Selenium

Selenium began as client work rather than as a product idea. Rebuilding the data layer for a retail partner produced something we had not set out to build: a way of reading customer and commercial data as one system rather than as a stack of disagreeing dashboards.

That engagement worked well enough to raise a different question. If the pattern held once, did it hold generally?

Mid-sized e-commerce brands are drowning in dashboards and starving for decisions. The tooling problem was solved years ago; every platform reports its own numbers confidently. The decision problem was not, because those numbers disagree and nobody owns the reconciliation.

Enterprise-grade intelligence existed, at enterprise prices and enterprise implementation timelines. For a brand doing $1M to $50M that is not a budget question so much as a shape question: the thing on offer was built for a company with a data team.

Productise the pattern rather than sell it again as a service. A service scales with our hours; a product scales with the problem.

Build it AI-native rather than adding AI to a reporting tool, because the value is in the layer that connects the stack, not in the layer that displays it.

Product strategy, brand and build, shipped as a working platform rather than a pilot.

A connective layer across the commerce stack, so the numbers agree before anyone tries to read them.

Surfacing the insights and actions a team would otherwise spend days assembling by hand.

Selenium is in market with active customers, one of three AI products the practice operates rather than observes.

The engagement that produced it remains the reference case: Femme Connection, whose net profit rose 64% across two years once its data read as one system.