We’ve been building data infrastructure to beat Amazon since 2008 — today’s AI tools are just the latest version of the same instinct.
Lawrence, a data-driven marketer with a background in corporate finance, pricing strategy, and operations, teams up with Matt, a developer with deep data chops. The first product isn’t a service deck: it’s pricing and demand-planning tools built on per-category demand curves correlated against sales rank.
We launch our own retail operation and grow it into a $20-million-a-year business across five verticals — beauty before FBA existed, sports nutrition with our own store in Los Angeles, pet gear, and more. Every tool we built got tested on our own inventory and our own margin. We called ourselves a big-data company, with a straight face, before “big data” was a phrase anyone put on a website.
We hire an MIT machine-learning PhD to push the predictive models further. It doesn’t work — we’re a decade early and the tooling isn’t there yet. But the instinct was right.
We sell the treats brand we built from scratch (GCP) — the full arc, owned end to end. By now the retail operation has taught us more about Amazon’s mechanics than any client engagement could, and channel management for other brands has become the business.
That data orientation merged with what’s now possible. An in-house technology team, led by co-founder and Technology Visionary Matt, focused entirely on the data and AI layer.
What we run is specific: a unified TESMO data warehouse spanning every account we manage, with MCP servers and a TESMO-built toolset on top of it — built and maintained by our own engineering team, not licensed from a vendor. Not sure that’s what others mean when they talk “AI infrastructure,” but it’s what we mean.
What that toolset does across every brand, every day: diagnostics, reporting, demand and inventory planning, competitive and pricing intelligence, and anomaly detection that flags problems before they surface in a standard report.
AI is the substrate, not the strategist. The model is deliberate:
Where the channel goes, and why — set by the principals you hired, not delegated down.
What’s leaking, what’s at risk, what’s possible — read by senior eyes that have seen it before.
Catalog, ads, inventory, and brand protection — run by senior operators who know your products.
The data warehouse and toolset that make the first three faster, more rigorous, and consistent across a whole portfolio.
One hard rule: anywhere a decision touches money or price, the guardrails are enforced in code — not in a prompt, not in someone’s memory. The machine surfaces and recommends; a human commits. Always.
What brands have always valued about TESMO is that the principals are in the room. The risk in that model is that senior attention is finite. The toolset is how we square it.
Heavy, repeatable analysis runs across every account, every day — so nothing waits for someone to get around to it.
Our operators don’t burn hours pulling reports. They spend them on the calls only experience can make.
Because the cracks are watched by code. Anomalies surface in hours, not at the quarterly business review.
The result: a boutique’s attention with the infrastructure of something much larger.
Book a call and we’ll point our tooling and senior operators at your channel — catalog, competitive position, and advertising — and give you a candid read of what we see.
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