Production environments should defend themselves.
We are building the control plane that stops bad changes at the gate, contains what gets through inside 1% of production traffic, and reverts the rest. It runs inside customer VPCs today.
We are engineering a future where enterprise software scales at high velocity without compromising production stability.
The proliferation of AI generated code has accelerated development, but it has critically exposed the lack of standardized deployment safety. Our vision is to make autonomous remediation the default for every engineering team, ensuring that AI-driven deployment velocity does not result in catastrophic production failures.
Build an autonomous AI SRE for enterprise infrastructure, and take the coordination tax out of every incident.
Our mission is to drastically reduce Mean Time to Resolution by eliminating the reactive coordination tax. A SEV0 mitigates in about an hour because it gets a war room: an incident commander, and every engineer who could help pulled off whatever they were doing. A SEV3 pages someone too, but it pages one on-call engineer who works it between their other commitments, and it commonly runs six to eighteen hours as a result. The gap between those two numbers is not technical difficulty. It is how much attention the label buys you. Both burn the same first fifteen minutes on context gathering and coordination; only the severe ones are ever worth staffing a swarm for. That coordination tax is the part we remove.
Odonat AI exists to shift reliability left. We intercept faulty deployments at the CI/CD gate, enforce strict blast radius containment during traffic rollouts, and execute deterministic, sub-minute remediations for any production anomaly, regardless of what severity a human eventually assigns it. The regressions we catch are caught before they merge or inside 1% of traffic, and the ones we cannot catch are listed in full on the home page. We would rather be held to that than to a guarantee.
By combining deep observability telemetry, causal reasoning, and autonomous execution capabilities, our control plane perceives its environment and resolves multi-step reliability tasks independently. The goal is to make continuous uptime an engineering property rather than an aspiration, protect the revenue that downtime costs, and free engineering teams from on-call firefighting so they can focus strictly on building products. We will not promise uptime we do not control. The boundaries of what Odonat can and cannot catch are published in full on the home page.

Navdeep Dahiya
Fifteen years improving the reliability of AI-native distributed systems at scale. Staff Engineer at Meta, building and running the platforms whose 3 AM pages Odonat is designed to eliminate.
Odonat exists because that experience kept producing the same conclusion: almost every severe incident was preventable at a gate that nobody was watching, and almost every minute of recovery was spent re-deriving context a machine already had.
linkedin.com/in/navdeepdahiyaPut it in front of your own production traffic.
Odonat installs into your VPC in under an hour. Zero inbound ports, zero code retention, and a Shadow Replay of your real requests before anything ships.
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