Self-hosted AI agents that auto-resolve Kubernetes monitoring alerts — running entirely inside your infrastructure. No data leaves your VPC. No more 3am pages for pod crash loops.
Pod crash loops. OOM kills. Disk pressure. Certificate expirations. The same alerts, the same runbooks, the same 3am pages — month after month.
70%+ of on-call alerts follow the same pattern. Your best engineers spend their nights running the same playbooks instead of building features.
In regulated industries — fintech, health, enterprise — you can't send infrastructure data to external AI services. Compliance says no.
Traditional automation follows static scripts. When the failure mode changes slightly, the runbook breaks and a human gets paged anyway.
Three capabilities that make the platform fundamentally different from static automation.
AI agents monitor your Kubernetes clusters, detect anomalies from your existing monitoring stack (Grafana, Prometheus, Datadog, PagerDuty), and automatically diagnose root cause — not just symptoms.
Agents execute remediation actions — scaling pods, restarting services, clearing disk, rotating certificates — starting in supervised mode where your team approves every action until trust is established.
Unlike static runbooks, our agents use a self-learning memory system. They consolidate lessons from every incident into reusable mental models — getting genuinely smarter over time, not just bigger.
Everything runs inside your infrastructure. Self-hosted LLMs, your cloud, your rules.
YAML-defined agents deployed via GitOps. Same change management you already use. Agents are version-controlled, auditable, and reviewable.
Zero data sent to OpenAI, Anthropic, or any external API. LLMs run on your GPU nodes inside your VPC. PCI-DSS, HIPAA, SOC2 compatible.
Prompt guardrails enforced at the gateway layer. Every agent action logged with full audit trail. Rate limiting, content filtering, and access control built in.
World Facts, Experience Facts, Observations, and Mental Models. Our memory system outperforms traditional RAG — agents consolidate knowledge, not just retrieve it.
No rip-and-replace. Plugs into the tools your team already uses.
Built for regulated industries where data sovereignty isn't optional.
Zero data sent to external AI providers. Models run on your GPU nodes in your VPC.
Every agent action is logged, timestamped, and attributable. Complete visibility for compliance.
Agents propose actions, humans approve — until your team is confident enough to enable autonomous mode.
Deployed via ArgoCD with the same change management, PR reviews, and rollback capabilities you already use.
If your MTTR doesn't improve by 40% or more within 60 days, you get a full refund. No questions asked.
Schedule a Demo →Proven at a Fortune 1000 company • Running in production for 6+ months