workflows, and organize buried context into memory that agents can query and act on.

V7 Go uses GPT‑5.6 Luna to extract information from millions of files and organize it in the Context Graph, which connects entities, relationships, and cited evidence, powering MCP search and repeatable workflows that can span hundreds of steps. For Workflows, V7 Go uses GPT‑5.6 Terra and Sol for reasoning and tool use across complex, multi-step instructions that take humans dozens of hours to complete. V7 is also starting to use GPT‑6 Astra on the most demanding Context Graph queries, including financial analysis across thousands of documents.

9% accuracy, while maintaining an auditable trail of every decision made.

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