Doris is a semantic layer for go-to-market teams that automatically captures, links, and structures every signal across calls, emails, and documents into a single typed record per deal. It surfaces what worked on similar past deals before the next call, and serves this ontology to AI agents via Model Context Protocol. The platform includes a notetaker, sales agent, and workflow builder, all built on the same underlying knowledge graph.
Funding
Funding not disclosed
Founders
Product
Problem
Critical deal intelligence is scattered across call recordings, email threads, documents, and the memories of sales representatives. When a rep leaves, the knowledge of why deals closed or what buyers wanted leaves with them, forcing the company to relearn the same lessons on every new deal. This fragmentation prevents teams from consistently applying winning strategies and understanding the true state of their pipeline.
Solution
Doris is a semantic layer that sits underneath the go-to-market stack, reading every call, email, and document, then linking each piece of information to the deal it belongs to. It maintains one typed, queryable record of what is true about each deal, including buyer wants, objections, champions, and competitive dynamics. The platform connects each deal to similar past opportunities, surfacing what convinced comparable buyers before the next call. Doris also serves its entire ontology as a remote Model Context Protocol (MCP) server, allowing AI agents like Claude, Cursor, and ChatGPT to query the pipeline in a single call. The product includes a notetaker that joins calls on Zoom, Meet, and Teams, a sales agent that answers pipeline questions, and a flows builder that automates described work.
Target Audience
Primary customers are sales operations leaders, revenue operations teams, and sales representatives at B2B companies that rely on consultative, multi-stakeholder deal cycles and need to preserve and apply institutional deal knowledge.
Features
- Automated notetaker that joins every call on Zoom, Microsoft Teams, and Google Meet, capturing and transcribing conversations
- Ontology engine that resolves fragments from multiple tools into one typed, queryable record per deal, including people, meetings, commitments, and objections
- Deal similarity matching that links current opportunities to closed-won deals with shared buyer wants, surfacing winning plays before calls
- Remote MCP server integration that exposes the full ontology to AI agents like Claude, Cursor, and ChatGPT for natural-language pipeline queries
- Sales agent that answers questions about the pipeline and retrieves what won similar deals in the past
- Flows builder that lets teams describe desired work and automatically constructs the workflow to execute it