Most AI agent tools start from a generic support template and ask your store to adapt to it. We thought that was backwards. A fashion brand's sizing questions and an electronics store's compatibility questions have almost nothing in common — so why should the agent that answers them?

Wrennon starts with your catalog, not our template. We sync your products, orders, and policies — including the exceptions and edge cases that never make it into a FAQ page — and build an agent around that reality. Ecommerce is where we started, and it's still our focus; the same approach extends to other operational workflows once a storefront agent is live.

We're a small, focused team — not a call-center-sized operation pretending otherwise. That means every build gets direct attention from the people actually engineering it, not a support queue. It also means we're upfront about where we are: an early-stage company, growing deliberately, choosing depth on each customer over breadth across thousands.

How we work

From your catalog to a live agent.

I.

Audit your setup

We review your storefront, catalog, current tools, and how your team actually handles support today — before writing a single line of the build.

II.

Build & train

The agent is trained on your real catalog, policies, and brand voice, wired into the systems you already run, then tested before anyone sees it.

III.

Launch & keep improving

We stay close after go-live — watching performance, tuning responses, and retraining on real conversations as your store grows.

What we believe

A few things we won't compromise on.

I.

Your process, not our template

If an agent doesn't fit how your team actually works, it's the agent that's wrong — not your process.

II.

Visibility over magic

You should always be able to see exactly what the agent said and why. No black boxes.

III.

Built to keep improving

Launch isn't the finish line. We treat every real conversation as training data.

IV.

Honest about limits

If something isn't a fit, or we're not there yet on a certification or feature, we'll say so — not oversell around it.

Want to see how we'd map yours?

Tell us about your process. We'll show you what an agent for it could look like.