The model was the easy part. Month seven is where AI systems quietly stop working.
We build AI systems and we keep them working after they ship. Drift monitoring, prompt regression testing, model deprecation management, and a 24x7 critical incident response. Named engineers who know your system, not a shared helpdesk queue.
- Critical incidents: 24x7 response within 4 hours
- All other incidents: Monday to Saturday, 9:30am to 6:30pm IST
- Security questionnaire turnaround: 48 hours
- Prompt regression testing before every deployment, not after the user complaint
30 minutes. Bring the production AI system that is running but not being maintained.
Managed AI operations is the ongoing work of keeping a production AI system doing what it was built to do - as the documents change, the model versions change, the user behaviour evolves, and the edge cases accumulate. It is what most AI projects do not plan for and most teams are not resourced to do.
What we keep hearing before the first call
Common bottlenecks and operational breakdowns we encounter before modernizing the workflow.
The true monthly cost of running production AI
These are the work items that accumulate after go-live. They are not optional and they are not small.
What goes into managed AI operations
Drift monitoring
Retrieval quality metrics tracked continuously. Alerts before the degradation becomes user-visible. Re-indexing run on a schedule and on event trigger.
Prompt regression testing
A test suite of known-good inputs and expected outputs maintained for each system. Run before every deployment. Failures block the release.
Model deprecation management
Deprecation schedules tracked for every model version in every client system. Migration initiated before the end-of-life date.
24x7 critical incident response
Critical incidents - system down, producing clearly wrong outputs at scale, security or compliance risk - responded to within 4 hours around the clock.
Security questionnaire support
Enterprise procurement questionnaires completed within 48 hours. The deal does not sit blocked on a security form.
Named engineers, not a helpdesk
The engineers who manage your system know it. Named lead and named backup. Escalation path in writing.
Service levels in writing, before you sign
| Incident type | Coverage | Response time |
|---|---|---|
| Critical - system down, clearly wrong outputs at scale, security or compliance risk | 24x7 | 4 hours |
| High - major functionality impaired, no workaround | Mon–Sat, 9:30am–6:30pm IST | 8 hours |
| Medium - partial functionality impaired, workaround exists | Mon–Sat, 9:30am–6:30pm IST | 24 hours |
| Low - minor issues, questions, improvement requests | Mon–Sat, 9:30am–6:30pm IST | Next business day |
| Security questionnaire | Business days | 48 hours |
How the engagement works
Technical assessment
We review the codebase, the model dependencies, the logging, and the monitoring. We tell you what we can take over safely and what would need to change first.
Managed operations agreement
Named components, SLA terms, monitoring scope, change management process, security questionnaire commitment. In writing.
Handover
We take over the monitoring, build the regression test suite, and map the model deprecation schedule.
Ongoing operations
Monthly review call covering output quality metrics, incidents, upcoming deprecations, and planned improvements.
Questions we get asked
Managed AI operations is what happens after you ship a production AI system. The model was the easy part. Month seven is where the retrieval results start drifting, the prompts that worked in testing stop working in production, the model provider deprecates a version, and the team that built the system has moved on. Managed AI operations is the ongoing work of keeping a production AI system doing what it was built to do.
Drift is the gradual degradation of an AI system's outputs as the inputs it was built for change - new document types, new terminology, new edge cases - without the system being updated to match. A retrieval system trained on your documents from six months ago will not retrieve correctly from documents written six months later that use different language. A prompt tuned for one model version may produce worse outputs when the model is updated. Drift is not a failure event; it is a slow deterioration that is invisible until it becomes a user complaint.
Prompt regression is when a change to a prompt, a model version, or the context being passed to the model causes outputs to worsen. It is caught by running a regression test suite against a set of known-good inputs and outputs before any change goes to production. We maintain a test suite for each client system and run it before every deployment.
Critical incidents - the system is down, producing clearly wrong outputs at scale, or presenting a security or compliance risk - are responded to 24 hours a day, 7 days a week, within 4 hours. All other incidents and requests are handled Monday to Saturday, 9:30am to 6:30pm IST.
We turn security questionnaires around within 48 hours. If an enterprise customer requires a third-party audit or specific certifications before onboarding a production AI system, we tell you the status of those certifications on the first call, not after three months of negotiation.
When a model provider deprecates a model version - meaning it stops being available - any system that uses that version has to be updated. This is not a theoretical risk. It has happened to OpenAI, Anthropic, and every major provider, and the window between deprecation announcement and end-of-life is sometimes shorter than the time it takes to test and deploy an update. We track deprecation announcements for every model version in every client system and initiate migration before the deadline.
Usually yes, for systems that use standard model APIs and have readable codebases. We do a technical assessment first to confirm we can take over the codebase and understand its architecture. If the system is built in a way that makes safe operation impossible - no logging, no test coverage, no documentation - we will tell you that on the assessment call.
A named managed operations agreement, covering: the system components under management, the SLA terms, the monitoring scope, the change management process, the security questionnaire turnaround commitment, and the review cadence. Not a support ticket queue. Named engineers who know the system.
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Learn moreReady to talk about your production AI system?
Book a 30-minute call. Bring the AI system that is running but not being maintained. We will tell you honestly whether we can take it over and what a managed operations engagement would look like for your system.
Working from Pune with clients across India, the US, the UK, Europe, the Middle East, Australia and Southeast Asia.
