Rainbird does decision automation for regulated work, and it states the problem it exists for plainly: your agents are clever, but not accountable.
That is the real barrier to AI in regulated industries. A language model can produce a defensible-sounding lending, underwriting or eligibility decision without any traceable chain of reasoning behind it – and in a regulated process, an unexplainable decision is not usable regardless of how often it is right. Rainbird encodes rules and reasoning so the path to each decision is inspectable, which is what a regulator or an ombudsman will actually ask for.
Accelerators provide pre-built starting points, there is a developer track, and RAKE is in early access. It starts free, which is unusual for enterprise decisioning software and makes evaluation straightforward. The trade against a general LLM approach is the familiar one: encoding rules is real work, and you get determinism and explainability in exchange for flexibility.
