How V7 gives AI agents institutional memory

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.

With context, models, and tools working together, V7 says agents complete 50–100 step workflows in minutes, reaching 99.9% accuracy, while maintaining an auditable trail of every decision made.

“To solve hard enterprise use cases across finance and insurance, AI needs to learn how your business operates just as well as it learned from the Internet.”

—Alberto Rizzoli, Co-Founder and CEO at V7

Giving agents the context to understand the whole business

That shared context is already speeding up document-heavy work across V7’s customers:

—Simon Edwardsson, Co-Founder and CTO at V7