Customer stories
Different teams, different motions. Each story frames the motion, the challenge, and the fit — then proof in the customer's own words.
Revenue and operations functions- Motion
- GTM strategy & architecture · advisory to revenue leaders
- Challenge
- Most stacks offer data or single-task automation—no operating layer that connects signal, context, and execution across the lifecycle.
- AI-native fit
- Full AI-native platform frame (awareness, automation, empowerment)—practical infrastructure for autonomous revenue motions, not another point tool.
In their words
“Bruce represents the next generation of AI-native GTM operating infrastructure.”
SB
Scott Bergquist
Managing Partner · Mountain Branch Consulting
Senior GTM executive at IBM, Cisco, and VMware
- Motion
- Enterprise platform AE · high-volume cold calling
- Challenge
- Manual prep, account prioritization, research, and dial-session overhead consumed time that should go to live conversations.
- AI-native fit
- Awareness across accounts plus automated execution of the full outbound block—prioritization, talking points, and session flow—so the rep stays on quality calls.
In their words
“Bruce takes a lot of the grunt work out of cold calling.”
WR
Wesley Randall
Enterprise Platform Account Executive · eHub
Previously at Contextual, Route, and RollWorks
- Motion
- VP Sales · complex enterprise demand gen (cybersecurity)
- Challenge
- Multi-step demand-generation workflows with heavy context—hard for the team to stay aligned on the right plays at the right time.
- AI-native fit
- Unified awareness across the motion and empowerment for sellers—deep context connects the dots so the team executes consistently, not in silos.
In their words
“Bruce helps us tame highly complex demand-generation workflows in an AI-native way. It connects the dots with deep context and empowers our team to consistently make the right plays.”
- Motion
- SVP Sales · scaling a mid-market enterprise SaaS team
- Challenge
- Reps lost hours to demand gen and deal prep; inconsistent preparation and reflection before and after calls.
- AI-native fit
- Automation of manual GTM work plus just-in-time empowerment—sellers spend more time in high-value conversations and the team baseline rises.
In their words
“Bruce gives sellers leverage. By automating the manual parts of demand generation and deal prep, it frees reps to spend more time in high-value conversations instead of busywork.”
- Motion
- VP Sales · mid-market B2B; rolling out AI without adding process debt
- Challenge
- Most AI SDR tools become another system to learn; reps still lacked real context going into meetings.
- AI-native fit
- Platform that plugs into how the team already operates—awareness for meeting prep and human leverage on calls, not a siloed outbound bot.
In their words
“What stood out immediately was that Bruce didn't create more work for my team. Most AI SDR tools turn into another system to learn. Bruce just plugged into how we already operate and actually gave me time back.”
- Motion
- CEO · enterprise SaaS pipeline and follow-up discipline
- Challenge
- Sellers spending up to 40% of time on follow-ups; missed or weak follow-ups directly translated to lost revenue.
- AI-native fit
- Automation on follow-up execution plus empowerment before and after calls—timely, high-quality touchpoints and a stronger motion across the team.
In their words
“One of the things I value most about Bruce is its ability to eliminate one of the biggest hidden drains in enterprise sales: follow-ups. Sellers routinely spend up to 40% of their time chasing responses, and missed follow-ups directly translate to lost revenue. Bruce ensures follow-ups are high quality, timely, and never dropped.”