For RevOps and GTM leaders building AI agents on their revenue data, Upside is a data layer that reconstructs messy account, deal, and touchpoint history from CRM, email, and calendars into a clean graph, queryable via dashboards, MCP, or custom miniapps.
Presence & Market Position
#54 of 74
AI-Native GTM Challengers
Measures earned, engagement-weighted share of voice across the GTM voices panel. Methodology →
Upside is working to become the trusted data layer beneath agentic GTM, reconstructing fragmented account histories before agents analyze what drove revenue. Its market footprint is still early, but the practitioner conversation aligns closely with that position: data readiness, trustworthy analysis, and the need to repair CRM context before deploying agents. The company develops that story through practical builds such as send-time optimization and closed-lost account research. The brand is moving beyond attribution dashboards toward a broader “GTM brain” identity grounded in clean data, explainable deal histories, and governed agent access.
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Business Profile
Founded in 2024, Upside is an independent, 14-person software company built by former Branch operators who had previously created internal attribution systems while scaling that business. It sells a forensic-attribution and GTM data platform to teams including ServiceTitan, Cresta, Graphite, and Assembled. The company uses a sales-led enterprise model, combining software with an Insight Engineering services layer for custom outputs. Its current stage is that of a focused early company turning a founder-developed internal capability into a standalone data and analysis platform.
Agent Readiness
Measures how easily your agents can build on it — API, MCP, CLI, SDK, docs depth. Methodology →
Upside’s agent strategy has two layers: the Genesis knowledge graph repairs fragmented buyer and activity data, while Dragonsight reasons across that history to explain deal outcomes. A remote, read-only MCP server lets compatible agents query Upside data through OAuth, and the company documents its developer surface through API references and llms.txt. Recent examples use the MCP for custom analytics and account-research workflows.
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