LLM operations

Manage LLMs like you manage any production system

You’re using large language models in production. ChatGPT in your customer support. Claude summarizing documents. OpenAI generating content. But LLMs are different from traditional systems — the behavior changes as models update. Costs vary based on usage. Prompt changes affect output unpredictably. LLMOps is about managing LLMs with the same rigor you’d apply to any production system. Version control on prompts. Monitoring for quality degradation. Understanding costs. Governance so you know what the system is doing. It’s not plug-and-play. It’s managed and maintained.

Where LLM Systems Become Unmanageable

Prompts are stored in different places. Someone tweaked one in production and nobody knows which version is live.

Costs are unpredictable. You expected $500/month and you’re at $2000 because usage spiked.

A new model came out and the team doesn’t know whether to upgrade or risk breaking what’s working.

The LLM system is a black box. Outputs seem off but problems only surface when users complain.

Customer data is going to an API with no audit trails, no policies, and unclear compliance.

Where Are You Starting From?

Just deployed an LLM system – need to manage it properly from here

LLMOps Foundation

Prompt management is chaotic – need version control and structure

Prompt Management

LLM costs are higher than expected and can’t optimise without visibility

Cost Visibility & FinOps

Suspect the LLM is degrading or hallucinating but not catching it systematically

Quality Monitoring

Multiple teams building LLM features with no consistent practices

LLM Governance

New model available – need to evaluate and upgrade safely

Model Upgrade Management

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 LLMOps.

Your prompts are version-controlled

Changes are tracked. Rollbacks are possible. When output is wrong, you can debug which prompt version caused it. Production versions are documented and auditable.

Your costs are visible and optimizable

You understand which features are expensive. You can make informed decisions about which models to use, which parameters to adjust, and where to optimize. Costs are tracked by feature, not lumped into one big bill.

You monitor quality systematically

Hallucinations are detected. Prompt effectiveness is tracked. When quality degrades, you know about it before users complain. You can test changes before deploying.

Model updates happen safely

You can test a new model version alongside the old one. You understand the impact before switching. You can roll back if something goes wrong. Upgrades are planned, not surprising.

You have governance and compliance confidence

You have audit trails showing what data was sent, what the model returned, and who accessed it. You have policies about which data goes to which models. You're confident you can explain the system's behavior.

How We Engage

1

Understand your LLM setup today

Which models? Which features? What data goes in? What’s the business impact? We assess your current setup and identify the operational gaps.

2

Design prompt management and versioning

Prompts get version control. Changes go through testing. Production versions are tracked. We set up cost monitoring so you understand where money is going and can optimize.

3

Establish quality monitoring and governance

We define what “good output” looks like for your use cases. We set up checks that catch hallucinations, detect quality degradation, and alert you to problems. We create processes for testing prompt changes before they go live.

Large language models are powerful but they require operations and governance

Start by understanding how you’re using LLMs today and where the operational gaps are. Book an LLM operations assessment. We’ll show you what’s working, what’s risky, and how to manage LLMs like any production system.

Patterns & Platforms

LLM Providers

Prompt Management & Versioning

Cost Monitoring & Optimization

Quality Monitoring & Testing

Deployment & Orchestration

Logging & Audit

Governance & Safety

What Clients Say About Working With Exillar

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

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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

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.

Thanks for the exceptional work!

It was a great experience.

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 count on his skills to assist us. I highly recommend him. He has excellent communication skills, which makes my life much easier when conveying out needs to a plan, and executing it.

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.

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

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Got Questions?

Should we use Azure OpenAI or ChatGPT API?

Azure OpenAI if you need enterprise features — VPC integration, audit logging, guaranteed throughput, compliance. ChatGPT API if you want simpler and don’t need enterprise. We help you make that choice based on your requirements.

How do we monitor LLM quality without having a data scientist?

Start with automated checks — does it follow instructions, does it stay on topic, does it avoid obvious hallucinations. Use LLM-based evaluation (another LLM checks the output). Add human review for sample outputs. We have frameworks for all of this.

How do we manage costs when usage is unpredictable?

Monitor usage by feature. Set up cost alerts. Choose efficient models for different tasks. Cache prompts and responses where possible. Use smaller models when appropriate. We help you optimize based on your actual usage patterns.

What happens if an LLM provider changes their API or pricing?

With proper abstraction, you can switch providers without changing application code. We set up that abstraction layer so you’re not locked in. When a provider changes, you evaluate and migrate if needed, but it’s not an emergency rebuild.

Manage LLMs like you manage any production system

Where LLM Systems Become Unmanageable

Where Are You Starting From?

What Changes

Your prompts are version-controlled

Your costs are visible and optimizable

You monitor quality systematically

Model updates happen safely

You have governance and compliance confidence

How We Engage

1

2

3

Large language models are powerful but they require operations and governance

Patterns & Platforms

What Clients Say About Working With Exillar

D&K

Growloup

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Darcy

Hans

Miguel

Travis

Raul Rodriguez/F&K

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Loudermilk Homes

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