Thirteen engagements across CPG, retail, financial services, technology, education and social impact — grouped by what was actually delivered. Client names are withheld where the work was under a firm's brand or an NDA.
A machine-learning framework that scores users and accounts on their likelihood to adopt new AI-driven product features. Marketing uses the scores to target the organizations most likely to move, and the client tracks readiness as a measurable metric rather than a guess.
A predictive model that finds the true purchase influencers inside client organizations from behavioral and firmographic signals — removing the dependency on CRM hygiene and giving sales precision targets for renewals, upsells and product launches.
Convolutional neural networks and clustering that extract the primary and secondary colors of every product image in a catalog, turning imagery into a structured trend signal merchandising and design teams can act on.
Built and deployed machine-learning models that let a financial institution rethink how it assesses portfolio risk — moving from static rules to data-driven scoring that surfaces exposure earlier and supports better-informed decisions.
Predictive models for a top-tier financial institution that identified the defining traits of successful investment bankers from historical hiring and performance data, streamlining a recruitment process that had relied on instinct.
Interactive reports with prediction, optimization and simulation models embedded directly in the dashboard — so business users explore scenarios and get model output in the same place they already look at their numbers.
A proprietary association-rules model that reveals product relationships even from single-item transactions, where conventional basket analysis finds nothing. A global footwear and apparel maker used it to drive attach rates and cross-sell online.
A brand-health model combining machine learning with consumer analytics to measure brand performance, customer retention and cross-brand engagement across a portfolio — the evidence base for marketing and growth decisions.
Customer segmentation and historical sales analysis for a multinational beverage and brewing company, surfacing the demographic patterns behind who buys what, where — and turning them into targeting that lifted product sales.
Data engineering plus a curated self-service environment in Tableau, so business users could explore trusted data, derive their own insights and share findings — replacing a queue of one-off report requests.
Predictive modeling for a Texas school district that identified which students would benefit most from support programs, improving pass rates on state-mandated assessments by directing limited resources where they mattered.
An operating framework for deploying generative AI in customer support for a global e-commerce client — where it fits, where it doesn't, how to govern it and how to measure the result — grounded in a working prototype rather than a vendor demo.
Tasked with reducing waste by 7% at a global confectionery, beverage and snack company, the answer was disciplined descriptive analytics and business intelligence — finding where the waste actually was before anyone proposed a model.
Most of these started as a thirty-minute conversation about data that already existed.