AI & Machine Learning
LLM apps, RAG pipelines, agents, and MLOps at production scale.
What we do for you
We build production-grade AI systems — not demos. From fine-tuned language models to distributed RAG pipelines, every system we ship is instrumented, observable, and designed to survive real-world traffic.
Our ML engineers have deployed models at scale for financial services, insurance, and healthcare companies where accuracy isn't optional. We own the full lifecycle: data ingestion, model training, evaluation, deployment, and ongoing drift monitoring.
We work natively with the Anthropic API, OpenAI, and open-source foundation models — and we'll recommend the right architecture for your latency, cost, and compliance constraints.
What's included
- LLM integration & prompt engineering
- RAG pipeline design & implementation
- Model fine-tuning and evaluation
- MLOps — training, deployment, monitoring
- AI agent frameworks & tool use
- Embedding pipelines & vector databases
- Production inference optimisation (latency / cost)
The specifics
Every engagement draws on a specific combination of these capabilities — applied by engineers who've done it in production.
Large Language Models
GPT-4, Claude, Gemini, Llama — integrated and fine-tuned for your domain.
RAG & Vector Search
Pinecone, Weaviate, pgvector — semantic retrieval at production scale.
Autonomous Agents
Multi-step tool-use agents with memory, planning, and fallback handling.
MLOps & Model Monitoring
MLflow, Weights & Biases, custom drift detection and alerting.
Inference Optimisation
vLLM, TensorRT, quantisation — sub-100ms p95 latency at scale.
Data Pipelines
Ingestion, transformation, and embedding pipelines with lineage tracking.
AI & Machine Learning in production
Real projects. Real outcomes. No case study padding.
SaaS
Building an Internal-Documentation MCP Server for a SaaS Engineering Team
Sep 2025
Read case study →SaaS / Cybersecurity
Website KPI & Malware Detection Platform for Kapient
Mar 2025
Read case study →
Tools chosen for the job,
not the trend.
Every technology decision in a AI & Machine Learning engagement is made against real constraints — performance targets, compliance requirements, team familiarity, and long-term maintainability. We'll tell you which choices are wrong for your situation.
Start a
AI & Machine Learning
project
Tell us what you're building and what's blocking you. We'll be direct about whether we're the right team — and what a timeline and cost looks like.