Data sovereignty isn't a feature. It's the architecture.
Randoli was built so raw observability telemetry never has to leave the environment it came from. That same principle extends to AI: Raiya lets you choose which model provider handles reasoning, under an account you already control, rather than handing your data to a backend you didn't pick.
See how the architecture enforces this →Sovereignty was the starting design, not a retrofit.
Logs, traces, and metrics carry more than metrics, they carry customer identifiers, credentials that slipped into a log line, and the shape of proprietary business logic. Handing that to a vendor to store and process is a bigger ask than most teams are comfortable making, regulated industries especially. So Randoli split the platform in two from day one: a data plane that runs and processes telemetry inside your own environment, and a control plane that receives only the derived signals and correlation results that come out the other side. See the mechanics of that split on Architecture.
AI needs more of your data, not less.
A dashboard shows you your data. An AI assistant reasons over it, and the more capable the assistant, the more of your operational data it needs to see to correlate a failure and get root cause right. Most AI-powered observability copilots solve that by centralizing your telemetry in the vendor's own cloud, where their model can reach it. That's a different, larger sovereignty question than a dashboard ever raised: it's not just where your data is stored, it's whose model gets to reason over it, and where.
Raiya applies a different answer: correlation and detection run entirely inside your environment, and for the reasoning step, you choose which model provider handles it, AWS, Azure, Google, Anthropic, or OpenAI, using the account and contract you already have, not a relationship Randoli brokers on your behalf. A fully self-hosted model option is coming, for teams that want inference to stay inside their own environment too. See the full capability on SRE Agent (Raiya), or the buyer-facing case for AI observability specifically on Sovereign AI Observability.
Not just AI. Every signal, every capability.
AI is the sharpest current example, but it's an application of a principle that already runs under everything else on the platform.
- ✓Observability: logs, traces, and metrics are processed where they're generated, not shipped out for indexing first
- ✓Cost: attribution runs against your cost and usage data in place, not a copy exported to a billing service
- ✓Security posture: CVE and CIS scanning runs in-cluster, against the workloads it's scanning
- ✓AI: correlation runs locally, and you choose which model provider handles the reasoning step, not Randoli