Comprehensive operations and management solutions for large language models, machine learning systems, data pipelines, and AI agents. Ensure reliability, performance, and cost efficiency across your entire AI infrastructure. Monitor, optimize, and scale your AI systems with enterprise-grade observability and governance.
Develop comprehensive operations strategies for managing LLMs, ML models, data pipelines, and AI agents aligned with your business objectives.
Evaluate your current operations maturity, identify gaps, and provide recommendations for implementing best practices in LLMOps, MLOps, DataOps, and AgentOps.
Design scalable and resilient operations architectures for managing AI systems, including monitoring, logging, and governance frameworks.
Evaluate and select appropriate tools and platforms for LLMOps, MLOps, DataOps, and AgentOps based on your specific requirements and technology stack.
Implement and configure operations tools and platforms, including integration with your existing CI/CD pipelines and infrastructure.
Provide ongoing operations management, monitoring, optimization, and support for your LLM, ML, data, and agent systems in production.
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Open-source platform for managing machine learning lifecycle including experiment tracking, model versioning, and deployment.
Comprehensive platform for building, testing, and monitoring LLM applications with debugging and observability capabilities.
Machine learning platform for experiment tracking, hyperparameter optimization, and model management at scale.
AWS SageMaker, Azure ML, Google Vertex AI, and other cloud-native ML platforms for building and deploying models.
LangChain, LangGraph, Crew.AI, and other frameworks for building autonomous AI agents and multi-agent systems.
Kubernetes-native platform for machine learning workflows, model training, and production deployment at scale.
Add observability, tracing, monitoring and debugging in your AI agents and LLM Apps. AgentOps.ai works for a wide variety of agent frameworks and LLMs (Crew.ai, Langchain, Google ADK, OpenAI, LiteLLM, Grok)
Client: UK-based Automobile and Home Insurer. 500,000 quotes a day, 4 million customers
Scope: Migration of Guidewire, Apps (100+), APIs, Microservices, Data, Infra and DevOps to Azure.
Europe's first successful modernization and migration of Guidewire to cloud (> 15 mil GBP)
Tech stack: Azure, Azure VMware Solution (AVS), APIM, Snowflake, Azure Data & Integration, Guidewire
Outcome: Increased performance by 1.6 times. 4 to 5 times faster insurance adjustments. Legacy-free IT estate, APIfication, automation, data modernization, and operational cost savings.
Client: Asia based health insurance and financial services major
Scope: Modernization and cloud migration of legacy and complex apps to Azure.
Apps: 20+
Lines of code: 2 million +
Technology: Azure, Java, .NET, Sybase, SQL Server, DB2, WebSphere MQ
Client: Middle east based petroleum and natural gas major
Scope: Define strategy & platform architecture for a new Joint venture organization that would be curved out from parent company
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