Lore offers an AI‑driven knowledge management platform that continuously captures tacit expertise from tools like Slack, email, documents, meetings, and code repositories.
Funding
Funding not disclosed
Founders
Product
Problem
Organizations lose critical institutional knowledge when employees leave, decisions remain undocumented, and expertise is scattered across conversations, emails, code, and meetings, making onboarding and continuity costly and inefficient.
Solution
Lore provides an AI-driven knowledge management platform that continuously captures and indexes tacit expertise from tools such as Slack, email, documents, meetings, and code repositories. Its AI interviewer, Mira, passively observes team communications and conducts structured interviews to extract context, reasoning, and decision rationale that are not captured in traditional documentation. All captured information is stored in a searchable, attributed knowledge base that can be queried directly in Slack or accessed programmatically via Lore’s MCP server, enabling both human users and AI agents to retrieve accurate answers with full provenance. The system updates continuously, ensuring the organizational memory grows and stays current without disrupting existing workflows.
Target Audience
Primary customers are mid‑size to large enterprises that rely on collaborative tools for product development, support, and operations, and need to preserve expertise for onboarding, incident response, and AI‑augmented workflows.
Features
- Passive integration with over 20 enterprise tools (Slack, email, docs, meetings, code repositories) to capture real-time conversational and decision data
- AI interviewer (Mira) that conducts structured interviews to surface tacit knowledge and contextual reasoning
- Centralized, searchable knowledge base with attribution to original contributors and timestamps
- Slack query interface delivering instant answers with source citations and full context
- MCP server API allowing any AI agent or internal tool to query the organizational memory programmatically
- Continuous learning cycles that schedule regular knowledge capture sessions to keep the repository up to date
- Secure, privacy‑first data handling that operates in the background without requiring manual documentation