Kala AI came to Dink with a clear ambition and an early prototype: turn a promising idea into a real, production-grade AI platform that customers could rely on. The challenge was the one most early AI products face — getting from a demo that works in a controlled setting to a system that is accurate, affordable and maintainable at scale.
What we did
- Model selection, case by case. Rather than defaulting to the largest model, we chose models on the balance of cost, accuracy and privacy for each part of the product.
- Grounding in real data. We built retrieval over the platform’s own data so answers reflected reality instead of generic web knowledge.
- An architecture built to last. Monitored, versioned and documented from day one — so the platform could grow without becoming the legacy system nobody wants to touch.
The outcome
Kala AI moved from concept to a production AI platform with the engineering foundations to scale: predictable running costs, observable behaviour, and a codebase a senior team can keep evolving. The same discipline that took it to launch is what keeps it reliable in production.