Managed AI Services
Ongoing management of AI systems — monitoring, optimization, support, and enhancement — so your solutions stay accurate, secure, and cost-effective.
Production AI Is Not "Deploy and Forget"
Models drift, costs creep, prompts stop working against a new data pattern, and dependencies change underneath you. Our Managed AI Services team monitors performance, spend, and reliability continuously, and steps in before small issues become production incidents.
We operate as an extension of your team — running the monitoring, retraining, and incident response, and reporting on the metrics that matter to the business, not just to the model.
- 24/7 monitoring & incident response for models and AI agents
- Model performance tracking, drift detection, and scheduled retraining
- Cost and capacity optimization across inference and compute
- Security patching and dependency management for AI pipelines
- Monthly reporting tied to business KPIs, not just model metrics
Sub-Services & Capabilities
Comprehensive solutions tailored to every stage of your initiative.
24/7 Monitoring & Incident Response
We instrument your AI systems with monitoring and alerting tuned to the failure modes that actually matter — latency, accuracy drops, cost spikes, and unsafe outputs — and respond under a defined SLA.
- Real-time monitoring dashboards
- Anomaly & drift alerting
- On-call incident response
- Root-cause & post-incident review
Model Performance Management
We track accuracy, relevance, and business-outcome metrics over time, and manage the retraining and evaluation cycle needed to keep models performing as your data evolves.
- Performance & drift tracking
- Scheduled retraining & evaluation
- A/B testing of model versions
- Bias & quality monitoring
Cost & Capacity Optimization
We continuously tune infrastructure sizing, caching, and model selection to keep inference and hosting costs predictable as usage scales, without sacrificing performance.
- Inference cost analysis
- Model & infrastructure right-sizing
- Caching & batching strategies
- Capacity forecasting
Real-World Applications
How organizations leverage our expertise to solve critical business challenges.
Ongoing Operations for a Fraud Model
Took over monitoring and retraining for a live fraud-scoring model, adding drift alerts and a monthly retraining cadence.
Outcome: Model accuracy sustained above target for eighteen consecutive months.
Cost Reduction for a Support Copilot
Audited inference spend on a customer support copilot and re-architected prompt and caching strategy without changing the user experience.
Outcome: 38% reduction in monthly inference cost.
Incident Response for a Clinical Assistant
Established 24/7 monitoring and a defined incident response runbook for a clinical documentation assistant used across multiple facilities.
Outcome: Mean time to detect production issues cut from hours to minutes.
Key Differentiators
Continuous coverage with defined SLAs, not best-effort support.
Models are re-evaluated and refreshed on a cadence tied to observed drift, not guesswork.
Every engagement tracks and manages inference and infrastructure spend as a first-class metric.
Reporting ties model health back to the outcomes the business actually cares about.
AI Operations, Handled
A dedicated operations team that monitors, retrains, and optimizes your AI systems so they keep delivering value after launch day.
Cost Reduction for a Support Copilot
A prompt and caching re-architecture cut monthly inference spend by 38% without changing the end-user experience.
Keep Your AI Systems Healthy
Talk to us about ongoing monitoring, optimization, and support for your production AI.