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What AI models do you use?
Whatever's right for the workload — Claude, GPT, open-weight models on your infra. Cost, latency, quality, and data residency all matter; we benchmark for the actual job rather than defaulting to the loudest model.
AI features that solve real problems, not demos. Smart search trained on your own corpus, chat assistants that handle actual workflows, and AI pipelines with cost controls and quality gates built in from day one.
Smart search and chat trained on your own content, docs, and data.
AI assistants that handle real workflows — bookings, support, lookups, summaries.
Cost controls and quality checks that keep AI features reliable as they scale.
Companies with a corpus to make searchable
Teams replacing manual support work with AI chat
Founders shipping an AI-native product end-to-end
Practice-specific answers. The full studio FAQ — pricing model, budgets, how engagements run — lives on /approach.
Whatever's right for the workload — Claude, GPT, open-weight models on your infra. Cost, latency, quality, and data residency all matter; we benchmark for the actual job rather than defaulting to the loudest model.
Customer data stays in your environment. We use vendor APIs only where they're contractually safe, or self-hosted models when they're not. Every engagement gets a written data-handling note before kickoff.
Yes — via retrieval-augmented generation (RAG) for most cases, fine-tuning when the data shape justifies it. We don't fine-tune by default; RAG ships faster and is easier to audit.
Hallucination is a quality problem, not a brand problem. Every shipped AI feature has measurable quality gates — recall, precision, latency, cost — and a deterministic fallback path.
We'll come back within one business day with the shape of the engagement, the team who'd run it, and a written proposal inside five.