ENTERPRISE CONTROL PLANE · NO MIDDLEWARE LOCK-IN
Run your entire operation with clarity and control. Judgment, action, verification, rollback and evidence — everything after detection — is owned by a single engine.
01 / PRODUCTS
The control plane for operational signals and the control plane for user queries run on the same models, policy and evidence.
AUTONOMOUS OPERATIONS PLATFORM
Predict operational risk, act under approval
An AI control plane between your observability stack and your execution tools. It adds prediction, judgment, action and evidence with no additional collector.
CONVERSATIONAL CONTROL PLANE
One question reaches every system you run
A conversational control plane between people and enterprise systems. Users do not learn the system; the system understands the conversation.
02 / PRINCIPLES
Instead of showing you more, it decides what comes next and closes the action safely.
Forecasting and unsupervised anomaly detection surface risk before a threshold is crossed. A rule-based risk score runs alongside the ML models, so it operates from day one.
The policy engine decides what is permitted and what needs approval; the workflow engine carries execution, verification and rollback. A failed action is always reverted.
Every rationale and approval from detection to closure is written to immutable storage, with audit and periodic operations reports generated automatically.
03 / SHARED ENGINE
Target systems are swapped by driver while the engine is reused unchanged. No vendor and no middleware is assumed.
Forecasting, judgment, inference, learning and quality monitoring are split across models, each deployed as an independent endpoint.
Policy managed as code decides automatic-execution permission, approval requirements, attempt budgets and cooldowns.
Hash-chained audit logs and per-incident snapshots are stored immutably, with reports published automatically.
Implement resource discovery, metric collection, action execution and schema sampling, and a new target system attaches.
04 / ENGINEERING
Forecasting, anomaly verdicts, causal inference, reinforcement learning, federated learning and drift detection are split across independent models. The policy and workflow engines carry action, verification and rollback through a state machine, and every rationale is written to a hash-chained audit log.
05 / ADOPTION
Automated action is never on from day one. Each phase is switched by a feature flag and returns to the previous phase immediately if anything goes wrong.
Automated action stays off while prediction and anomaly detection are observed.
Every action passes human approval while rollback paths are verified.
Actions with a proven success rate begin running without approval.
Drivers and connectors extend the reach into adjacent systems.
