What we do

We focus on verifiable and deployable ML use cases: data wrangling, feature engineering, model training, basic evaluation, and exposing inference via simple APIs. We prioritize concrete business problems (classification, clustering, demand forecasting) and build PoCs using available or small datasets, delivering performance reports and implementation recommendations. For constrained environments, we advocate starting with off-the-shelf frameworks and lightweight models while accumulating data.