AI operations

Keep your AI models cloud infrastructure reliably and cost-effectively

You deployed an AI model. It works today. But AI systems degrade over time. The data changes. Real-world edge cases appear. Model accuracy drifts without you knowing. AIOps is about monitoring what your model actually does, catching performance degradation before it affects decisions, automating the responses to problems, and maintaining reliability without someone babysitting the system. It’s the difference between an AI project that works and an AI system your business depends on.

Where AI Models Break Without Anyone Noticing

The model was accurate at deployment. Three months later it drifted – and nobody knew until users complained.

AI models go live without monitoring. Nobody checks whether they’re still accurate or when performance degrades.

When something breaks, you’re debugging after the fact instead of catching it before it happens.

Your team is manually reviewing model outputs instead of having the system flag problems automatically.

Retraining happens on a schedule or not at all – not when the model actually needs it.

Where Are You Starting From?

Just deployed a model – need monitoring before problems happen

Model Monitoring Setup

Model is in production but nobody is watching whether it’s still accurate

Production Monitoring

Managing multiple models manually – need a system that watches all of them

Multi-Model Observability

Model performance has degraded and need to understand why

Drift Investigation

Concerned about edge cases or fairness issues on specific segments

Fairness & Edge Case Monitoring

Need an automated signal for when the model should be retrained

Retraining Automation

Already have AI pilots need help getting them to production

Pilot-to-Production Roadmap

Pre-IPO or pre-acquisition need to show AI maturity to diligence

AI Maturity Uplift for Diligence

What can I help with ?

What Changes

Five outcomes of proper AIOps.

You know immediately when your model stops performing

Not weeks later when someone notices the business impact. Not through customer complaints. The monitoring system tells you the moment accuracy drops or drift increases.

You understand why the model is degrading

Is the input data changing? Did a data pipeline break upstream? Are you seeing new patterns in the data? Is the model underfitting new scenarios? You have visibility into what's happening, not just that something is wrong.

Your team responds automatically to common problems

A model starts drifting? The system logs it. A specific customer segment is being misclassified? Alerts fire. Data quality drops? You know before the model sees bad data. Some responses are automatic; some require a human to investigate.

You can retrain confidently and safely

You know when the model needs retraining because the monitoring tells you. You have historical performance data so you know whether the new model is actually better. You can deploy with confidence.

Your AI system earns trust over time

It works consistently. When something does go wrong, your team fixes it quickly because they caught it early. Leadership trusts the model's decisions because it's been reliable.

How We Engage

1

Define what success looks like

We understand what your AI model does and what “good performance” looks like. What’s the actual business outcome you care about? Accuracy? Precision? Customer satisfaction? Cost savings? We define what success metrics mean in your business context, not just statistical metrics.

2

Instrument your model

We set up logging on predictions, inputs, outcomes, and performance metrics. We create baselines so we know what normal looks like and can detect when things drift.

3

Build automated monitoring and alerting

We identify which problems require automatic responses (retrain, fallback, alert a human) and which need investigation. We create dashboards so your team can see model health at a glance. We establish processes for investigating issues and deciding when to retrain.

An AI model in production without monitoringis a ticking time bomb

Start by understanding what your model actually does and how it’s performing. Book an AI operations review. We’ll show you what you’re monitoring, what you’re missing, and how to set up reliable monitoring that catches problems early.

Patterns & Platforms

Model Monitoring Platforms

Monitoring & Observability

Data Quality & Drift Detection

Model Registry & Versioning

Retraining Orchestration

Logging & Analytics

What Clients Say About Working With Exillar

Umair handled my problem timely and efficiently. He is easy to collaborate with and I will be using him again.

Super good explanation, patience and a good sense of indagatory about the data, sources, etc. The solutions suggested were very safisfactory.

It is always a pleasure to work with Umair and his team. Rock start service!

Honestly, this has been an outstanding experience from start to finish. The team went far beyond my expectations — not only did they understand a very complex real-world operation, but they were also able to translate it into a functional and well-structured system. Their communication, patience, and willingness to adapt to my requirements were truly impressive. What I appreciated the most is how committed they were throughout the entire process. They were always available, proactive, and focused on delivering real value, not just completing the task. The system works great, and you can tell there is a lot of thought and effort behind it. This is not just a basic delivery — it’s a solid, professional solution. I would 100% recommend working with them, and I’m definitely looking forward to continuing this project long-term. Great job to the entire team.

Excellent work as always by Umair and team. Umair and team continue to provide excellent work product. Highly recommend, responsive and attention to detail.Umair + Exillar team continue to impress and innovate as business needs evolve

Thanks for the project. If you are an Executive, you need a PowerBI dashboard. great working with the team. Many ongoing projects with Umair. Great person to work with

These guys are true professionals, they helped me improve the idea of the work I wanted to develop, very kind and prepared. We will definitely do more work together. second work and I’m very statisfied.

Thanks for the exceptional work!

It was a great experience.

Working with Exillar has been amazing. Bhavisha has has gone above and beyond to get us what we need. Very pleased. ~Sherwin

Great working with Umair, fantastic communication and excellent delivery! Would surely recommend Umair to anyone for sure! THINGS THAT WENT WELL Politeness, Delivery time, Went above and beyond, Proactive communication, Level of cooperation, Language fluency, Deep understanding, Quick responsiveness, Attention to details, Code expertise, Bug free, Exceeded expectations, Documentation. Professionalism of work.

The guys were great to work with, very fast to reply and have a deep understanding of PowerBI. This become a learning experience for me as they shared best practices for PowerBI.

Industries We've Worked In

Got Questions?

How do we know what metrics to monitor?

Start with what matters to your business. For a classification model, is precision more important or recall? For a forecast, do you care about absolute accuracy or directional correctness? For a recommendation system, do you care about engagement or satisfaction? Define that first, then build monitoring around it.

Do we need to monitor every model or just the critical ones?

Critical ones first. If the model drives a business decision or affects customers, monitor it. If it’s an experimental model or low-impact, you can start lighter. We help you prioritize based on business impact.

When should we retrain?

When performance degrades beyond acceptable thresholds, or when data distribution shifts significantly. Not on a fixed schedule on actual performance signals. We set up the triggers that tell you it’s time.

Can we automate retraining or do we need a data scientist?

You can automate most of it data pipeline, model training, validation. But someone needs to evaluate whether the new model is actually better and decide to deploy it. We automate the mechanics and monitoring; your team focuses on the decisions.

Keep your AI models cloud infrastructure reliably and cost-effectively

Where AI Models Break Without Anyone Noticing

Where Are You Starting From?

What Changes

You know immediately when your model stops performing

You understand why the model is degrading

Your team responds automatically to common problems

You can retrain confidently and safely

Your AI system earns trust over time

How We Engage

1

2

3

An AI model in production without monitoringis a ticking time bomb

Patterns & Platforms

What Clients Say About Working With Exillar

Travis Wayne Kelley

Héctor Raúl Rodríguez Díaz

Alex Bleicker

Andres Garrido Assad

Criag Peters

Louis Philippe

Jacopo

Hans Gildenhuys

Miguel

Sherwin Loudermilk

Wasim

Darcy

Industries We've Worked In

Retail & E-Commerce

Healthcare

Finance & Banking

Real Estate & Construction

IoT & Technology

Manufacturing & Industrial

Got Questions?

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