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The problem

AI capabilities for revenue teams are advancing fast, but the ability to actually deploy AI into real sales workflows hasn’t kept pace. The gap between what AI can do and what teams have shipped is growing, and early movers are pulling ahead. RevOps and GTM Engineers are the people best positioned to close this gap. You understand your team’s workflows better than any vendor. You have access to connect data sources and deploy tools. But there are real obstacles in the way. Data fragmentation The data you need is scattered across CRM, calls, email, calendar, and Slack. No single tool has the full picture. Agents give incomplete answers. Automations miss key context. You’re left manually connecting dots that should be connected automatically. Buy vs. build You can buy a sales AI product, but it’s built for the average company, not yours. The features don’t match your workflow. The insights miss your context. And when it doesn’t fit, you’re stuck waiting on a roadmap that may never prioritize your problem. Or you can try to build something custom, but that means competing for engineering resources or settling for brittle workflows that break as your process evolves. The path that should exist There should be a third option: build it yourself, with full context, production-ready and secure, in hours not months.

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What we believe

  • Context is everything. Agents are only useful if they have access to the full customer picture — not just CRM data, but calls, emails, calendar, Slack, and more.
  • One size fits none. Generic features built for the average company don’t work for anyone. Teams need agents tailored to their specific workflows.
  • RevOps should ship solutions, not manage integrations. Technical operators on revenue teams shouldn’t be limited by the lack vendor features or limited engineering resources.
  • Usage pricing beats seat licenses. You should pay for value delivered, not headcount. Seat-based pricing creates rollout friction and misaligns incentives.
  • Iteration beats perfection. Ship a working agent fast, collect feedback, and improve. That beats waiting months for a “perfect” solution.

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Next steps