Sentinel is an autonomic triage and remediation layer for production systems. It watches every signal, reasons about failures, applies fixes — and commits each fix as a permanent immunity. Your engineers stop being paged for problems the system already knows how to solve.
Modern infrastructure generates more signal than humans can triage. Auto-remediation tools react to known patterns. Neither remembers. The cost compounds every quarter.
Engineers are paged at 03:00 to apply a fix the system already knew about the last three times it happened. Every fix lives in chat scrollback, a closed ticket, an engineer's head. Tribal memory does not survive turnover.
of alerts in median production clusters are ignored or auto-acknowledged. The few that matter get lost.
for repeat-class incidents at mid-stage companies. Time-to-diagnosis is the largest variable cost.
of incident-response knowledge is irretrievable after twelve months of team turnover.
Sentinel separates the work by tempo: continuous low-cost triage scans every signal, escalates to a reasoning core only when the pattern is novel, and commits each successful remediation as an antibody — a permanent immunity recalled by the triage tier next time.
Sub-second scan of every signal in the production telemetry stream. Recalls antibodies, applies known remediations. Escalates only when the pattern is genuinely novel.
Multi-source diagnosis across logs, metrics, configs, and code. Proposes a remediation plan, verifies it in a sandbox, applies it — and commits the resulting antibody.
Every successful remediation becomes an immunity recalled by Tier 1 on the next occurrence. The system gets faster and cheaper every quarter.
Point Sentinel at a complex legacy environment nobody fully understands anymore, and within hours it is diagnosing, remediating, and learning the local terrain.
It ships with an operational corpus distilled from years of production infrastructure work — generic antibodies for the failures that recur across most environments. Once deployed, it specialises: your config patterns, your failure modes, the knowledge that lives in your senior engineers' heads. The generic baseline becomes a local immunity, custom to your stack.
Arrives pre-loaded with a substantial antibody database covering the failure classes that recur across most production environments. It does not start from zero and does not require months of supervised learning before producing value.
Within the first weeks of deployment, Sentinel learns your specific stack: your config patterns, your failure modes, your tribal knowledge. The generic baseline becomes a specialised local corpus.
No need to migrate, modernise, or rewrite anything before deployment. Legacy systems, undocumented services, post-acquisition estates nobody owns anymore — Sentinel learns them as they are.
Kubernetes·OpenShift·Nomad·bare metal·VMware·Proxmox·RHEL / Rocky / Ubuntu / Debian·SLES·Windows Server·FreeBSD·AIX / Solaris (read-only)·edge / IoT·AWS·Azure·GCP·OCI·on-prem·colo·hybrid·— and whatever else you actually run
Observability vendors show you what broke. Auto-remediation vendors react to a fixed playbook. Sentinel's antibody database is the moat — a growing corpus of fingerprinted failures, verified remediations, and recall triggers that compounds with every incident.
Triggered when a pod fails to mount a Ceph RBD volume because the previous mounter died without releasing the lock. Sentinel verifies no live mounter, releases the lock, retries the mount.
Detected via service connectivity probe failures from a specific node subset. Sentinel restarts the k3s-agent unit, validates iptables rule propagation, re-runs the probe.
Common after in-place package upgrades that rewrite ExecStart with literal backslash-space sequences. Sentinel diffs against the healthy peer node, copies the clean unit file, daemon-reloads.
Triggered by ServiceMonitor scrape failures with NXDOMAIN errors. Sentinel cross-references active fleet inventory, prunes stale targets, regenerates the rule manifest.
After a node rotation, the storage CSI driver retains a stale node ID. Volume attachments hang indefinitely. Sentinel deletes the driver pod to force re-registration.
An amd64-only image scheduled on the cluster's arm64 node crashloops with an exec format error. Sentinel patches the deployment with a nodeAffinity exclusion and flags the missing multi-arch manifest.
Sentinel runs against any production stack — Kubernetes, bare metal, hybrid. The reasoning core is hyperscaler-portable. The triage tier runs on commodity inference, including local vLLM for air-gapped deployments.
Claude Haiku 4.5 (triage)·Claude Opus 4.7 (reasoning)·Amazon Bedrock·Bedrock AgentCore·K3s / Kubernetes·Ceph·vLLM (air-gap)·pgvector + NATS JetStream
Sentinel V2 entered production on PureTensor's Trinity cluster on 2026-05-18. The numbers below are real, from the live system. We are not pre-product. We are pre-customer.
Continuous operation since 2026-05-18. Tier 1 scans 4.2M signals a day. Median escalation rate to Tier 2: 0.018%. Median antibody recall hit-rate on familiar patterns: 91.3%.
A managed offering follows once the antibody corpus is portable across customer infrastructures.
Sentinel runs autonomically across the Trinity cluster: Kubernetes, Ceph, bare-metal compute, monitoring tier. The antibody corpus accrues from real operational incidents.
Selective onboarding of three operational design partners running Kubernetes at meaningful scale. Co-engineered antibody portability, shared corpus modes, customer-specific safety policies, and a tight feedback loop on the human-in-the-loop boundary.
Generally available as a managed control plane. Bring-your-own-cloud or fully managed. Antibody corpus federation with cryptographic provenance. Per-incident pricing.
We are taking introductions from operators running Kubernetes or hybrid infrastructure at scale. Architecture deep-dive, a live demo against your incident classes, and design-partner terms.