[ Case study · Sales intelligence ]
Prospects that show up before you go looking.
A prospecting agent that learns who a rep wants to reach in a five-minute chat, reads billions of third-party intent signals overnight and drops a ranked list of in-market buyers into the inbox every morning.
Your leads, 7:00 amExample · 5 new
- 96Head of Revenue, Series B fintechHiring 4 SDRs this month
- 91VP Sales, logistics SaaSVisited pricing page 3 times
- 88CRO, healthtech scale-upRaised a $24M round on Tuesday
- 82Sales Director, HR platformSwitched CRM last quarter
- 77Founder, devtools startupPosted about outbound tooling
What Changed for Sales Teams
- Research time
- 70%
- less time spent building prospect lists per rep
- Reply rate
- 3.1x
- the reply rate of the team's previous cold outbound
- Onboarding
- 5 min
- from sign-up to a trained, working agent
- Signal volume
- 35B
- intent signals processed every single day
Figures reflect pilot customer averages during the first quarter after launch.
A US revenue intelligence company selling to B2B sales teams who are tired of cold lists, stale contact databases and reps spending half the week on research instead of conversations.
- Sector
- B2B sales & revenue tech
- Market
- United States, enterprise
- Data scale
- 35B signals a day
What Stood in the Way
- 01
Lead lists went cold before anyone called
Static databases showed who fit the profile, not who was actually shopping. Reps burned their best hours dialing people with no reason to talk.
- 02
Intent data was big, noisy and expensive
Third-party signals arrived by the billion. Without serious filtering, the useful ones were buried under bots, job seekers and competitors.
- 03
Setup took weeks of ops time
Traditional tools needed ICP workshops, filter trees and a RevOps person to babysit them. Most teams gave up before the first good lead.
- 04
Nothing learned from the outcome
Whether a lead closed or ghosted, the next day's list looked exactly the same. There was no loop between results and targeting.
An agent that does the research, so reps can do the selling
- 01
Onboarding by conversation
Reps chat with Stuart, the onboarding agent, for five minutes. He asks the right questions and trains a personal prospecting agent from the answers.
- 02
Signal filtering at scale
A multi-stage pipeline scores behavioral, social and firmographic signals and discards noise before a single name reaches the rep.
- 03
Morning delivery
Scored and ranked prospects arrive in the inbox or CRM each morning with the context behind every pick. Set it once and it keeps running.
- 04
A loop that sharpens
Wins and misses flow back from the CRM, and each agent re-weights its own model so tomorrow's list is better than today's.
From a five-minute chat to a daily pipeline
Four stages between the first conversation and a booked meeting.
- 01Rep briefs the agentTarget accounts, roles and buying triggers captured in plain language, no filter trees.
- 02Signals collectedBillions of daily web, social and company-activity signals ingested and de-duplicated.
- 03Scored and rankedEach prospect scored for fit and timing, with the reason attached so reps know the opener.
- 04Delivered and learnedLeads pushed to inbox or CRM, outcomes fed back to retrain the agent overnight.
Built with
Before and After
Before
Reps pulled lists from a static database, guessed at timing and spent hours researching every account before a single call.
After
A trained agent delivers in-market buyers each morning with the reason attached. Reps open the inbox and start conversations.
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Delivered in this engagement
- Conversational onboarding agent
- Intent signal scoring pipeline
- CRM sync and feedback loop
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