AI Operations
Keep AI reliable, secure, and cost-efficient with continuous monitoring and evaluation.
AI operations
Keep your AI applications performing in production
Deploying AI is only the beginning. Model performance can change, operating costs can increase, and new security or reliability risks can emerge over time. We help organizations deploy, monitor, evaluate, and maintain AI applications with the operational controls needed to improve reliability, manage costs, and support continuous improvement.
Our capabilities
What we deliver in AI operations
AI Evaluation & Testing
Measure model and application quality through automated evaluations, regression testing, and performance benchmarks.
Model Deployment & Lifecycle Management
Manage model versions, deployment pipelines, updates, and controlled releases across AI environments.
AI Monitoring & Observability
Track response quality, latency, errors, usage, and application behavior to identify issues and maintain performance.
AI Cost & Performance Optimization
Monitor token consumption, compute utilization, and infrastructure costs to improve efficiency and control spending.
MLOps & LLMOps Automation
Automate model and application workflows, including testing, deployment, versioning, and operational maintenance.
AI Security & Guardrails
Implement access controls, content safeguards, policy enforcement, and monitoring to reduce operational and security risks.
Why Ambidex
Why choose us for AI operations
Quality you can measure
We establish evaluation metrics, testing processes, and monitoring to track AI performance and reliability.
Controlled and reliable releases
We use versioning, automated testing, and deployment controls to reduce risk when updating models and applications.
Visibility into AI costs
We track usage, infrastructure consumption, and operational costs to help you manage spending and improve efficiency.
Technology partners
Partnered with the platforms you run on
Technologies
Our technology expertise across leading platforms
FAQ
Frequently asked questions
What does an AI operations engagement include?
Our AI operations services cover model deployment, automated evaluation, monitoring, lifecycle management, cost optimization, and security controls. We tailor the engagement to your AI applications, infrastructure, and operational requirements.
What is the difference between MLOps and LLMOps?
MLOps focuses on deploying, monitoring, and managing machine learning models throughout their lifecycle. LLMOps extends these practices to large language model applications, including prompt management, response evaluation, token usage, retrieval quality, and model updates.
How do you monitor the quality and reliability of AI applications?
We establish evaluation metrics and monitoring processes to track response quality, latency, errors, usage, and application behavior. We also use automated testing and alerts to identify performance changes and operational issues.
How can AI operations help control costs?
We monitor model usage, token consumption, compute resources, and infrastructure spending. Based on these insights, we identify opportunities to optimize model selection, request processing, caching, and resource utilization.
Can you manage AI applications built using our existing platforms and models?
Yes. We can work with AI applications deployed across cloud platforms and model providers, including AWS, Azure, Google Cloud, and supported open-source technologies. We assess your existing architecture and implement operational practices suited to your environment.
Get started
Talk to us
Connect with an Ambidex expert to discuss how to plan, build and run your cloud, data, and AI solutions.