AN OWNED SYSTEM · DESIGNED WITH AI, RUN WITH JUDGMENT

Product data is a system problem. So I built a system.

Most stores run on product data they can't fully see — supplier feeds, spreadsheets, an app for every ad channel, and nobody sure which number is true. DRYVN Commerce is the system I built to end that: one clean catalog, published to every channel that sells, watched by something that never gets bored.

The problem, stated plainly

Five sources. Zero agreement.

An online store's product data lives in five places and agrees in none of them. The supplier's feed says one thing, the storefront another, Google Merchant Center a third. Products get disapproved and nobody notices for weeks. A promo price leaks to an ad channel after the sale ends. Customers search for what you sell, get zero results, and leave. None of this shows up on a dashboard as a single red light — it shows up as revenue that quietly doesn't happen.

What the system does

It moves product data the way it should move.

It ingests from wherever the truth lives — the store, a distributor's file drop, a supplier's API — normalizes everything into one canonical catalog, and publishes it to the channels that sell: Google, Microsoft, Meta, TikTok. Prices, promo windows, identifiers, compatibility data, compliance flags — carried correctly, kept current.

Then it does the part feed tools can't.

Because it runs on the same data foundation as the rest of my operating system, it sees how buyers actually behave — what they search for and don't find, where navigation dead-ends, which channel disapprovals trace back to which catalog gaps. Every week it turns that into specific, evidenced recommendations: fix this category mapping, restructure that collection, add these search synonyms. Safe mechanical fixes it applies itself — behind an approval gate. Structural changes get a plan, not a silent edit.

How it's built

The method is the proof.

One catalog, source-stamped.
Every field knows where it came from and how much to trust it. A distributor feed can fill a gap; it can never overwrite what a human curated. Safety warnings can be added by any source and removed by none.
Guarded writes, provable dry-runs.
Nothing pushes to a live ad channel without passing a target allowlist that’s enforced three separate times. Dry-run mode provably makes zero external calls. Every sync is designed to be auditable after the fact.
Refuses bad data on its own.
When an inbound feed shrinks or mutates beyond safe thresholds, the system halts the sync instead of propagating the damage — then tells a human. This isn’t hypothetical: it recently caught a corrupted supplier file that would have silently erased most of a store’s catalog from its ad channels. The gate held, twice, until the supplier fixed their export. That’s the difference between a feed tool and an operating system.
Recommend first, write second.
The intelligence layer earns trust by showing its evidence in a weekly packet before it ever touches anything. Human judgment stays in the loop where it belongs.

Built with an AI team

Specified, vetted, and built by AI — deliberately.

This system was specified, reviewed, and largely built by AI — deliberately, and with the gates showing. The product spec was drafted by an AI thinking partner, then adversarially vetted by three AI specialists: a catalog domain expert who caught a compliance-handling gap, an engineer who verified the code and corrected a platform-rules assumption against live documentation, and a strategist who pressure-tested the commercial framing with verified market data. Every finding was incorporated before a line of new code was planned. The build itself runs on an AI fleet with human approval gates at every consequential step.

That's the actual thesis of this site: AI doesn't replace operating judgment — it makes judgment enforceable at scale. A system like this is what that looks like when it's real.

Where it fits

DRYVN Commerce is one system inside DRYVN — the operating discipline I run businesses against. The catalog it maintains is the same catalog the AI co-worker reads, the weekly proof packet reports on, and the ad channels sell from. One source of truth, consumed everywhere.

Wondering what your product data is costing you?

Talk to Brian