DorisLabs
Doris is a semantic layer for go-to-market teams that unifies scattered deal data from calls, emails, and CRMs into a single, queryable record. It uses an ontology to standardize concepts like deals, objections, and value drivers, enabling AI agents and sales teams to reason over the full deal cycle. The platform surfaces what worked on similar past deals before the next call, turning institutional knowledge into a competitive advantage.
- Artificial Intelligence
- AI Agents
- Sales Technology
- Software Only
Funding
Founders
Product
Problem
Sales teams lose critical deal context scattered across call recordings, email threads, documents, and the CRM, which only captures fields like stage and amount. When reps leave, the knowledge of why deals closed or stalled leaves with them, forcing the company to relearn the same lessons on every new deal. This fragmentation makes it nearly impossible to understand what actually drives revenue outcomes.
Solution
Doris is the semantic layer underneath the go-to-market stack that reads every call, email, and document, then links each piece of information to the deal it belongs to. It builds one typed, queryable record per deal using an ontology that standardizes concepts like deals, people, objections, and value drivers, and understands how they relate across the entire deal cycle. The platform serves this ontology as a remote MCP server, so AI agents like Claude, Cursor, and ChatGPT can read the pipeline in a single call and answer questions like "what worked on deals similar to Titan Aerospace?" Doris also includes a notetaker that joins meetings on Zoom, Meet, and Teams, turning conversations into typed facts on the deal record, and a sales agent that lets reps ask the pipeline anything and hear what won similar deals last time.
Target Audience
Primary customers are sales operations leaders and revenue teams at B2B companies who need to reduce institutional knowledge loss and improve deal win rates. The platform is also built for sales reps who want AI-assisted context before calls and for forward-thinking sales leaders looking to deploy AI agents across their go-to-market stack.
Features
- Ontology-based semantic layer that translates scattered data into standardized concepts (deals, people, value drivers, objections) with relationship mapping across the deal cycle
- Remote MCP server integration that indexes 80,000+ objects, letting Claude, Cursor, ChatGPT, and Claude Code query the pipeline in one call
- Notetaker that joins Zoom, Google Meet, and Microsoft Teams calls, records what was said, and converts it into typed facts on the deal record
- Sales agent that answers pipeline questions and surfaces what convinced similar buyers in past deals
- Flows feature that lets teams describe work and Doris builds the automated workflow to execute it
- CRM write-back that draws from the same typed record, ensuring the system of record stays current without manual entry