Managed AI operations is the ongoing work of keeping AI systems reliable in production: monitoring behaviour, evaluating accuracy against real cases, managing model and prompt changes, controlling cost, and producing the evidence your auditors ask for. It’s the part of enterprise AI that nobody demos and everybody eventually needs.
Why AI systems decay
Traditional software fails loudly. AI systems fail quietly, and for reasons that have nothing to do with your code:
The model changes
Providers deprecate versions and update behaviour. An output that was reliable in March is subtly different in July.
Your data changes
New product codes, new document formats, new branch values. The agent's accuracy drops on cases nobody flagged as new.
Usage drifts
Users find the agent useful for something you never designed it for, and it performs worse there.
Cost drifts
Token consumption grows with adoption, and nobody notices until the invoice.
Nobody owns it
The project team moved on. The agent is running, quietly getting less accurate.
None of these are exotic. All of them are certain.
What we run
Monitoring
Volume, latency, confidence distribution, escalation rate, error rate, cost per transaction. Dashboards your team can read, plus alerting on drift indicators.
Evaluation
A labelled set of your real cases, re run on a schedule and on every change. The only honest measure of whether the agent is still doing its job.
Change control
Prompt and configuration versioning, diffs, staged rollout, rollback. Model upgrades tested against the evaluation suite before production.
Exception oversight
Reviewing what the agent escalated, finding patterns, and either retuning the threshold or fixing the upstream data.
Cost management
Model routing (cheaper models for simpler tasks), caching, prompt efficiency, and a monthly cost report against volume.
Compliance reporting
Audit log integrity, access reviews, decision trail exports, and the periodic evidence pack for RBI, SEBI or IRDAI examinations.
Plans
| Standard | Enterprise | |
|---|---|---|
| Monitoring and alerting | Yes | Yes |
| Evaluation runs | Monthly | Weekly + on every change |
| Model upgrade testing | Yes | Yes, with staged rollout |
| Named engineer | Yes | Yes, plus backup |
| Exception review | Monthly | Fortnightly |
| Compliance evidence pack | Annual | Quarterly |
Token and cloud costs are yours, paid direct, with no margin from us.
What good operations looks like after six months
Accuracy is flat or improving rather than quietly declining. Cost per transaction is falling as routing improves. The escalation rate is understood, and its causes are being fixed upstream rather than absorbed. And when your auditor asks how a decision was made in April, the answer takes ten minutes.
Common questions
Can you run agents you didn't build?
Yes, subject to an assessment. We need to see the logging, the evaluation approach and the data dependencies before we take accountability for behaviour.
What SLA can you offer on AI accuracy?
We commit to SLAs on availability, response time and evaluation cadence. We don't commit to a fixed accuracy percentage. Anyone promising that without seeing your cases is guessing.
Who is accountable if the agent makes a bad decision?
Accountability stays with you. Our job is to ensure the control design, thresholds and audit trail mean bad decisions are caught, logged and recoverable.
How do you control model costs?
Route by task complexity, cache aggressively, keep prompts efficient, and report cost against volume monthly so growth is a decision rather than a surprise.
Don’t let your agents drift
Talk to our operations team about keeping your AI systems reliable, accurate and compliant in production.
Book a working session