AI & Automation · AI Development & Integration
MLOps and LLMOps Services
MLOps and LLMOps are the practices that take AI from a working prototype to reliable production and keep it healthy there. They cover deploying and serving models, monitoring cost, latency, and quality, versioning models and prompts, and governing the whole system. The model is a small part of the work; we build everything around it that makes AI dependable in the real world.
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What it is
What are MLOps and LLMOps?
MLOps is the discipline of running machine learning models in production reliably: deploying them, serving predictions, monitoring how they perform, versioning them, and retraining when needed. LLMOps applies the same idea to large language models and adds the parts unique to them, such as versioning prompts, evaluating non-deterministic outputs, and tracking cost per request. Together they cover everything that happens after a model works in a notebook.
It matters because most AI projects fail not at the model but at production: the model is a small slice of the system, and the rest is data validation, serving infrastructure, monitoring, and governance. Without it, models silently degrade, costs drift, and outputs go unchecked. Strong operations is the difference between a demo that impressed everyone and a system your business can actually depend on day after day.
What's included
What an MLOps engagement includes
Model deploymentWe package and deploy models with safe rollout strategies like canary and blue-green.
Serving infrastructureWe stand up scalable, reliable serving so predictions and responses meet your latency needs.
MonitoringWe track latency, cost, errors, and output quality, with alerts when any metric drifts.
VersioningWe version models, datasets, and prompts so every change is tracked and reversible.
Evaluation pipelinesWe wire automated evaluation into CI/CD, including LLM-as-a-judge checks for generative output.
Retraining and driftWe detect data and model drift and retrain on a schedule or trigger to keep accuracy up.
Governance and auditWe add access controls, logging, and audit trails to meet review and compliance needs.
How we work
How we set up AI operations
1Assessment
We review your models, infrastructure, and goals to design the right operations setup.
2Deployment pipeline
We build automated packaging and deployment with safe rollout strategies.
3Serving and scaling
We set up serving infrastructure that scales to your traffic and latency targets.
4Monitoring and evaluation
We instrument cost, latency, quality, and drift, and wire evaluation into CI/CD.
5Governance
We add versioning, access controls, logging, and audit trails for compliance.
6Operate and improve
We monitor in production, retrain or roll back as needed, and tune for cost and reliability.
Why it matters
Why operations decide success
Done right, MLOps and LLMOps turn a fragile prototype into AI that stays reliable, affordable, and accountable in production.
Models that reach production
Automated deployment and serving get models out of notebooks and into reliable use.
No silent degradation
Monitoring catches drift, quality drops, and cost spikes before they hurt the business.
Auditable and compliant
Versioning, logging, and access controls give you a clear record for review and regulation.
Who this is best for
The right fit
Best fit when
You have models or LLM features that need to run reliably in production, or projects stuck at the prototype stage, and you want deployment, monitoring, versioning, and governance handled properly rather than improvised.
You might not need this
If you still need the model itself built or trained, that is upstream modeling work rather than operations. If your priority is wiring a language model into a product feature first, start with LLM Integration. MLOps and LLMOps matter once something must stay reliable in production.
FAQs
Common questions about MLOps and LLMOps
Why do so many AI projects fail in production?
Because the model is only a small fraction of the system. The rest is data validation, serving infrastructure, monitoring, and governance, and that is where projects break. A model that scored well in testing can degrade quietly, cost more than expected, or go unmonitored. MLOps exists to handle that gap so AI stays reliable after launch.
What is the difference between MLOps and LLMOps?
MLOps covers operating machine learning models generally: deploy, serve, monitor, and retrain. LLMOps is the same discipline adapted to large language models, which behave differently. LLM outputs are non-deterministic, so traditional accuracy monitoring does not fully apply, and prompts change often and must be versioned like code. We apply whichever fits, and often both in the same system.
How do you monitor a model you cannot see inside?
We track signals around it: latency, cost per request, error rates, output quality, and input drift that shows the data has changed. For LLMs we add evaluation checks, including using a model to judge outputs against criteria, wired into your pipeline. When a metric crosses a threshold, we alert and can roll back or retrain.
Do you work with our existing cloud and tools?
Yes. We build on your cloud and existing stack where possible rather than forcing a rebuild, using standard tooling for serving, tracking, monitoring, and CI/CD. The setup is designed to fit your infrastructure and team. We will recommend changes only where they clearly reduce risk or cost.
Can MLOps help with AI regulations and audits?
Yes. Versioning, logging, and audit trails record which model and data produced a given result, which is what reviewers and regulations increasingly require. Governance controls who can deploy and change models. This auditability is becoming a baseline expectation, not an extra, especially under emerging AI rules.
We have a model that works but is not deployed. Can you take it to production?
Yes, that is a common starting point. We take a working model or LLM feature and build the deployment, serving, monitoring, and governance around it so it runs reliably at your scale. If the model still needs building or training, that is upstream modeling work we would scope separately. Either way, we tell you what production-readiness actually requires.
In their words
What clients say about working with our AI team
Real voices, in writing, audio, and on camera.
Standards we build to
Security & Compliance Standards
ISO 27001 Certified
SOC 2 Type 2
PCI DSS Compliance
GDPR Compliance
CCPA Compliance
ISO 27018 Certified“We follow the principles of GDPR, CCPA, and ISO standards certified to ensure security, privacy, and compliance across all operations.”
Is your AI stuck before production?
Get a free build audit. We will assess where your models stand, tell you honestly what production-grade operations require, and map deployment, monitoring, and governance before you commit.
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