v2.0 Now Available

Predict Incidents
Before They Happen

AI-powered AIOps platform that detects anomalies, predicts outages, and automatically remediates issues before they impact your users.

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1 + 4
Default + scoped agents
5
RPL policies
8
Plugins seeded
SHA-256
Hash-chained audit
app.obvia.io/dashboard

Adapters in the catalog

DatadogPrometheusOpenSearchPagerDutySlackGitHubKubernetesincident.io

Plugin manifests declare ingestion + action capabilities; runtime activation is credential-gated per provider runbook.

Core Capabilities

Everything you need to stay ahead of incidents

From prediction to remediation, Obvia provides a complete toolkit for proactive operations management.

Pattern detection

Threshold, error-rate, and trend detectors fire on telemetry from your existing observability stack. Calibration histogram + confidence scoring keep over-confident detectors out of the queue.

Governed remediation

Workflows graduate draft → approved → auto as eval scores accrue. Every trigger evaluates the active RPL policies before any executor mutation runs.

Blast-path topology

Recursive-CTE walk over the service dependency graph surfaces every downstream service an incident will touch, ranked by criticality, before approval.

Plugin marketplace

Demo seed ships Datadog, Prometheus, OpenSearch, Slack, PagerDuty, GitHub, Kubernetes restart/scale, and incident.io. MCP-server plugins extend the agent tool surface at runtime.

Runtime Policy Language

RPL gates auto-execution by confidence threshold, blast-radius cap, service scope, and environment. Production-remediation-requires-approval is the default pilot gate.

Multi-agent fleet

Pipeline-scoped agents (Conway's-Law mapped) own slices of the topology. Each carries its own tool allowlist + scope filter; the default agent catches anything unscoped.

How It Works

From chaos to control in minutes

01

Connect your stack

Install source plugins (Datadog, Prometheus, OpenSearch, …) and Obvia starts reading telemetry. No new agent to deploy; we sit on top of the tools you already pay for.

02

Define governance

Author RPL policies that gate every workflow trigger. Set confidence thresholds, blast-radius caps, and approval routing per service or environment.

03

Promote with evidence

Workflows ship as draft, run in shadow + dry-run modes, and only graduate to auto-execution after the eval harness clears them. Operators stay in the loop until the loop is proven.

Ready to predict the future?

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