AdChat started with a lawn mower problem.

In 2016, Cleveland-based agency Marcus Thomas hired us to build a Facebook Messenger experience for Troy-Bilt. The brief was straightforward: shoppers standing in a Home Depot aisle, looking at a row of lawn mowers that all looked roughly the same, needed help figuring out which one was right for them.

We built a Messenger app that asked shoppers about their property and lawn care needs and recommended a mower. They could compare models side by side, explore features, and get to a confident decision without a sales associate. It worked well enough that Troy-Bilt expanded it to snow blowers and other equipment. It won Best Use of Artificial Intelligence at the Content Marketing Awards.

The more interesting thing it taught us was what genuine engagement looked like. When people could explore at their own pace, in a format that felt familiar, they paid attention. They spent real time. They left with more confidence than a banner ad or a product page visit would ever generate.

That insight is what became AdChat.

We took that interaction model and rebuilt it as a programmatic ad format. Then we added a curated supply path to improve inventory quality. Then a measurement and optimization layer to turn every campaign into a cleaner signal for the next one.

After billions of impressions and hundreds of campaigns, we’ve refined the creative, supply, and measurement levers that drive performance. The agencies and brands that run AdChat tend to keep running it.

We also built MixBright, a separate platform that helps agency and marketing teams turn brand, category, and audience research into structured strategic outputs. We’ve built an application that maps those outputs to contextual targeting segments, which can be applied directly to an AdChat PMP Deal ID. Some clients use it at the start of a campaign to sharpen targeting decisions. It’s a different product, built by the same team, solving a related problem.