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.
