Databook Launches the GTM Decision System: Actionable Customer Context for Enterprise Revenue

Databook today launched the GTM Decision System, a reimagined offering that enriches the verified customer context graph and agentic workflows trusted by customers like Salesforce, Microsoft, and Databricks, while adding a new composable architecture for fast, fully customizable deployments.

The Decision System launches alongside an outcome-based commercial model that directly aligns Databook’s revenue with customer value. Databook collects a base fee and shares only in incremental growth: taking a fee strictly on revenue earned above the CFO’s financial plan, with zero variable cost on baseline commitments. Both the new system and pricing model are available now to new and existing customers. Every deployment includes Databook’s forward-deployed engineers and enablement leads working alongside the customer’s team.

Read More – Breaking Silos With an AI Revenue Operating Model

Solving the Account Context Gap

For decades, enterprise revenue software has been organized around individual opportunities rather than the customer’s actual reality. This approach not only prioritizes isolated deal milestones over the broader relationship, but it also positions key context about the customer as a whole to degrade as leads pass through various functional handoffs and across disconnected systems. Moreover, when an opportunity closes or a seller departs, institutional knowledge about that account leaves with them and is lost forever. The CRM never fully captures the nuances of the relationship.

Databook has anchored its technology on the account since its founding, driven by a core conviction: vendors only win when their customers win. Earning trusted advisor status requires deeply understanding a customer’s strategic priorities and financial pain and urgency—not just tracking an isolated deal pipeline. The GTM Decision System puts that foundational spine, the Databook Customer Context Graph, into the hands of the entire revenue team and makes it actionable across every workflow in the lifecycle. Context not only survives deal handoffs and seller turnover, but actively powers the core jobs every GTM function performs—from account scoring, planning, and ABM campaigns to whitespace analysis, renewal, expansion, and consumption.

“Enterprises have spent over two years buying AI capabilities and still aren’t seeing accelerated revenue growth,” said Anand Shah, CEO and Co-founder of Databook. “Most AI tools reason over a customer’s CRM or their emails or call transcripts, which record internal rep activity and seller-side bias rather than buyer reality—amplifying insight gaps instead of correcting them. Our conviction from the beginning was that we don’t win unless our customer wins. Taking a share of revenue above plan is what that conviction looks like when you mean it.”

“Anyone can produce a convincing interface now. Almost nobody can build what makes the output factually accurate and deterministic,” said Frank Wittkampf, Head of Applied AI at Databook. “What a customer builds on our platform sits on a verified context graph and encoded go-to-market judgment, with provenance behind every claim—the difference between a system that demos well and one that survives contact with the c-suite.”

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