[Solutions · Managed operations]
Deploying a system is only the beginning. Models drift, data changes, usage grows, and costs evolve. We help enterprises keep AI, data, and cloud platforms reliable, accurate, and cost-efficient in production so teams can focus on running the business, not maintaining the technology.
[Managed Operations Capabilities]
Managed operations at Ascentt covers two responsibilities: keeping AI systems running in production & sustaining the platform, cost, and reliability foundation underneath them. The practice spans AIOps, MLOps, LLMOps, DataOps, FinOps, and SRE under service levels defined with you and reported monthly.
AI systems in production
Detect anomalies, reduce alert noise, and automate remediation for known issues so teams can focus on the incidents that actually matter.
Monitor model performance, detect drift, retrain safely, and keep production models aligned to the accuracy baseline they shipped with.
Operate GenAI applications and AI agents with prompt management, safety monitoring, observability, and cost controls.
Data, cloud, & reliability foundation
Keep pipelines fresh, complete, observable, and reliable so stale or broken data is caught before it reaches dashboards, models, or decisions.
Monitor platform health, manage capacity, and keep cloud and AI spend explainable as workloads change.
Provide SLA-backed support, incident management, root-cause analysis, and uptime management for the applications your AI depends on.
[WHAT CUSTOMERS GET ]
The goal of managed operations is simple: keep critical systems running so teams can focus on improving the business instead of troubleshooting technology.
Faster
Problems identified and addressed before they impact users.
Stable
AI systems monitored and maintained against defined performance targets.
Trusted
Fresh, reliable data for analytics, automation, and AI.
Controlled
Usage and costs monitored with clear visibility into optimization opportunities.
[Who We Work With]
Managed operations serve three groups: executives who funded the systems, AI and platform teams who want to build rather than babysit, and operations leaders who depend on the systems staying up.
[For the C-Suite]
Know that the AI you funded is still monitored, maintained, and accountable to service levels instead of quietly degrading until business metrics stop making sense.
[For Heads of AI, Data & Platform ]
Take production operations, monitoring, drift management, incidents, and cost optimization off your engineers so they can focus on the next capability that differentiates the business.
[For Operations Leaders ]
Keep models, agents, and pipelines accurate, fresh, and available with 24/7 accountability and service levels that can be reported and escalated when needed.
[Common Questions]
Traditional managed services keep the lights on: monitoring, ticketing, help desk, patching. Managed operations keep the outcome alive. For enterprise AI that means watching for model drift, retraining before accuracy slips, catching a pipeline that went stale before it feeds a bad decision, and tuning cost before the cloud bill surprises anyone. The difference is reactive support versus proactive operations. You are not paying for someone to answer tickets; you are paying for your AI to keep paying back.
Yes. We onboard existing models, pipelines, agents, and platforms through a structured assessment that maps what you have, documents what is missing, and stands up monitoring, observability, and SLAs around it. We operate best when we also built the system, because there is no knowledge handoff, but we regularly take over systems built by internal teams or other vendors and bring them up to an operable standard first.
Every model we operate runs with continuous performance monitoring against a baseline, drift detection on both inputs and predictions, and automated retraining triggers when accuracy crosses a threshold you set. Every pipeline runs with data quality checks and freshness SLAs, so a stale or broken feed raises an alert before it reaches a model or a dashboard. The goal is that you hear about an issue from us, with a fix already in motion, not from a business user who noticed a wrong number.
Service levels are set per engagement against the criticality of the system. A model that drives a production line gets tighter response and resolution targets than an internal analytics dashboard. We define severity tiers, response and resolution SLAs, escalation paths, and reporting cadence during onboarding, and we report against them every month. You always know what you are entitled to and whether we are meeting it.
FinOps is built into the operating model. We monitor spend by system, by team, and by use case, flag anomalies before they compound, right-size infrastructure as load changes, and match model and inference choices to the cost the workload justifies. Most engagements include a cost dashboard so you can see what each system costs to run and where the next optimisation is. Operations should reduce your run cost over time, not quietly grow it.
[Continue Reading]
Our AI advisory engagements are designed for senior leaders in manufacturing and automotive enterprises who are tired of AI pilots that never reach production and ready to own a measurable outcome inside one quarter.
Solutions · BUILD
Build the governed data foundation, pipelines, and lakehouse architecture that managed operations keeps reliable in production.
Solutions · think
Deploy forecasting, predictive maintenance, computer vision, and defect analytics that require ongoing monitoring, retraining, and operational support.
Solutions · Build
Run GenAI applications and AI agents with LLMOps, guardrails, observability, and cost controls under SLA-backed operations.
