Enterprises adopting generative AI face a trade-off: maintaining high precision (16-bit) for trustworthy results at prohibitive costs, or compromising quality with low precision (e.g. 4-bit) to manage expenses. Quantization, while cost-effective, can introduce quality losses in average and outlier performance, which are difficult to evaluate in generative AI (e.g. media gen, or trading systems). This dilemma hampers adoption in critical enterprise applications, where trust in AI results is paramount - as enterprises are moving towards AI as co-pilots and pilots.

Recogni introduces Pareto, a groundbreaking AI math approach leveraging logarithmic scales to eliminate costly multiplications—transforming them into simple additions at no loss in precision. Pareto accelerates models in true 16-bit, at the cost of running it in 4-bit on available systems, enabling enterprises to scale generative AI affordably while retaining high-quality outputs.