When documents are scattered across repositories, you may have to ask AI to find the same company information again for each task. V7 describes V7 Go as connecting to repositories such as SharePoint and Google Drive, extracting information about companies, people, funds, and their relationships from documents, and organizing it into a “Context Graph.” For organizations that repeatedly review connected documents, the key feature is the ability to examine where the AI gets the information behind its answers. Source

The important point is that building a graph does not mean discarding the source documents. V7 Go links organized facts to records while preserving citations to the originals. If the graph does not contain enough information, it can search the source documents using retrieval-augmented generation (RAG). An adoption decision therefore depends not only on how quickly it produces an answer, but also on whether you can verify the evidence behind that answer. Source

Before You Start: Choose the Sources and Workflows

First, decide which repositories to connect and which recurring tasks to address. V7 describes documents in repositories such as SharePoint and Google Drive as inputs. It extracts not only entities, but also facts, attributes, metrics, and the relationships among them. Identify where the material resides and which tasks will reuse it so you can check whether the resulting graph contains the information those tasks require. Source

V7 gives private equity deal screening and insurance underwriting as examples. These are examples of potential applications, not a claim that connecting a repository automatically completes every review. Rather than handling all materials at once, pair a set of sources with a recurring review task and examine the result. That approach aligns with the workflow described in the source material. Source

Step by Step: From Documents to a Cited Graph

V7 describes a sequence of connecting repositories, extracting information from documents, building the Context Graph, and querying it. Extracted facts are linked to new or existing records, and citations to the original documents are retained. When reviewing an AI conclusion, users should check which document supports a fact rather than accepting only the resulting statement. Source

Once the graph is built, it can be queried as an information source for recurring work. In V7’s demo description, V7 Go extracts financial information, deal terms, and management information from a confidential information memorandum (CIM), then drafts a screening memo with source citations for risk items. The output to check is not just a one-page summary: look for missing information required by the task and whether each cited passage supports the associated risk assessment. Source

When Information Is Missing or a Conversation Runs Long

If the graph lacks the information you need, V7 Go can search the original documents using RAG. If an answer is missing or poorly supported, do not simply repeat the graph query. Check separately whether the information exists in the connected sources and whether the search results are relevant. Do not treat a fact that has not been organized in the graph as though it has already been verified. Source

For long-running work, V7 says recent conversation stays in the model’s active context, while older material is stored in the graph and retrieved when needed. V7 Go also provides graph-query and collection capabilities through an MCP server. Customers can use it with compatible clients such as ChatGPT or build workflows through Codex’s MCP. To connect it to tools you already use, first check client compatibility and the graph access required for the workflow. Source

Evaluate Results: What to Check Before Speed

When evaluating work product, start with citation accuracy, extraction quality across document types, answer accuracy, and whether instructions were followed. V7 says its model-selection benchmarks include these measures, along with latency, cost, and real enterprise tasks. Even when using the same graph, the deciding factor is whether the information needed for your own document types and questions is extracted correctly. Source

V7 reports shorter deal-screening and review times and fewer insurance-claim processing errors in its customer examples. These results, as well as the model performance and cost figures it presents, come from V7’s own tests or customer cases. The supplied material does not provide independent verification or the full testing methodology. Rather than assuming your organization will see the same results, compare citation errors, omissions, processing time, and costs in your actual workflows. Source

Pre-Adoption Checklist

Before implementation, choose the repositories and scope of work, and establish how you will check source citations in the output. During use, check whether a missing answer in the graph leads to a search of the original documents. Afterward, review error rates, processing time, costs, and execution logs at each step. The key is to distinguish AI that appears to have found information from a system that reliably reuses information in recurring work while preserving its evidence. Source