V7 Gives AI Agents Company Memory, Lifts Hard-Query Accuracy to 89% With GPT-6 Astra
Updated
Updated · OpenAI · Sep 21
V7 Gives AI Agents Company Memory, Lifts Hard-Query Accuracy to 89% With GPT-6 Astra
1 articles · Updated · OpenAI · Sep 21
Summary
V7 said its V7 Go platform turns company files into a “Context Graph” that lets AI agents query institutional memory, with GPT-6 Astra scoring 89% on the hardest graph-query tests across thousands of documents.
GPT-6 Astra beat GPT-5.6 Sol’s 78% on V7’s very-hard benchmark, while both models were near 100% on easier levels, underscoring that the gain shows up mainly on messy, real-world enterprise queries.
V7 uses GPT-5.6 Luna for extraction and GPT-5.6 Terra or Sol for reasoning and tool use, saying agents can complete 50-100 step workflows in minutes with 99.9% accuracy and an auditable trail.
Customer examples cited by V7 include deal screening 21x faster, a review process cut from 100-plus hours to under 10, and insurance claims errors reduced 13.5% versus a manual baseline.
The platform already exposes its Context Graph through MCP for ChatGPT and Codex, as V7 pushes toward workflows that trigger automatically when underlying business facts change.