Work that had to hold up in production.
A selection of engagements where AI and data systems had to perform reliably, not just demo well.
Product catalog normalization and entity resolution
A fragmented product catalog made search, pricing, and reporting unreliable across millions of listings.
Outcome[X million records] normalized across [N source systems]
Read the engagementModel reliability program for a machine learning platform
Production models drifted quietly, and the team had no consistent way to know when a model had stopped behaving.
Outcome[N production models] brought under monitoring across [M business lines]
Read the engagementApplied AI and machine learning training for engineering teams
Engineering teams were expected to adopt AI and ML tooling without a shared, practical foundation to build on.
Outcome[X-person training cohort] across [N engineering teams]
Read the engagement