Managed AI Services

Why Managed AI Services

Deploying an AI model is just the beginning. The real challenge is keeping it performing in production — where data distributions shift, user behaviors evolve, infrastructure demands change, and compliance requirements tighten. Industry research shows that 91% of ML models degrade within the first year of deployment, and most organizations lack the specialized MLOps expertise to manage AI systems at scale.

Shailka Systems manages 1,000+ AI models in production across industries, ensuring they deliver consistent performance with 99.95% uptime SLAs. Our managed AI services let you focus on innovation while we handle the operational complexity of running AI at enterprise scale.

Our Managed AI Services

24/7 AI Operations

We provide round-the-clock operational support for your entire AI ecosystem:

• Proactive Monitoring — Real-time monitoring of model performance, inference latency, data quality, and infrastructure health with automated alerting and escalation. We detect issues before they impact your business.
• Incident Management — Dedicated on-call AI engineers with a 15-minute response SLA for critical incidents. Our team handles root cause analysis, remediation, and post-incident reviews.
• Change Management — Controlled deployment of model updates, configuration changes, and infrastructure modifications with automated rollback capabilities and zero-downtime deployments.
• Capacity Planning — Proactive capacity forecasting based on usage trends, seasonal patterns, and business growth projections to ensure your AI infrastructure scales ahead of demand.

Model Performance Management

AI models require continuous attention to maintain accuracy and relevance:

• Drift Detection — Automated monitoring for data drift, concept drift, and prediction drift using statistical tests and ML-based drift detectors. We alert you when model performance begins to degrade, often weeks before it impacts business metrics.
• Performance Dashboards — Executive-ready dashboards showing model accuracy, precision, recall, AUC, and business KPIs linked to model performance, with trend analysis and root cause diagnostics.
• A/B Testing & Experimentation — Infrastructure for safely testing model updates against production traffic, with statistical significance testing and automated winner selection.
• Bias Monitoring — Continuous fairness monitoring across protected attributes, with automated alerts when disparate impact thresholds are exceeded.

Automated Retraining

When models degrade, we ensure they're updated quickly and safely:

• Automated Pipelines — End-to-end retraining pipelines triggered by performance thresholds, scheduled intervals, or new data availability. Pipelines include data validation, feature engineering, training, evaluation, and deployment stages.
• Human-in-the-Loop — For high-stakes models, our pipelines include human approval gates where Shailka AI engineers review model changes before production deployment.
• Shadow Deployment — New models are deployed in shadow mode alongside production models, comparing predictions on real traffic before any switchover.
• Rollback Automation — If a newly deployed model underperforms, automated rollback restores the previous version within minutes.

AI Infrastructure Management

We manage the full stack of AI infrastructure:

• ML Platform Operations — Management of Kubernetes clusters, GPU instances, model serving infrastructure, feature stores, and experiment tracking systems.
• Data Pipeline Operations — Monitoring and maintenance of data ingestion, transformation, and feature engineering pipelines that feed your AI models.
• Security & Compliance — Ongoing security patching, access control management, audit log maintenance, and compliance reporting for AI systems.
• Disaster Recovery — Automated backups, multi-region failover, and disaster recovery testing to ensure business continuity.

Cost Optimization

AI infrastructure costs can spiral without careful management:

• FinOps for AI — Detailed cost visibility by model, team, and use case, with anomaly detection for unexpected cost spikes.
• Right-Sizing — Continuous optimization of compute resources — matching GPU types, instance sizes, and scaling policies to actual workload requirements.
• Spot Instance Management — Intelligent use of spot/preemptible instances for training and batch inference workloads, reducing compute costs by 60-80%.
• Model Optimization — Techniques including quantization, pruning, and distillation that reduce inference costs without sacrificing accuracy.

Service Tiers

"With Shailka's managed AI services, our model uptime went from 94% to 99.97%. Their team caught a critical data drift issue that would have cost us $50M if it had gone undetected for another week." — VP of AI, Global Insurance Company