(07) 2025 · AI

Rippling

Built an AI powered outbound system that researched prospects, personalised sequences and booked meetings automatically. Sales team went from 20 to 64 qualified calls per month.

Rippling
Live case study
Industry
SaaS · HR Tech
Region
United States
Duration
45 days
Outcome
3x pipeline growth from AI outbound in 45 days
01
The challenge

Where they were stuck.

Rippling SDRs were spending 60% of their day on manual research and personalisation. The team of 6 was sending 300 emails per week with a 1.2% reply rate. Leadership had approved headcount to double the team but first wanted to see whether automation could squeeze more from the existing team.

02
The approach

What we did about it.

We built a Clay powered research pipeline pulling prospect data from LinkedIn, funding news and job postings to generate a personalisation brief for every lead. That brief fed into an AI agent writing unique subject lines and first lines per email. Sequences loaded into Smartlead and a Make agent routed hot replies directly into SDR calendars without any manual touch.

03
The outcome

What changed.

Reply rate jumped from 1.2% to 4.8% within three weeks. Qualified calls went from 20 to 64 per month without adding a single rep. Each SDR reclaimed 3 hours daily. Leadership cancelled the headcount approval and redirected budget to closing resources instead.

Results

Numbers
that mattered.

4.8%
Reply Rate
01
64
Qualified Calls Monthly
02
3hrs
Saved Per Rep Daily
03
3x
Pipeline Growth
04
Trend
Qualified meetings booked per month across the 45 day rollout.
64
Latest
Breakdown

The qualified up close.

Plotted across the engagement window. Each checkpoint represents a measurable shift in the underlying metric.

20
28
38
52
64
Stack & tools
ClaySmartleadMakeOpenAI APIHubSpot

We cancelled the SDR headcount after seeing the numbers. Same team, same budget and 3x more pipeline. The AI research layer completely changed our outbound motion.

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