Kyle AI
The intelligent knowledge butler that unifies your disparate documentation into a private, searchable brain.
The AI-Native Operating System for Collaborative Knowledge and Automated Team Workflows.
Exhale is a pioneering AI-native operating system designed to unify fragmented team data into a coherent, actionable knowledge graph. Architecturally, it utilizes advanced Retrieval-Augmented Generation (RAG) and multi-LLM orchestration (supporting GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro) to serve as the 'connective tissue' across siloed platforms like Slack, Jira, Notion, and Google Drive. By 2026, Exhale is positioned as the primary interface for enterprise 'ambient intelligence,' where the system proactively identifies knowledge gaps and automates documentation without human intervention. Its technical stack emphasizes real-time semantic indexing and vector-based search, allowing teams to query their entire historical workspace context with sub-second latency. Unlike traditional wikis that require manual upkeep, Exhale's self-healing knowledge base evolves as conversations and projects progress, ensuring that technical documentation and project requirements are never out of date. This makes it a critical tool for scaling engineering and product teams that suffer from information overload.
Silently monitors Slack threads and Zoom transcripts to update technical wikis in real-time using NLP-based relevance scoring.
The intelligent knowledge butler that unifies your disparate documentation into a private, searchable brain.
Turn static documentation into an AI-powered self-service intelligence hub.
The internet's memory: An AI-powered workspace that automatically indexes your files, bookmarks, and thoughts.
The knowledge base platform designed to scale human expertise with AI-powered search and deep customization.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Dynamically routes queries to the most cost-effective or high-reasoning LLM based on task complexity.
Identifies contradictory information across different platforms and prompts human experts for reconciliation.
Uses past behavior to predict what files or context a user needs before they start a search query.
Allows AI outputs to trigger external API calls (e.g., creating a Jira ticket or a GitHub PR).
Automatically redacts PII and sensitive data before passing context to public LLM endpoints.
Visualizes the relationships between people, projects, and documents in a 3D graph view.
New hires spend weeks digging through outdated documentation to understand system architecture.
Registry Updated:2/7/2026
Valuable context from incident response is lost in transient Slack channels.
Sales teams use outdated product info during live calls.