We help enterprise IT teams move beyond pilots — deploying secure, scalable generative AI workloads on AWS using Amazon Bedrock, SageMaker, and purpose-built RAG architectures.
Semantic search over 2.4M internal documents — latency under 800ms, 94% relevance score.
End-to-end Gen AI implementation — from architecture design through to production deployment and ongoing optimisation.
Retrieval-Augmented Generation pipelines connecting your enterprise knowledge bases to foundation models — with source citation, access controls, and hallucination guardrails.
Custom model training on your proprietary data — domain-specific language, tone, and compliance requirements baked into the model at inference time.
Multi-step AI agents that reason, plan, and act across your systems — integrating with your APIs, databases, and operational tooling securely.
Enterprise controls for AI workloads — data residency, content filtering, PII detection, and audit trails that satisfy security and compliance teams.
Full visibility into your AI workload performance — token usage, latency distributions, model drift detection, and cost attribution by team or use case.
Automated pipelines for model versioning, testing, and promotion — so your teams can iterate on AI workloads with the same rigour as application code.
Common patterns we implement for IT teams operating large-scale AWS environments.
A RAG-powered assistant that lets employees query internal documentation, runbooks, HR policies, and product specs in natural language — with citations and access controls by department.
Agentic workflows that interpret operational alerts, suggest remediation steps, auto-generate IaC from natural language, and accelerate code review for engineering teams.
Deploy fine-tuned conversational AI that handles Tier 1 support queries, escalates intelligently to human agents, and improves continuously from real interaction data.
Let business users query data warehouses and dashboards in plain English — with AI-generated summaries, anomaly callouts, and auto-drafted executive reports.
We map your data sources, use cases, compliance requirements, and current AI maturity to define the right architecture before writing a line of code.
A working prototype in your AWS account — evaluated against real enterprise data with measurable quality benchmarks and stakeholder sign-off criteria.
Hardened, scalable deployment with CI/CD pipelines, security controls, observability, and cost guardrails baked in from day one.
Ongoing model evaluation, cost optimisation, and capability expansion — with your team trained to own and iterate on the system.
We work across the full AWS AI/ML stack — selecting and combining services to suit your specific workload requirements.
Foundation model access & RAG
Training, fine-tuning & MLOps
Intelligent enterprise search
Vector search & embeddings
Serverless inference & agents
Data lake & document storage
Agentic workflow orchestration
AI workload observability
Book a call with our team to map out the right architecture for your enterprise AWS environment.
Whether you're evaluating use cases, stuck in pilot mode, or ready to scale — we'll help you define the right path forward.