Generative AI
Generative AI Development
We turn foundation models into dependable product features - content and code generation, retrieval-augmented generation over your own data, fine-tuning and the guardrails and evaluation that make generative AI safe to ship.
Our generative AI capabilities
- Retrieval-augmented generation (RAG) over your data
- Text, image and code generation
- Prompt engineering & model selection
- Fine-tuning & domain adaptation
- Guardrails, safety & content moderation
- Evaluation, testing & cost optimisation
What we deliver
Knowledge assistants
Answer questions accurately from your documents, wikis and databases.
Content generation
Draft, summarise and personalise content at scale with brand-safe output.
Developer copilots
Internal tools that generate, explain and review code for your teams.
Workflow acceleration
Automate research, drafting and triage steps inside your operations.
Explore related services
Ship generative AI that works
Tell us about your goals and we'll get back to you within 24 hours.
Frequently asked questions
What is generative AI development?
It is building product features on top of generative models - systems that produce text, code or images. In practice that means RAG over your data, prompt design, fine-tuning, guardrails and evaluation, wired into your application.
What is RAG and do I need it?
Retrieval-augmented generation grounds a model's answers in your own content so responses are accurate and up to date. It is the most reliable way to deploy generative AI on private or domain-specific knowledge.
How do you stop generative AI from hallucinating?
We ground answers in retrieved sources, constrain outputs, add evaluation and guardrails, and keep a human in the loop where stakes are high - then monitor quality in production.
Can you fine-tune a model on our data?
Yes, when it adds value. Often RAG is enough and cheaper; we recommend fine-tuning only when you need a specific tone, format or task the base model can't reach with prompting and retrieval.