We are model-agnostic by design. Every build starts with the cheapest capable model that clears the quality bar, and scales up only where the task demands it. Grounded, evaluated, and never locked to one vendor.
The model is the smallest part of a working AI product — but it is where budgets quietly bleed. We treat model selection as an engineering decision, not a default.
Picking a model is a cost-versus-quality trade-off made per task, not per project. A classification step, a summarisation step, and a customer-facing reasoning step have completely different requirements — so we route each to the right tier. The result is production AI that is grounded, measured against real evaluation sets, and typically a fraction of the cost of a single-model build. Across our own fleet of 33 built products, this discipline is why 18 run live in production without a runaway inference bill.
We climb the ladder only when the task needs it. $0 local and open models first; a strong paid model only at the quality gate.
We keep several model families in active rotation so we can match capability to task and never depend on a single provider’s pricing, availability, or roadmap.
Selection is evidence-based. We benchmark candidate models on your task with real evaluation sets, score them on cost, quality, and latency, and let the numbers pick the winner.
Every technology below is delivered by the same composed engineering team.
Tell us the outcome. We’ll pick the model ladder, ground it in your data, and ship it gated end to end.
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