Refinder AI

Indexes and searches across multiple disconnected work applications (email, documents, chat, project management, CRM) using semantic embeddings rather than keyword matching. Maintains a unified vector index that maps queries to relevant content across all connected sources, enabling users to find information without knowing which tool it lives in or remembering exact keywords.

Provides an LLM-powered chat interface that grounds responses in indexed workspace content rather than relying solely on training data. When answering questions, the assistant retrieves relevant documents from your connected applications, cites sources, and maintains conversation history to understand follow-up questions in context. Uses retrieval-augmented generation (RAG) pattern with source attribution.

Analyzes conversational queries and workspace content to automatically identify actionable tasks, extract structured data (dates, assignees, priorities), and suggest next steps. Uses NLP to parse intent from natural language and maps it to available actions in connected tools (create task in Asana, send email, schedule meeting). Learns from user behavior to improve suggestion relevance over time.

Continuously monitors connected applications for new activity (messages, document changes, task updates) and synthesizes notifications using AI to reduce alert fatigue. Learns user priorities and notification preferences to surface only relevant updates, groups related notifications together, and provides summaries of activity bursts. Implements intelligent batching to avoid notification spam while maintaining timeliness.

Automatically generates summaries of long documents, email threads, and chat conversations using abstractive summarization techniques. Extracts key insights, decisions, action items, and stakeholders from unstructured content. Supports multiple summary lengths and formats (bullet points, narrative, structured data). Maintains context about who said what and when for accountability.

Enables users to create automated workflows that span multiple connected applications using a visual or natural language interface. Supports conditional branching (if-then logic), data transformation between tools, and sequential or parallel task execution. Implements a workflow engine that orchestrates API calls to multiple tools based on triggers and user-defined rules. Stores workflow definitions and execution history for auditing and debugging.

Manages access to indexed workspace content and AI-generated insights based on user roles and organizational hierarchy. Implements fine-grained permission controls that respect source application permissions while enabling secure sharing of summaries and insights. Prevents unauthorized access to sensitive information and maintains audit logs of who accessed what and when. Supports role-based access control (RBAC) and attribute-based access control (ABAC) patterns.

Continuously learns from user interactions (search queries, clicked results, feedback on suggestions) to improve relevance and personalization. Uses implicit feedback (which results users click on, how long they spend reading) and explicit feedback (thumbs up/down on suggestions) to refine ranking models and suggestion quality. Implements collaborative filtering to identify patterns across similar users and improve recommendations for everyone.