AI-powered operational memory for enterprise infrastructure support teams. Surface relevant historical fixes when a new incident opens.
No credit card · 1 GB storage · 0.5 GB ingest · 0.1 GB embedding · 10 TB scan · 1 connector
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 beta sources such as Jira, Slack, Teams, Confluence, PagerDuty, Datadog, GitHub, and ServiceNow. Access is read-only by default, with real-time webhook ingestion available where configured.
AnvaiOps ingests your historical tickets, threads, and runbooks. We extract RCAs, fixes, and owning teams using AI — then store them as a searchable knowledge index.
When a new incident opens, engineers search in natural language. AnvaiOps returns relevant historical fixes, root causes, workarounds, and owning-team context with supporting metadata.
Built specifically for the complexity of enterprise infrastructure support — not generic helpdesk AI.
Natural language search across every ticket, thread, and runbook. Hybrid retrieval finds related incidents even when you don't know the exact terminology.
Hybrid retrieval with intelligent re-ranking. Low-latency results with full metadata filtering across large document corpora.
LLM-assisted extraction can turn root cause, fix, workaround, and affected components from ticket text and Slack threads into structured, searchable knowledge.
Incidents, services, runbooks, teams, and engineers can be linked as structured relationships, giving searches more context than keyword matches alone.
Search across structured metadata, unstructured text, log lines, document content, and relationship context in a single query path.
Index alert history, metrics anomalies, and PagerDuty escalation context alongside ticket and thread data. Surface correlated signals — not just text matches — when diagnosing active incidents.
Surface clusters of related issues across teams, components, and time ranges so recurring incident patterns are easier to review and prioritize.
Cloud SaaS, private VPC, or on-prem intranet. Three deployment topologies — standalone service, managed process, or in-process embedded — fit isolated or air-gapped environments.
Engineers search directly from Slack with /anvai <query> or mention the bot in Teams when enabled. Results appear inline during an incident, and webhook events can ingest new messages in real time.
AnvaiOps is powered by ProximaDB — an open-source multi-model database that unifies the indexes and data planes our knowledge layer depends on. The engine is public and auditable. The intelligence layer is what we build on top.
One engine for the multiple index types modern knowledge retrieval requires — unified under a single query plane. No separate search infrastructure to operate or pay for.
Server: standalone service — cloud, on-prem, or intranet-isolated. Managed: process lifecycle handled for you. Embedded: single-binary in-process for air-gapped environments. Same engine, three topologies.
Security-conscious enterprise buyers can inspect the open-source engine. No black-box indexing path, no hidden retention layer. Fork it, audit it, trust it.
No migration, no workflow changes. AnvaiOps reads from your tools and makes them smarter.
No search infrastructure to size. Connect your support stack, choose async or sync ingest, and pay for indexed knowledge, retrieval, and outbound transfer.
| Meter | Unit | Rate | What drives it |
|---|---|---|---|
| RetrievalPrimary | per TB scanned | $0.02 | You're billed for the GB of corpus your queries physically read — not the total size of your knowledge base. Intelligent indexing skips blocks that don't match your query filters before retrieval runs. Example: 5,000 queries/mo, 2 GB corpus, 40% scan fraction = 4,000 GB scanned/mo = 4 TB scanned = $0.08/mo. Tighten further by splitting your corpus into multiple named indexes (route runbook queries to your runbook index only) or adding metadata filters (team, severity, date) — both physically reduce scanned bytes. Team includes 30 TB/mo scan budget; Pro includes 300 TB/mo scan budget; Business includes 1 PB (1,000 TB)/mo scan budget. |
| Storage | per GB-month stored | $0.25 | Persistent searchable incident knowledge — indexes, metadata, and replicas in the primary region. Cross-region DR replication is billed only when selected or included by tier. Billed against the actual storage footprint so a 200KB Confluence page is billed fairly against a 20KB Jira ticket. This is managed searchable knowledge, not raw object storage. |
| Ingest / IndexingKIU | per GB indexed | $0.75 BYO vectors or document payload |
Write, validate, index, and compact customer-supplied content or vectors. v1/v2 vector batch APIs accept embedded vectors; v3 documents can also carry vectors through ProximaDB.Included ingest scales by tier from 1.5 GB on Team to 50 GB on Business. Ongoing retention is covered by the storage meter. |
| Managed EmbeddingKEU | per GB embedded | $5.00 $8.25 sync · BGE -25% · large $26.25 / $52.50 |
Optional embedding performed by AnvaiOps when you send documents without vectors. Async uses batched OpenAI text-embedding-3-small by default; hosted BGE models are available at 25% below the comparable OpenAI lane. Sync is available on Pro and above for immediate searchability. Small managed embedding allowances are included by tier. Native document ingest is $5.75/GB async when KIU + OpenAI KEU are used together, or $4.50/GB with hosted BGE. OpenAI text-embedding-3-large is available as a premium managed embedding option. Teams that bring precomputed vectors skip KEU. |
| Extra Connector | per connector / mo | $39 | Beyond the included connector count. Each adds a scheduled polling worker (every 2–12 hours depending on source). Real-time Slack and Teams webhook ingestion is included in the platform fee — not a per-connector charge. All connectors included on Enterprise. BYOC available for regulated or on-prem sources. |
| Network Egress | per billable GB | cloud nominal rate | Outbound transfer for result exports, cross-cloud access, on-prem access, and selected DR replication. Provider free allowances and same-cloud paths are reflected in the calculator. Network transfer is tenant-specific even when compute is pooled. |
Introductory rates use pooled compute to keep entry pricing low. Tenant data remains isolated and is billed through simple GB-based meters: retrieval (GB scanned), ingest/indexing (GB indexed), managed embedding (GB embedded), and storage (GB-month). No pass-through cloud bills. Dedicated Enterprise deployment starts at +$1,500/mo when isolation, private networking, or customer-controlled cloud is required.
Our support team uses AnvaiOps the same way your engineers will. Every ticket we resolve for a customer gets indexed back into our own instance, so our institutional knowledge compounds with each interaction. That lets us include support in the platform fee while keeping response quality high.
Try AnvaiOps with 1 GB indexed storage, 0.5 GB ingest, 0.1 GB managed embedding, 10 TB scan budget, and one connector. If incident knowledge loss is a real pain, we can move you to a paid plan after the trial.