Managed AI OperationsScale
AI systems keep changing after go-live: models are updated, input data shifts, edge cases appear and business rules evolve. For selected capabilities, Zeyt takes ongoing operational responsibility for monitoring, evaluation, incident handling and improvement, independently or together with your teams.
Go-live is when the operating work starts.
Traditional software can often be monitored through availability, latency and errors. AI systems add another layer: the system can remain technically available while the quality of its decisions, outputs or actions changes.
Models are updated, prompts and tools evolve, source data shifts and new cases appear in production. Someone has to detect those changes, investigate deviations, test improvements and decide when a new version is safe to release. For the first few AI systems, building a dedicated internal team for this is often unnecessary.
We define which capabilities Zeyt operates, which remain with your teams and where responsibility is shared. Service levels, escalation paths, quality criteria and change responsibilities are agreed upfront, using the evaluations and controls established during the Build phase.
In production, we monitor both technical and business performance: availability, latency and cost, but also quality, evaluation scores, exceptions, escalation rates and other metrics relevant to the process. Deviations are investigated against the cases and evidence behind them.
Changes to models, prompts, tools or workflow logic are tested against defined evaluations before release. Incidents and material changes are documented, and improvements move through the same controlled release process as the original system.
Ownership does not have to remain with Zeyt. Runbooks, tooling, evaluations and operating practices are kept transferable so responsibility can move fully to your teams when the economics or organisation make that the better option.
AI capabilities that remain reliable after go-live.
Ongoing monitoring, evaluation and improvement for selected production AI capabilities, with agreed responsibilities, clear service levels and full visibility into performance, incidents and changes.
Monitoring & evaluation
Continuous measurement of technical performance, quality, cost, exceptions and business outcomes against agreed thresholds and evaluation sets.
Incident & change management
Deviations investigated, incidents handled and changes to models, prompts, tools and workflow logic evaluated before controlled release.
Continuous improvement & transferable ownership
Performance improvements identified and implemented over time, with reporting, runbooks and tooling kept ready for your teams to take full ownership when appropriate.
Bring us one system in production.
Tell us which AI capability is live or about to go live and who looks after it today. In a 45-minute working session, we look at what needs to be monitored and evaluated, how responsibilities could be split and what a managed service would cover.