A curated view of public engineering work that demonstrates breadth across model families and business/technical problem classes.
AI capability is difficult to evaluate from marketing copy alone. Buyers and technical stakeholders need evidence that a team can work across data preparation, model selection, evaluation, deployment constraints, and different modalities.
The portfolio brings together public projects in retail forecasting, SAM2 segmentation, DeiT classification, FairFace bias analysis, reinforcement learning, flight dynamics/control, Airbnb regression, and knowledge-graph QA preprocessing.
The result is an inspectable technical portfolio that makes capability concrete and gives prospects a better starting point for discussing their own use case.
These points describe implemented or publicly inspectable work. They intentionally avoid unsupported revenue, productivity, or client-performance claims.
Bring us the business problem. We can map the architecture, identify the first useful release, and define the path to production.