V7 cuts costs 78% while boosting accuracy with GPT-5.6 Luna

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 that organized context in workflows such as private equity deal screening and insurance underwriting. In the demo below, a workflow extracts information from a deal document called a Confidential Information Memorandum (CIM) feeds key financials, deal terms, management details, and cites risk fields before V7 Go produces a screening note.

V7 tests new models against a continuously maintained benchmark suite covering citation accuracy, extraction quality across hundreds of document types, answer correctness, instruction following, latency, cost, and real-world enterprise workflows. OpenAI outperforms most models on the behaviors V7 cares about most. OpenAI also approved and implemented V7’s capacity increase needs within hours, compared with weeks for other providers V7 uses.

V7 also tested GPT‑6 Astra on its most difficult queries. GPT‑5.6 Sol had saturated many of V7’s existing benchmarks, so the company created a more challenging set of graph-query questions using messier, real-world data across thousands of documents. The test’s dataset spans four difficulty levels. On the very-hard level, V7 reports that GPT‑5.6 Sol scored 78%, while GPT‑6 Astra scored 89% accuracy. Both models scored close to 100% on the easy, medium, and hard levels.

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.

The Context Graph solves a specific problem. Agents have to rediscover context on every request, leading to dozens of searches costing time and tokens, and often missing key information buried in relationships.