Traditional CloudOps and SRE, redesigned from first principles with AI as the force multiplier. One operations team — covering CloudOps, DataOps, MLOps, and AgentOps — running end-to-end on a 24×7 model.
The traditional managed-services model is broken. Tickets, ladders, status pages, and pretend RCAs — work designed around headcount, not outcomes.
AI changes the geometry of operations. The cost of intelligence has collapsed; the cost of missed signals has not. The right operations team is small, senior, and force-multiplied — not large, junior, and rotational.
We rebuilt operations the way it should have been built: one team, four disciplines, AI inside the loop, accountable to consequence not to throughput.
A unified platform operations practice — staffed by senior engineers, run end-to-end, on the same SLAs, on the same dashboards, on the same incident bridge.
Multi-cloud reliability, cost guardrails, patching, incident response. Followed by AI-assisted RCA and postmortems your engineers will actually read.
Pipeline reliability, data quality SLAs, freshness monitoring, schema evolution. Data downtime treated like service downtime — with the same severity model.
Model lifecycle, deployment, drift, retraining, evals, safety. The operational discipline that turns experiments into production systems — and keeps them honest.
Tool routing, guardrails, observability, cost ceilings, replay, and human escalation paths for autonomous and semi-autonomous agents. The discipline AI ops will become.
Every loop in the operations cycle has been reimagined with AI inside it — not as a productivity tweak, but as the new substrate of how the work is done.
Follow-the-sun coverage from our two engineering hubs. Severity-based SLAs, AI-augmented runbooks, and transparent postmortems for every Sev-1.
AI-correlated alerts cluster into a single incident. On-call paged. Bridge auto-opened. Initial impact estimate posted.
AI-suggested first hypotheses against logs, traces, metrics, and recent changes. Mitigation applied; customer impact contained.
Root cause confirmed, fix shipped, postmortem drafted by AI and edited by humans, runbooks updated, detection improved.
Operations is no longer a back office.— Platform Operations at Revoleap
It is the front line of an AI-native firm.
A 90-minute readiness review with our operations principals — coverage gap analysis, SLO maturity, AgentOps preparedness.
Begin a conversation— The cycle runs across both engineering hubs, twelve hours apart, so the moment a production AI behaviour drifts is the same moment an on-call engineer is awake to mitigate it. Most "AI in production" stories end at Deploy. Ours don't.
