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AnvaiOps

The memory layer for AI agents. Give your agents durable, governed memory — incident history and code knowledge in one open-source engine, managed in your cloud.

Built on the open-source ProximaDB context database, managed with tenant isolation, usage metering, and MCP built in.

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What it does

Your engineers — and your agents — re-solve the same incidents every week. Tribal knowledge lives in people's heads and buried Slack threads; when they leave, it evaporates. AnvaiOps connects every incident to its resolution and surfaces it in real time.

flowchart LR
    SRC["Your sources<br/>Jira · Slack · Confluence · PagerDuty · GitHub"]
    ANV["AnvaiOps<br/>ingest · normalize · govern · meter"]
    ENG["ProximaDB engine<br/>hybrid vector + BM25 + graph"]
    OUT["Agents & engineers<br/>MCP · search · code memory"]
    SRC --> ANV --> ENG --> OUT

Why teams use it

Outcome Before After
Time to resolve an escalation 4–8 hours 30–90 minutes
Duplicate investigations 30–40% < 5%
New engineer ramp 3–6 months 4–6 weeks

Principles

  • Read-only by default. Connectors observe your sources; they never write back.
  • Your cloud, your data. The engine runs in your cloud region — data never egresses through our control plane.
  • Open and auditable. The core engine is open source; you can inspect exactly what runs.
  • Honest capabilities. Every feature carries a maturity status that never claims more than the engine actually supports.

Where next

  • Getting started

    Connect your first source and run your first search.

    Start here

  • Integrations

    Jira, Slack, Zendesk, Confluence, PagerDuty, GitHub, and more.

    Browse connectors

  • Deployment

    Managed, bring-your-own-storage, or fully in your cloud.

    Deployment models

  • Built on ProximaDB

    The open-core engine and where the commercial layer begins.

    Open-core boundary