A few examples of what production AI work looks like. Details are anonymized to protect client confidentiality, but the outcomes are real.
Large enterprise with complex forecasting needs and high cost of error. Inventory and revenue decisions depended on forecasts that weren't reliable enough.
Organization deploying LLMs in sensitive workflows where errors could have real consequences. Needed a way to trust the outputs.
Mixed technical and non-technical teams starting to adopt AI tools. Everyone had different expectations and comfort levels.
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