AIOps
Answers failure mode 2 — no one can see what changed.
The model backbone, governed by default.
One layer for the whole model lifecycle — train, serve, monitor, govern — on infrastructure you control, with the audit trail built in, not bolted on.
The second reason AI stalls is that when a model or agent acts, no one can see — or prove — what changed. AIOps is the operations layer that makes every training run, deployment, and drift event observable and logged.
The visibility problem
Ungoverned automation is a liability, not a feature.
Gartner expects 40%+ of agentic-AI projects cancelled by 2027 — cost, unclear value, weak controls.
Most teams stitch separate tools for training, serving, monitoring, and governance, and the seams are where things break and where accountability disappears. "Agent washing" — selling autonomy with no way to audit it (Gartner, 2025) — makes it worse. AIOps brings the lifecycle under one roof so every step is observed and every action is on the record. 64% of firms over $1B have already lost more than $1M to AI-related risk (EY, 2025).
How it works
Frame, train, serve, monitor, governance over every stage.
What monitoring means
Drift detection, alert thresholds, and automatic re-entry into Frame when drift exceeds the threshold — the governed step that closes the loop.
One lifecycle. Governed at every step.
What you get
Capabilities, not a feature list.
Problem framing
Turn a goal into a trainable spec.
Automated training
Find a working model without months of custom engineering.
Serving
Deploy under one roof.
Monitoring
Catch drift and cost early.
Orchestration
Pipelines that hold.
Governance + audit
Every action on the record.
What's working, and what's maturing
We market the flows that run, and name the rest.
Problem framing, real automated-training engines, and the governance-and-audit layer run today. Monitoring depth, gateway routing, and the operator console are maturing; the model-merge and serving marketplace and fine-tuning flows are on the roadmap. We label them that way on purpose — an operations layer you can't trust the status of is not an operations layer.
Live today
- Problem framing
- Automated training engines
- Governance + audit layer
In progress
- Monitoring depth
- Gateway routing
- Operator console
On the roadmap
- Model-merge + serving marketplace
- Fine-tuning flows