Enterprise generative AI & RAG
Secure, ring-fenced LLM environments using Amazon Bedrock, LangChain or LlamaIndex, with high-performance RAG architectures on vector databases that connect models to your private data without leakage.
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Practical AI integration. Production-ready MLOps.
Move beyond the AI hype and experimental sandboxes. We build the secure cloud infrastructure, retrieval-augmented generation (RAG) pipelines and MLOps frameworks needed to run AI models safely in production.
Engineer your AI infrastructureOur philosophy
The hardest part of AI isn’t the model. It’s the heavy engineering required to serve it, secure it and feed it clean data in real time. AI pilots often fail because the surrounding infrastructure is brittle, insecure or unable to handle enterprise scale. As cloud architects, we build the ring-fenced environments that bridge data science and stable IT operations, turning models into high-availability commercial assets.
What we deliver
Production MLOps & deployment
End-to-end model lifecycle automation: CI/CD pipelines with SageMaker Pipelines or Step Functions for model serving, systematic retraining and containerised hosting.
Real-time feature delivery
Dedicated low-latency feature stores and data feeds, so your models always receive precise, fresh context at the moment of inference.
Monitoring, drift & safety guardrails
Continuous telemetry for model performance, data drift and anomalous outputs, with content filters, prompt-injection defences and cost-control caps built into your cloud setup.
Our stack expertise
AI & MLOps
- Amazon SageMaker
- Amazon Bedrock
- MLflow
- Docker
- Kubernetes / EKS
Vector data engines
- Amazon OpenSearch
- Pinecone
- pgvector