In this exclusive interview, Tiger Analytics Co-founder Pooja Agarwal traces the company’s evolution from solving localized analytics problems to orchestrating global, cloud-scale AI transformations. Discover why the conversation has shifted from experimental AI pilots to full-scale production workflows, how AI exposes critical data foundation gaps, and what it takes to scale a specialized global team to over 7,000 AI-ready professionals.
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Tiger Analytics is pioneering a shift from traditional, static AI models to probabilistic, self-learning systems that can assess and communicate their own levels of certainty. By incorporating techniques like Bayesian modeling and ensemble approaches, AI doesn’t just predict outcomes but it also quantifies how confident it is in those predictions. This “certainty code” allows businesses in high-stakes industries such as finance, healthcare, and manufacturing to make more informed decisions by understanding the risks and variability involved. The approach reflects a broader vision of building AI that is not only intelligent but also trustworthy and transparent.
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