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Virgo is an enterprise reasoning graph.

Every pull request, ticket, thread, doc, and meeting becomes a connected node in one graph. Engineers and AI agents trace why work happened and which artifacts prove it.

  • GitHub
  • GitLab
  • Bitbucket
  • Jira
  • Google Docs
  • Google Meet
  • Confluence
  • Notion
  • Slack
  • Teams
  • Figma
  • Agentic workflows
  • Local files
  • S3
  • Notes
  • Custom source

Virgo keeps engineering reasoning in one graph.

Virgo connects work scattered across your engineering stack: code, tickets, conversations, documents, and meetings. The reasoning graph is shared by every permitted team and agent.

Bring your data to Virgo.

Install the Virgo app on the tools your team already uses. Their work enters the graph in real time or on a schedule.

  • GitHub
  • Bitbucket
  • Jira
  • Confluence
  • Notion
  • Google Docs
  • Google Meet
  • Slack
  • Teams
  • S3
  • Local files

Install the Virgo app

Install the Virgo app on the tool and grant it read access.

Push in real time

Real-time pushes keep the graph current with the latest work.

Pull periodically

For tools without webhooks, configure jobs that pull data on a set schedule.

Build the reasoning graph.

As soon as data is available, the pipeline triggers. Beyond storing the data in the graph, it turns raw work into connected, retrievable context.

Entity extraction

Virgo pulls the useful entities out of the data and connects them to the work they came from.

Canonicalization

References to the same thing collapse into one canonical node. The graph stays clean.

Linking

Virgo links entities by the right relationships. Context and causal links established during ingestion connect work across domains into one graph.

Retrieve into your workflow.

Virgo ships a purpose-built retriever for each job, each running a graph-traversal algorithm tuned to its question.

Context retriever

Assembles the connected context around a feature, bug, or decision: the pull request, the ticket, the thread, and the doc that explain it.

Causal retriever

Traces decision lineage: what changed, what triggered it, and what it affected, by following causal edges across the graph.

Learning retriever

Recalls what teams and agents have already learned. Work starts from prior knowledge rather than rediscovering it each time.

webhooks: claude
⏎ send · esc to interrupt

Retrieve into your workflow.

Virgo ships a purpose-built retriever for each job, each running a graph-traversal algorithm tuned to its question.

  1. Assembles the connected context around a feature, bug, or decision: the pull request, the ticket, the thread, and the doc that explain it.

Permission-awareRBAC native

Virgo mirrors the permissions in your connected tools and filters results at query time. People and their agents retrieve only what they already have access to. There is no sync delay or shadow permissions copy.

Extend the graph with the SDK.

The configuration framework extends the graph. Add a datasource, teach Virgo a new relationship, or write a retriever. Use it right away.

Add a datasource

Define a source, its entities, and how data flows in. Virgo backfills it and keeps it current, with no custom pipeline to build.

Custom relationships

Teach Virgo a new edge between entities, and every retriever can traverse it the moment it is registered.

Custom retrievers

Write a traversal over the graph, then ship it on your endpoint for agents to call.

Questions about the platform.

Common questions about how Virgo connects, ingests, retrieves, and extends.

How does Virgo get data from my tools?

Install the Virgo app on the platforms you use. It starts pulling data right away and backfills history in bulk. From then on, webhooks push new work in real time, and scheduled jobs pull from sources that do not support webhooks.

What does the ingestion pipeline do beyond storing data?

Every time data lands, Virgo extracts the entities it contains, canonicalizes them so duplicates collapse into one node, and links them across domains. Context and causal links are established during ingestion, which is what makes retrieval fast and accurate.

How does retrieval work?

Virgo ships purpose-built retrievers for each feature, grounded in current research on graph retrieval. Each retriever runs a traversal algorithm tuned to the question it answers. The graph returns connected context rather than a list of documents.

Does Virgo respect our access controls?

Yes. Virgo mirrors the permissions in your connected tools and filters results at query time, so a person and their agents only ever retrieve what they are already allowed to see. There is no sync delay and no shadow copy of permissions.

Can we add our own data sources?

Yes. The configuration framework lets you define a datasource, its entities, and how data flows in, then start using it right away. No custom pipeline to build.

Can we write custom retrievers or features?

Yes. The SDK lets you create custom relationships, ingestion strategies, and retrieval algorithms on the graph. Writing a new feature is as straightforward as writing a new traversal over the graph.

Is our data used to train models?

No. Your data is query-only, isolated to your graph, and never used to train or fine-tune any model, ours or a third party. Infrastructure controls enforce this policy.

How is our data secured?

Data is encrypted at rest with AES-256 and in transit with TLS 1.3. Every query, retrieval, and agent invocation is logged with who asked and what was returned, and logs export to your SIEM.