LLM & generative AI
The reasoning layer — models that know your domain, grounded in your documents, exposed to your systems through interfaces you control.
LLM & generative AI
What does custom LLM development involve?
Custom LLM work means adapting a language model to your domain — through prompt architecture, retrieval over your own documents, fine-tuning, or self-hosted deployment. The goal is a model that answers accurately in your context, with citations, guardrails and costs you can predict.
Grounding beats raw capability
A general model that has never seen your pricing, your policies or your product catalogue will answer confidently and be wrong in ways your customers notice. Almost every useful deployment is therefore a retrieval problem before it is a model problem: get the right context in front of the model, cite the source, and refuse when the answer is not in scope.
Fine-tuning is worth it for tone, format and narrow classification tasks. Retrieval is worth it for facts. Choosing wrongly between them is the most expensive mistake in this category, so it is the first thing we settle.
Models
Custom LLM development
Domain-adapted models — prompt architecture, fine-tuning, evaluation and deployment.
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Purpose-built assistants for a team or a task, with your knowledge and your rules.
Read moreIntegrationMCP server development
Model Context Protocol servers that give AI tools safe, governed access to your systems.
Read moreKnowledgeRAG & knowledge systems
Retrieval pipelines over your documents, with citations and access control.
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Automated video production pipelines for marketing, training and localisation.
Read moreContentGenerative AI content systems
Content pipelines with brand rules, review gates and human sign-off built in.
Read more
Frequently asked
Questions we get about this
Do we need to train our own model?
Almost certainly not. Training from scratch costs millions and is the wrong answer for nearly every business problem. Retrieval over your own content, careful prompt architecture, and occasionally a light fine-tune will get you further for a fraction of the cost.
Can we keep our data private?
Yes. Options range from enterprise API tiers with no-training guarantees, through private cloud deployment, to fully self-hosted open-weight models on your own infrastructure. The trade-off is cost and quality against control, and we work through it explicitly rather than defaulting.
How do you stop it making things up?
Retrieval with citations, an explicit refusal path when the answer is not in the source material, and evaluation sets built from real questions before launch. Hallucination is a design problem, not an unavoidable property.
Talk it through before you commit
A discovery call is a working session on your constraint, not a sales pitch.
Related
Where this connects
Quick inquiry
Tell us what you're trying to build
A short note is enough. You'll hear back from the team, not a bot — usually within one working day.
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