AI & ML Services
DifferentiatorFull-spectrum AI services from hosted open-source LLMs for privacy-first enterprises to AWS Bedrock and SageMaker Pipelines for managed ML workflows.
What's Included
Get Started
Talk to our team about how AI & ML Services can help your business.
Contact Us WhatsApp UsFree Consultation
30-minute call. We review your setup and recommend the right approach — no commitment.
Book free 30-min callAI infrastructure is rapidly becoming a competitive differentiator. But building reliable, secure, and cost-effective AI systems requires more than calling an API. InfraOpex provides full-spectrum AI and ML infrastructure services — from hosting private open-source LLMs for data-sensitive enterprises to building production RAG pipelines and MLOps workflows on AWS Bedrock and SageMaker.
Our private LLM hosting service is particularly valuable for organisations in healthcare, finance, and legal — industries where sending data to public AI APIs like ChatGPT or Claude raises compliance concerns. We deploy open-source models (Llama, Mistral, DeepSeek) on your own infrastructure, expose them via a standardised API endpoint, and manage the infrastructure so you get the power of AI without the data privacy risk.
For AWS-native organisations, we implement Bedrock integrations — including Knowledge Bases for RAG, Agents for automated workflows, and Guardrails for responsible AI governance. For teams building custom ML models, our SageMaker Pipelines service handles the full MLOps lifecycle: training, evaluation, deployment, and monitoring for model drift.
Frequently Asked Questions
What is the difference between AWS Bedrock and SageMaker?
Bedrock gives you managed access to existing foundation models (Claude, Llama, Titan) via API — no infrastructure to manage, pay per token. SageMaker is for building, training, and deploying your own custom ML models with full control over the training process. Most enterprises use both.
Why would I host my own LLM instead of using ChatGPT?
If your data is sensitive (medical records, financial data, legal documents), sending it to OpenAI or Anthropic creates compliance and data privacy risks. Self-hosted open-source models like Llama or Mistral give you the same capabilities on your own infrastructure — no data leaves your environment.
What is a RAG pipeline and when do I need one?
RAG (Retrieval-Augmented Generation) lets an AI answer questions using your private data. Instead of the model relying on its training data, it searches your documents, databases, or knowledge base at query time and uses that context to generate accurate answers. It's ideal for internal chatbots, document Q&A, and customer support automation.
How long does a Bedrock integration take?
A basic Bedrock integration (model invocation + Knowledge Base) typically takes 1–2 weeks. A full Agents + Guardrails + RAG implementation is 3–6 weeks depending on data complexity.
Related Services
System Design & Architecture
Large-scale distributed system design across AWS, Azure, and GCP — cloud-native from the ground up.
DevOps-as-a-Service
CI/CD pipelines, IaC, GitOps, and platform engineering — fully managed.
Managed Kubernetes
End-to-end K8s on EKS, AKS, GKE, and on-premises — from initial setup to ongoing cluster operations.