Deep expertise, not a generalist menu.
Four core capability domains. Each one is something we do at depth — not something we have added to the brochure because the market moved there.
HomeCapabilities
Capability 1 — Conversational AI
AI systems that communicate naturally — chat, voice, and copilots.
Conversational AI
Chat, Voice & Copilot Systems
Design and deployment of AI systems that communicate naturally — chat interfaces, voice agents, customer-facing assistants, and internal copilots. We build conversation flows that feel intuitive, stay on-topic, and handle edge cases gracefully.
- LLM integration and prompt engineering
- Multi-turn conversation design and state management
- Voice AI — speech-to-text, text-to-speech, and real-time processing
- Customer-facing chatbot deployment
- Internal copilot design for operations and support teams
- Guardrails, fallback handling, and human escalation flows
Capability 2 — Cloud Platform
Scalable AI infrastructure on modern cloud.
Cloud Native
Cloud Native Platform Engineering
Building and operating AI products on modern cloud infrastructure — scalable, cost-efficient, and designed for the specific demands of AI workloads. We work across AWS, GCP, and Azure.
- Cloud architecture design — AWS, GCP, or Azure
- Kubernetes orchestration for AI workloads
- Serverless deployment for inference endpoints
- Infrastructure as Code — Terraform, Pulumi, or CDK
- Multi-cloud strategy and vendor management
- Cost optimisation and right-sizing for AI compute
Capability 3 — Data Engineering
The data layer that makes AI products intelligent.
Data Engineering
Data Pipelines & RAG Systems
The data layer is what makes AI products intelligent. We build the pipelines, stores, and retrieval systems that give AI access to the right information at the right time — reliably and at scale.
- Data audit — what you have, what you need, and what you can use
- Retrieval-Augmented Generation (RAG) pipeline design and build
- Vector database selection and optimisation
- Embedding model selection and fine-tuning strategy
- Real-time and batch data ingestion pipelines
- Data quality monitoring and refresh cadence
Capability 4 — ML & MLOps
From model selection to production monitoring.
ML Modelling & MLOps
Model Training, Deployment & Monitoring
From model selection and fine-tuning through to deployment, monitoring, and continuous improvement. We bring ML engineering discipline to AI product development — not just prototype-quality experimentation.
- Model selection and fine-tuning for your use case
- Evaluation frameworks — accuracy, latency, cost, and quality
- MLflow integration for experiment tracking and model registry
- Drift detection and automated retraining pipelines
- Inference optimisation for cost and latency at scale
- Quarterly model evaluation against updated benchmarks
Ready to ship your AI product?
Book a free 30-minute discovery call. We will tell you honestly whether we are the right partner for what you are building — and what we would do if we were.