Accuracy and compliance are the two big worries for any leader. The way we solve it is by grounding answers in approved data sources, running automatic checks for faithfulness and privacy, and logging every step of the process. For high-stakes use cases, we also add a human-in-the-loop. That way, you don’t just get speed - you get trust and auditability built in.

The fastest wins come when you link each model or LLM to a single clear decision; Like which customer to retain or which supplier to prioritize. Then measure the uplift with experiments or causal methods. Once you’ve proven value, you scale it through shared platform components. That’s how you move from pilots that impress in demos to solutions that actually shift P&L numbers.

We infact recommend starting off small, but it doesn’t mean overbuilding. Start small, with just the essentials: a registry for models, a way to monitor performance, and a governance layer. Even if you only have two or three use cases, this avoids costly rework later. And when you’re ready to scale, you already have the rails in place for a bigger AI portfolio.

You don’t have to choose between risk and speed. The smarter way is to bake in governance during the build: policy controls, safety filters, and automated checks for bias or drift. Reviews happen at stage gates, not at the end. That means your delivery teams stay fast, but compliance and oversight are always in play.