V7 Go hits 99.9% accuracy on 50–100 step workflows

V7 was founded in 2018 by Alberto Rizzoli and Simon Edwardsson, who had previously built a widely used computer vision accessibility app together. Their pitch is that today's models can reason through complex tasks but do not automatically understand the business context behind them — which fund report is current, how the same entity is named across three systems.

The Context Graph is V7's answer to that gap. It connects to repositories such as SharePoint and Google Drive, scans them for entities, relationships, facts, attributes, and metrics, and preserves cited evidence back to the original source. V7 says this graph is an order of magnitude cheaper and faster to traverse than long-context approaches, and that it can fall back on RAG when the graph lacks enough information. The company's customers include asset managers, a financial services team, and insurance claims processors; V7 reports deal screening 21x faster, a review process cut from more than 100 hours to under 10, a $12,000 expert-cost saving per task, and a 13.5% error reduction in claims processing versus a manual baseline.

V7's model choices also reveal how it weighs cost against capability. It assigns each workflow step to fast, medium, or smart tiers; reports a 78% lower cost per document with GPT-5.6 Luna than with GPT-5.4 mini; and says moving document-heavy workloads to the Responses API cut token use by roughly 5% for some PDF-heavy workflows. On the hardest graph-query set, V7 reports GPT-6 Astra at 89% versus GPT-5.6 Sol at 78%, with both near 100% on easier levels. Whether this holds as customer files change is likely the real test, since the longer-term goal is workflows that start when facts in the Context Graph change and flag analyses still relying on old figures.