The sales / GTM team was spending most of its time manually identifying, researching, and reaching out to prospects that matched its ideal customer profile (ICP).
Much of the work required to generate pipeline, from prospect discovery and qualification to prioritization, outreach, and post-meeting follow-up. It was repetitive, time-consuming, and difficult to scale. In an AI-native sales organization, people should focus on what humans do best: building relationships, understanding customer needs, and closing deals. Everything else that can be reliably automated should be.
What We Built 1. Codified the team's GTM knowledge and workflows We started by capturing how the team defines a qualified deal, identifies high-potential prospects, prioritizes opportunities, and manages the sales process from initial outreach through to post-signature. We translated that knowledge into a structured, end-to-end workflow using Palantir's framework of Objects, Links, and Actions. 2. Unified fragmented data into one system of understanding The team relied on multiple systems of record and disconnected dashboards to access the information needed to do its job. We brought that context together into a unified system of understanding, accessible through each team member's preferred AI assistant, whether Claude, ChatGPT, Gemini, or Copilot. Instead of switching between tools to find information, the team can interact with its business context in one place. 3. Integrated existing SaaS instead of rebuilding it Rather than building a CRM from scratch, we connected our custom AI software to existing SaaS platforms. We ran a bake-off across Attio, Day AI, and Lightfield to determine which CRM best fit the team's needs, evaluating it as the foundational system of record for customer relationships, deal context, and GTM execution. 4. Built iteratively with the sales team We developed targeted prototypes, tested them with the people who would use them, and incorporated their feedback at every stage. This ensured the solution reflected real-world workflows and addressed the team's actual needs, rather than assumptions about how sales should work. 5. Built toward a fully agentic GTM system The end state is a GTM system that automates the operational work across the sales lifecycle: - Prospect discovery: Identifies leads matching the ideal customer profile (ICP) using relevant intent and interest signals. - Qualification and prioritization: Evaluates prospects against the team's qualification criteria and prioritizes the highest-potential opportunities. - Intelligent meeting routing: Coordinates meetings with the sales reps best positioned to convert each prospect, based on industry, context, and expertise. - Automated sales execution: Supports the journey from initial outreach to proposal generation, producing proposals that reflect the sales professional's authentic voice and adhere to company brand guidelines. The outcome: A connected, AI-native GTM operation designed to reduce manual work across the sales lifecycle and free the team to focus on what drives revenue: building relationships, engaging prospects, and closing deals.
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