Most AI in RevOps lives in demos. I build systems that sit inside your real workflow: the CRM your team opens every morning, the reports leadership reads on Mondays.
Most mid-market teams have more AI tools than they know what to do with. The ones that see results aren't the ones with the best tools. They're the ones with the best underlying systems for AI to layer onto.
If your pipeline stages aren't clean, AI lead scoring is guessing. If your handoffs are murky, AI routing makes the problem faster. If your data is inconsistent, AI forecasting generates confident-sounding wrong numbers.
The work starts with the system. Then AI becomes leverage.
Talk Through Your Stack →Models trained on your closed-won data to rank leads by actual conversion likelihood, not gut feel.
Record, transcribe, and analyze sales calls to surface patterns in what's winning and losing.
Forecast models accounting for deal velocity, historical close rates, and rep performance.
AI-personalized multi-touch sequences triggered by buying signals, without manual intervention from reps.
Automated monitoring of competitor moves synthesized into actionable weekly briefs.
The full architecture: ICP, channel strategy, handoff logic, designed to feed clean data into every AI layer above it.
We start with the foundation. Then we layer in the intelligence. That's the sequence that actually works.
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