Enterprise RAG pipelines, intelligent document processing, and conversational AI — production MLOps with full observability. AWS and Amazon Bedrock are where we go deepest today, with the same engineering across Azure and Google Cloud.
We build retrieval-augmented pipelines and conversational assistants on Amazon Bedrock — grounding models in your own documents, wrapping them in guardrails, and wiring traces and evaluations so behaviour stays observable in production, not just in a demo.
A GenAI system is only as reliable as the data behind it, so we are extending our work upstream into data quality and readiness — using AI-assisted discovery to profile sources, surface gaps and duplicates, and trace lineage, then remediate what retrieval depends on. This is a direction we are investing in, alongside the delivery work above.
Retrieval-augmented generation on Amazon Bedrock and OpenSearch, grounded in your own knowledge base.
Intelligent extraction and classification pipelines built on Textract, Comprehend, and foundation models.
Context-aware assistants for customer and internal support, with memory and enterprise SSO.
SageMaker pipelines, model registry, drift detection, and cost-optimized inference at scale.
Bedrock Guardrails for PII, content safety, and full logging of every model call and decision.
Autonomous workflow agents built on the same foundation — see our Agentic AI practice.
View Agentic AI →Tell us what you're working on. We'll share how we'd approach it.
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