How V7 gives AI agents institutional memory

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 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.

When data arrives, V7 Go connects to repositories such as SharePoint and Google Drive, scans them for entities, relationships, facts, attributes, and metrics, and populates a graph that’s an order of magnitude cheaper and faster to traverse than long-context approaches.

The Context Graph gives agents a structured, up-to-date record they can query directly. When a new file arrives, V7 Go identifies the companies, funds, people, or any entity in an ontology, then connects each fact to a new or existing record, and preserves cited evidence to the original source. If the graph does not contain enough information, V7 Go can still search the underlying documents with RAG.

V7 has also tested how much the structure of that context matters. On HERB, a benchmark for finding and connecting information spread across enterprise systems, V7’s retrieval-only system outperformed the official baseline by 69% and reduced hallucinations on un-answerable queries by 38%. V7 Go uses that source-linked context to keep complex workflows grounded in each company’s own information.

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.

The Context Graph makes it so that a model can work with a firm’s history without relearning it each time. For long-running agents, V7 Go keeps recent exchanges in the model’s active context and stores older material in the graph to be retrieved when needed.

That shared context is already speeding up document-heavy work across V7’s customers:

Complex, multi-step workflows depend on a model reliably following lengthy instructions, running tools, interpreting results, and navigating a long series of steps. A single upstream error can lead to expensive consequences and broken trust in AI systems. V7 Go guides models across long horizon tasks spanning deterministic code, handovers to smaller models, file generation steps, and integrations, with an auditable trace of every run.

In AI-generated workflows, V7 Go maps each step to fast, medium, and smart tiers. GPT‑5.6 Luna handles structured extraction and other high-volume work, and GPT‑5.6 Terra or Sol power chat, the Go Agent path, and steps that require more reasoning or tool use.

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.

In V7’s latest harness, key workflows with several external calls now finish up to 50% faster. V7 measured another efficiency gain with GPT‑5.6 Luna: a 78% lower cost per document than with GPT‑5.4 mini. V7 also moved its document-heavy V7 Go workloads from the Chat Completions API to the Responses API. In its testing, the change reduced token use by roughly 5% for some PDF-heavy workflows and improved caching reliability.

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 already exposes Context Graph querying and ingestion through its MCP server, so customers can use it from ChatGPT and other compatible clients. They can also create V7 Go workflows through MCP in Codex. Together with simpler workflow design, this has reduced the time required to create a medium-length workflow from around one hour to about 20 minutes.

V7’s longer-term goal is to make that shared memory more proactive. The team is working toward workflows that start when facts in the Context Graph change, flag inconsistencies, and show people which analyses need another look. A restated fund report, for example, could prompt V7 Go to flag work that still relies on the old figures.

“Our goal is to help enterprises re-tool for the age of AI, with workflows that solve mission critical tasks, and memory that outperforms us humans” says Rizzoli. “Finance firms getting real value from AI will not be the ones with the most agents. They will be the ones with the best context.”