AI-powered operational memory for enterprise infrastructure support teams. Surface exact historical fixes the moment a new incident opens.
No credit card · White-glove onboarding for design partners
Designed for technical support teams in these categories
Enterprise infrastructure support teams lose weeks per quarter to tribal knowledge loss and repeated investigations.
The same Kafka lag spike, Postgres connection pool exhaustion, or Kubernetes OOMKill gets investigated fresh every time it recurs — because the RCA from six months ago is buried in a comment thread nobody can find.
Your best engineers carry institutional knowledge no one else has. When they leave, go on vacation, or join a different team — that knowledge evaporates.
New engineers escalate everything because they don't know what's been solved before. Senior engineers spend hours on issues they've already fixed three times.
AnvaiOps connects to your existing tools and surfaces the right fix at the right moment — no workflow changes required.
Authorize AnvaiOps to read from Jira, Slack, Zendesk, Confluence, PagerDuty, and GitHub. Read-only access — we never write to your tools. Setup takes under 30 minutes.
AnvaiOps ingests your historical tickets, threads, and runbooks. We extract RCAs, fixes, and owning teams using AI — then store them as searchable vector embeddings in ProximaDB.
When a new incident opens, engineers search in natural language. AnvaiOps returns the exact historical fix, root cause, workaround, and owning team in under 100ms.
Built specifically for the complexity of enterprise infrastructure support — not generic helpdesk AI.
Natural language search across every ticket, thread, and runbook. Vector similarity retrieval — finds related incidents even when you don't know the exact keywords.
Powered by ProximaDB's hybrid vector + BM25 search. Results in under 100ms across millions of indexed documents with full metadata filtering.
GPT-4o automatically extracts root cause, fix, workaround, and affected components from unstructured ticket text and Slack threads.
Identify recurring incident patterns before they become P0s. Surface clusters of related issues across teams, components, and time ranges.
Cloud SaaS, private VPC, or fully air-gapped on-prem. Built on ProximaDB's server, SDK, and embedded modes to meet every enterprise security requirement.
Engineers search directly from Slack with /anvai <query>. Results appear inline — no context switching during an incident.
AnvaiOps is powered by ProximaDB — an open-source vector database built for hybrid search, graph traversal, and multi-modal retrieval. The engine is public and auditable. The intelligence layer is what we build on top.
ProximaDB runs both semantic vector search and keyword search in a single query, re-ranked with Reciprocal Rank Fusion. No separate search infrastructure needed.
Server, SDK, or fully embedded (in-process via PyO3). AnvaiOps runs ProximaDB in embedded mode for air-gapped enterprise deployments — zero network surface.
Security-conscious enterprise buyers can read every line of the database engine. No black-box indexing, no hidden data retention. Fork it, audit it, trust it.
No migration, no workflow changes. AnvaiOps reads from your tools and makes them smarter.
Every tier includes white-glove onboarding. We don't succeed unless you do.
We're opening 5 design partner spots. If you run a technical support engineering team and MTTR is a real pain — let's talk.