V7 Launches Institutional Memory for AI Agents with GPT-6 Astra

V7, a startup founded in 2018, has launched V7 Go, an agentic platform that uses GPT-6 Astra to provide AI agents with institutional memory, achieving 89% accuracy on its hardest graph-query tests. 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. 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.

Alberto Rizzoli, Co-Founder and CEO at V7, stated, “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.”

V7 Go is already speeding up document-heavy work across V7’s customers:

• Asset managers can screen deals 21x faster than before, reducing a full-day process to just 15 minutes.
• A financial services team cut review time from more than 100 hours to under 10, saving $12,000 in expert costs per task.
• Insurance teams reduced errors in claims processing by 13.5% compared to a manual baseline, after granting their agents historical knowledge of all previous claims and existing policies.

Simon Edwardsson, Co-Founder and CTO at V7, added, “With GPT-5.6 Terra, we have been able to remove many intermediate workflow stages that previously existed only to simplify the task for the model. It’s saved us days of delivery work and often gets things right on the first build of a workflow, thanks to a stronger model and access to more context.”

Source: openai