MottoNote builds a continuously updated knowledge graph from an organization’s emails, chats, documents, and code repositories, normalizing entities across tools like Gmail, Slack, Notion, and GitHub. The graph can be queried via API and connected to any large language model, giving teams contextual search and AI insights while keeping data fully owned and portable.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises generate large volumes of unstructured information across emails, documents, chats, and data repositories, making it difficult to locate relevant context, track institutional memory, and enable consistent decision‑making. Existing knowledge bases are static, siloed, or tied to specific AI models, leading to vendor lock‑in and outdated information.
Solution
MottoNote continuously compiles an organization’s documents, communications, and data sources into a living knowledge graph that remains owned by the enterprise. The platform parses and resolves entities across integrated tools such as Gmail, Slack, Notion, Salesforce, GitHub, and cloud storage, creating a structured, queryable repository that updates automatically as new content arrives. Users can connect any large language model—Claude, GPT, Gemini, or open‑source options—to reason over the graph, enabling contextual search and AI‑driven insights without vendor lock‑in. The graph can be exposed via APIs to any downstream AI application, allowing teams to leverage their existing AI stack while maintaining full control over the underlying data.
Target Audience
Primary customers are knowledge‑intensive organizations such as venture capital firms, law and consulting practices, M&A teams, research groups, and engineering departments that need unified, searchable institutional memory.
Features
- Automatic ingestion and entity resolution from email, chat, document, and code repositories (Gmail, Slack, Notion, Salesforce, GitHub, Drive, etc.)
- Continuous, self‑updating knowledge graph that reflects the latest organizational information
- Plug‑and‑play integration with any LLM via a standard MCP interface, supporting Claude, GPT, Gemini, and open‑source models
- Queryable, structured graph accessible through API for custom AI tooling and enterprise search
- No vendor lock‑in: the knowledge graph is fully portable and can be exported or hosted on‑premise
- Timestamped provenance for each data point, enabling traceability of decisions and context