Building the outbound engine behind
37 new logos

Outbound was manual. Reps spent most of the week prospecting instead of selling, and generic messaging was pulling under 1% response rates. Every campaign looked the same regardless of who it was sent to.

There was no unified data across the tech stack, no AI in the sales process, and no consistent pipeline to forecast against. Leadership couldn't tell which accounts were worth chasing and which weren't, so reps chased everything. New logos had plateaued at a handful per quarter, and there was no clear reason why that number wasn't moving.

We integrated 14 data sources into a single account view and built scoring models on the firm's own historical deal data, so accounts were ranked by fit rather than assumption. Reps stopped guessing which accounts to prioritize and started working a list that was already ranked for them.

We then built vertical-specific messaging and 127 campaign variations across company size, sub-industry, and tech stack, with intent signals tracked across 1,000+ platforms to time outreach to active buying behavior. Instead of one message sent to everyone, each account received messaging built for its industry, its size, and its stack.

The firm closed 37 new logos and $5.7M in net-new revenue, with 68 qualified opportunities still active in pipeline. Meeting acceptance rate moved from 11% to 47%. Win rate moved from 18% to 34%, and average deal size held at $155K.

Deal cycle length dropped by 31%, from 127 days to 88. Pipeline was no longer a lagging indicator that showed up after the fact — it was a number leadership could plan a quarter against.