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one from scratch.

The Sales Team Stopped Processing Emails. They Started Closing Deals.

A global freight forwarder was losing thousands of hours a year to one bottleneck: someone had to read every inbound quote request, track down missing details, and package it before pricing could begin. Every minute spent on intake was a minute not spent closing. We eliminated that workflow entirely.

A five-stage AI pipeline that reads, classifies, extracts data, follows up conversationally for missing information, and routes a fully formatted RFQ package to the pricing team — without a human touching a single email.

Every Dealer. Every Model. Every Question. Answered in Seconds.

One of the world's largest automotive manufacturers needed consistent, complete product knowledge across a dealer network spanning multiple continents. Buyers were arriving more informed than ever, asking precise questions about configurations, towing specs, and warranty terms. Dealers were often unable to answer on the spot. That gap was costing deals.

A custom AI assistant trained on the full vehicle catalog — every model, trim, option package, and configuration dependency — deployed globally, responding in any language, with zero lookup time on any question or comparison.

Every Agent Was Looking at the Same Listings. We Changed That.

A national real estate SaaS platform was sitting on a competitive advantage buried in their own data. Buyers were signaling intent through their behavior — returning to listings, narrowing searches, spending time on floor plans — and no one was reading it. Every agent on every platform was waiting for the same hand-raise from the same buyers.

A behavioral ML engine that reads buyer activity across the platform, weights each signal by intent strength, and surfaces a ranked, continuously updated lead feed. Agents see the best buyers in their market before those buyers contact anyone.

A Full AI Intelligence Layer Built Into a Financial Services Firm.

A financial services firm was spending 4 to 6 weeks producing reports that were outdated before anyone acted on them. The data existed. The analysis did not. What started as an efficiency problem became something larger: four interconnected AI capabilities that the firm eventually turned into the foundation of a standalone company.

Deep financial analysis across 21 metrics, GAAP compliance scanning, industry benchmarking, and ML cashflow forecasting — connected, automated, and deployed across more than 500 professionals.

An ML System That Finds the Right Path for Every Student. Not Just the Average One.

Students with disabilities have always been handed the same curriculum paths as everyone else and told to make it work. A software company awarded an NSF grant needed a validated ML model that could read each student's individual profile and prescribe the sequence most likely to lead to real employment outcomes. Federal funding renewal depended on the results.

A recommendation engine using collaborative filtering, K-nearest neighbors, and graph convolutional matrix completion — tested against three ML architectures and validated on real student data to prescribe curriculum aligned with specific career goals.