Case Study · AI Go-To-Market
An AI SDR system that tripled qualified pipeline
Replacing spray-and-pray marketplace acquisition with an AI outbound and qualification engine that cut acquisition cost by forty-four percent.
Client: Meridian Trade
3.1x
qualified buyer pipeline
-44%
cost per qualified account
31%
repeat-order rate after retention rebuild
The problem
Meridian Trade was buying both sides of its marketplace with paid spend. Buyer acquisition costs kept climbing while most acquired accounts ordered once and disappeared. The growth team was scaling a leaky bucket.
The strategy
Replace volume with intent. We designed an AI SDR system that identifies high-intent buyer segments from platform and external signals, researches each account, and runs personalised outreach that reads like it came from a knowledgeable trade specialist, because the underlying data is real.
The execution
- 01Buyer-intent model trained on two years of order and browsing behaviour
- 02AI research and personalisation layer generating account-specific outreach
- 03Qualification agent scoring responses and routing hot accounts to the sales team
- 04Automated reorder journeys triggered by consumption-cycle predictions
- 05Weekly conversion tuning against cohort data for two quarters
The outcome
Qualified buyer pipeline tripled while cost per qualified account fell forty-four percent. Combined with the retention rebuild, Meridian reached its first contribution-margin-positive quarter. The system now runs with one operator overseeing what previously occupied a team of five.
Facing a similar constraint?
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