Shipping is the beginning, not the end. We monitor drift, cost, and reliability continuously, respond to incidents against a defined runbook, and report monthly — so quality doesn't quietly decay after the team that built it moves on.
Monitoring, runbooks, and reporting — the full system that keeps a production AI system healthy after launch.
Ongoing evaluation catching accuracy decay before users notice, not after complaints.
Weekly spend tracked against a budget model, with alerts before overruns and recommendations to cut waste.
Defined detection, escalation, and response steps with a named engineer and an SLA.
A clear, leadership-readable report on uptime, cost, quality, and incidents.
Periodic re-evaluation as your data, usage, and the underlying models change.
The monitoring topology and a real alerting pattern — not a slide about “AI operations.”
// elhaa Operations Monitor — Alert Rule const rule = elhaaOps.defineAlert({ metric: 'eval_pass_rate', threshold: 0.93, window: '24h', onBreach: 'page-oncall', runbook: 'accuracy-degradation-v2' });
An internal AI system was running in production with no monitoring beyond “users will tell us if something breaks” — and inference costs had crept up 40% over two quarters unnoticed.
We audited the system, stood up drift and cost monitoring wired to Slack alerts, documented incident runbooks with the internal team, and began monthly reporting with a quarterly re-audit cadence.
*Illustrative example based on a representative engagement.
Assess what's currently monitored, what isn't, and where the real risk sits.
Stand up drift, cost, latency, and error dashboards wired to your alerting channels.
Document incident response steps and agree response-time commitments.
Ongoing monitoring, monthly reporting, and quarterly reviews as the system evolves.
Tracked against an agreed SLA, not just “it seemed fine.”
Weekly tracking against a budget model, with alerts before overruns.
Accuracy decay caught by ongoing evaluation, not by user complaints.
Measured against the agreed runbook response-time commitment.
No — we take on AI systems built in-house or by other vendors. The operational audit at the start tells us exactly what we're inheriting before we commit to an SLA.
Pilot to Production is a one-time hardening engagement. Managed AI Operations is the ongoing care afterward — many clients do both, in sequence.
The agreed runbook defines detection, escalation, and response steps, with a named on-call engineer and a response-time commitment — not an ad-hoc scramble.
Yes — it's a retainer, not a lock-in. We document everything so an internal team can take over cleanly whenever you're ready.
A monthly retainer scoped to the number and complexity of systems under management, agreed upfront with no surprise overages.
A 30-minute call. We'll tell you honestly whether this is the right solution — and what it would take.
A short form, then a 30-minute call. We reply within one working day.
We'll be in touch within one working day.