Probably secures $9M to trade AI power for precision

Founder Peter Elias envisions a shift toward deterministic accuracy, aiming for the 99.99% reliability standard typical of traditional software. The company’s inaugural tool, designed for complex data science tasks, uses a "data science mech suit"—a harness system that checks every output against a strict validator. By training the LLM to function within these boundaries, the system forces the model to adhere to the dataset, effectively stripping away the ambiguity that leads to errors.

This architecture allows the platform to operate on AI models four classes smaller than current industry leaders. Because these models are computationally modest, they can run on local desktop hardware, bypassing the need for massive data centers and significantly slashing token costs. Elias argues that while major AI labs prioritize larger models to drive usage fees, his approach proves that superior engineering can achieve better results with less raw processing power. The company plans to scale this validation engine beyond data science into high-stakes sectors like accounting and medical diagnostics, where precision remains the primary barrier to broader enterprise adoption.