The model isn't your advantage. The integration is.

Enterprise LLM spend has shifted meaningfully between providers over the past two years, and that pattern isn't over — market share between the major model providers keeps reshuffling as capability and pricing shift. Companies that hard-wired their product to one provider's SDK, one proprietary format, one specific model version, are the ones that have to do an expensive rewrite every time the market moves.

The second problem is cost blindness. Model API costs scale with usage in ways that are easy to underestimate at prototype stage and genuinely painful at production scale — teams routinely discover their AI feature costs ten times what they modeled, because nobody accounted for retry storms or unbounded context windows. The third problem is context poverty. A model call with no access to your actual data produces generic, sometimes wrong output.

We build an abstraction layer between your product and the underlying model provider, so switching from one frontier model to another — or running different models for different tasks based on cost and capability tradeoffs — is a configuration change, not an engineering project. This also means we can route different request types to different models: a cheap, fast model for simple classification, a stronger model for complex reasoning.

Cost control is engineered in from the start: token budgets, caching for repeated queries, rate limiting, and monitoring that alerts before a cost spike becomes a bill you didn't expect. And every integration is built with real context injection — pulling from your actual databases, documents, and APIs at request time — so the output is grounded in your business.