AI development services that ship to production — not the demo folder.

Yes — Llama 3, Mistral and others where privacy, cost or latency require it. We also use OpenAI, Anthropic and Gemini when they're the right tool.

Grounding with RAG, structured outputs, evals on a golden set, guardrails, source citations and human review where accuracy matters.

Pricing is quoted after discovery based on scope, team shape and delivery timeline. A scoped chatbot MVP, production RAG system and enterprise ML platform are each priced differently. Model usage is billed at cost through your own accounts and is never marked up.

Yes — we deploy with open-source models like Llama 3 and Mistral on your own infrastructure. Local vector databases (pgvector, Qdrant) keep all data on-premise. This is common for healthcare, legal and financial services clients.

RAG retrieves your documents at query time — best for knowledge that changes often. Fine-tuning trains the model on your data — best for consistent tone, format or specialised tasks. We often combine both for production chatbots.

We build a golden evaluation set before launch, run automated regression tests on every release, track per-answer quality scores, and set up human review loops for high-stakes workflows. Accuracy is measured, not assumed.