Assess
Review your team, stack and target use case.
Deeper, technical workshops for developers, data teams and product owners who will build, run and own AI. Your team leaves able to ship AI features, not just talk about them.
AI enablement workshops build the in-house skills to design, build and run AI systems. ML Data House works with your developers, data and product teams on LLM applications, retrieval-augmented generation (RAG), AI agents, evaluation, deployment and governance, using your own stack and a real use case as the project.
Prompting in code, structured outputs, tool use and streaming.
Chunking, embeddings, vector databases and answer quality.
Agent loops, tools, guardrails and human approval steps.
Test sets, automated scoring and regression checks for AI features.
Versioning, monitoring, cost control and rollback.
Prompt injection, data leakage and access control.
Short, visible steps with a demo or decision at the end of each one.
Review your team, stack and target use case.
Live technical sessions with code-along labs.
Your team builds a working prototype with our engineers.
Patterns, templates and a review of the prototype.
One remote team, planned around the rules and working patterns of each market.
We work from our UK office in Wolverhampton with clients across England, Scotland, Wales and Northern Ireland, remotely. Projects are planned around UK GDPR and the Data Protection Act 2018, ICO guidance on AI and data protection, and, if you sell into Europe, the EU AI Act.
For UK teams we include data-minimisation and logging patterns that support UK GDPR accountability.
We work remotely with US companies from coast to coast, and UK working hours overlap with the US East Coast morning for live calls and demos. We plan around the NIST AI Risk Management Framework, state privacy laws such as the CCPA/CPRA, and sector rules like HIPAA where they apply.
For US teams we include controls that support SOC 2-style audits and customer security reviews.
Developers, data scientists, data engineers and product owners who will build or own AI features.
Comfort with Python or TypeScript and basic APIs is enough. We adjust depth to the group.
Yes. Labs run on your cloud, languages and tools wherever possible so the patterns transfer directly.
Yes. The team builds a working prototype of one of your use cases during the program.
Yes, including tool calling, planning, memory, guardrails and when an agent is the wrong choice. See agentic AI systems.
Teams create a test set for their use case, automate scoring and add it to their release process, so quality is measured rather than guessed.
Yes. We offer code reviews, pairing and architecture support as the team moves to production.
Tell us about your goals, data and systems. We will reply with practical next steps, wherever you are in the UK, the US or worldwide.