AI development services that ship to production

AI development that survives contact with production

Demos are easy; reliable AI features are not. The difference is evaluation, guardrails, fallbacks and cost control. We build AI products with measurable output quality, a defined behaviour when the model fails, and per-request cost you can forecast before you scale.

We work across chat interfaces, RAG over your own documents, agentic workflows, classification and extraction, voice, and image generation — chosen by what the problem needs, not by what is trending.

RAG, fine-tuning or plain prompting

Most business use cases are solved by retrieval over your own data plus a well-designed prompt, not by fine-tuning. Fine-tuning earns its cost when you need a consistent format or tone at scale; retrieval wins whenever answers must reflect current, private, changing information.

• →Retrieval-augmented generation: your documents, chunked, embedded and cited in answers.
• →Evaluation sets so a prompt or model change is a measured improvement, not a vibe.
• →Cost and latency budgets per feature, with smaller models used wherever quality allows.
• →Privacy and GDPR: EU data handling, retention limits and no training on customer data.

What AI software costs to run

Model usage is an ongoing operational cost, unlike a one-off build. We size it during discovery — tokens per action, actions per user, expected users — so you know your gross margin per customer before you launch, and we design caching and model routing to keep it healthy.