The Context Graph: Moving Beyond RAG

Most enterprise AI agents struggle because they lack persistent business context, forcing them to rediscover information on every request. V7 Go addresses this with a "Context Graph" that maps entities, relationships, facts, and metrics across disparate systems like SharePoint and Google Drive.

Unlike standard RAG (Retrieval-Augmented Generation) which relies on document-level search, the Context Graph creates a structured, up-to-date record that is faster and cheaper to traverse. When new data arrives, V7 identifies entities within an ontology, links facts to existing records, and maintains citations to original sources. This structure allows agents to maintain an auditable trail of decisions and significantly reduces hallucinations—V7 reported a 69% improvement over baseline retrieval systems and a 38% reduction in hallucinations on unanswerable queries in their HERB benchmark testing.

Optimizing Complex Workflows

V7 Go manages long-horizon tasks (50–100 steps) by mapping different stages of a workflow to specific model tiers based on complexity. High-volume, structured extraction is handled by GPT-5.6 Luna, while reasoning and tool-use tasks are delegated to GPT-5.6 Terra or Sol. For the most demanding graph-query tasks, V7 utilizes GPT-6 Astra, which achieved 89% accuracy on their "very-hard" difficulty tier.

By integrating these models with the Context Graph, V7 has demonstrated significant operational gains:

  • Speed: Asset managers reduced deal screening time from a full day to 15 minutes (21x faster).
  • Cost: Financial services teams cut review time from 100+ hours to under 10, saving $12,000 per task.
  • Accuracy: Insurance teams reduced claims processing errors by 13.5% by providing agents with historical policy and claim data.

Operational Efficiency and Future Proactivity

V7 optimizes its infrastructure by moving document-heavy workloads from the Chat Completions API to the Responses API, which improved caching reliability and reduced token usage by approximately 5%. By exposing the Context Graph via an MCP (Model Context Protocol) server, V7 allows users to query internal business data directly from interfaces like ChatGPT and Codex, reducing the time to build medium-length workflows from one hour to 20 minutes.

Looking forward, V7 is moving toward proactive agentic behavior. The platform aims to trigger workflows automatically when facts in the Context Graph change—for example, flagging existing analyses that rely on outdated figures in a restated fund report.