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Generative AI Development on Your Own Data
Custom generative AI applications for UK and US businesses: knowledge assistants, copilots, document automation and content tools that are grounded in your data and safe to use.
Quick answer
What is generative AI development?
Generative AI development is building applications that use large language models (LLMs) and other generative models to write, summarise, answer questions, extract information or create content. ML Data House builds generative AI apps grounded in your own data with retrieval-augmented generation (RAG), evaluation, guardrails and secure deployment.
- Knowledge assistants grounded in your documents
- Copilots inside your tools and workflows
- Document extraction, drafting and summarising
- Evaluation and guardrails for reliable answers
What we deliver
What our Generative AI Development service covers
RAG knowledge assistants
Answers from your policies, manuals and data, with sources shown.
Business copilots
Assistants for sales, support, HR, finance and operations teams.
Document automation
Extract, compare, summarise and draft contracts, reports and forms.
Content generation
On-brand product descriptions, emails and reports with human review.
Model selection and tuning
Choosing between hosted and open-source models; fine-tuning where it helps.
Evaluation and guardrails
Test sets, hallucination checks and safety filters.
How it works
Our Generative AI Development process
Short, visible steps with a demo or decision at the end of each one.
Prototype
Working assistant on a sample of your data.
Harden
Evaluation, guardrails, security and integrations.
Launch
Deployment, monitoring and continuous improvement.
Deliverables
What you get
- Use-case design and quality criteria
- Working generative AI prototype
- Production app with RAG and guardrails
- Evaluation set and quality reports
- Monitoring and cost dashboard
Best fit
This is right for you if…
- Staff waste time searching for information
- You handle large volumes of documents
- You want a private ChatGPT-style assistant
- Content creation is slow and repetitive
UK & US
Generative AI Development for UK and US businesses
One remote team, planned around the rules and working patterns of each market.
United Kingdom
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 organizations we can use UK/EU-hosted models and keep prompts and documents out of model training, documented for UK GDPR.
United States
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 organizations we can use US-hosted enterprise model endpoints and support HIPAA-eligible services where health data is involved.
Tools, models & frameworks
OpenAI GPTAnthropic ClaudeGoogle GeminiLlamaMistralAzure OpenAIAWS BedrockLangChainLlamaIndexpgvectorPinecone
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FAQ
Generative AI Development: frequently asked questions
What is the difference between generative AI and traditional AI?
Traditional (predictive) AI classifies or forecasts, such as predicting churn. Generative AI produces new content, such as text, summaries or answers, usually with large language models.
What is RAG (retrieval-augmented generation)?
RAG finds the most relevant passages from your own documents and gives them to the language model, so answers are based on your content and can cite sources.
Can we have a private ChatGPT for our company?
Yes. We build private assistants on enterprise model endpoints, connected to your content and protected by your login and access rules.
How do you reduce hallucinations?
By grounding answers in retrieved sources, asking the model to cite them, testing against evaluation sets and adding checks that refuse to answer when evidence is missing.
Is our data used to train the AI models?
Not with the enterprise model services we use by default, which do not train on customer data. We confirm the terms for each provider with you.
Which LLM is best for business?
It depends on the task, cost, speed and privacy needs. We test several models on your use case and pick the best fit.
How long does a generative AI project take?
A prototype often takes a few weeks; a production-ready application typically takes about three months.
Ready to talk about Generative AI Development?
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.