Conference Presentation
Dr. Uri Yerushalmi, Co-Founder&CAIO, Fetcherr: How AI Driven Decision Maximizes Business Performance
- The company plans to continue developing large market models over the coming years, applying deep learning principles to market data to output anticipated market dynamics inspired by algorithmic and high-frequency trading decision-making processes.
- These models are expected to predict future market dynamics by consolidating internal and external data, including business transactions, orders, prices, capital markets, and competitor behavior, while utilizing a simulation engine that scans daily or hourly influences on demand, inventory, and market share.
- The systems are designed to identify probabilities for specific market dimensions, such as demand value, elasticity, and competitor pricing, to proactively anticipate shifts rather than merely reacting to market hints.
- Decision-making policies selected by the system will target maximal reward in terms of revenue, market share, or profitability, with the expectation that implementation will yield revenue uplifts of more than 10% across every scenario and customer.
- While generative AI tools have not yet demonstrated significant business growth and large language models are deemed insufficient due to their qualitative nature, the company positions its quantitative approach as the missing link to closing this gap and driving revenue optimization.
- Unlike traditional strategies relying on human resources, size, or speed, the company asserts that future competitive survival depends on access to AI decision-making tools that allow specific market players to access underlying market structures and move before the market moves.
- Deployment of this technology is expected to result in outcomes with significant statistical significance in A-B testing comparisons and to reduce human resource costs, as the models are designed to optimize operations rather than replace or imitate human decision-makers.