Description
Atono is a product management platform built to give both people and AI tools the product knowledge they're missing, so AI output is right rather than almost right. It addresses the problem that AI tools are fast and confident but guess at how a product actually works, forcing teams into endless correction loops.
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Atono organizes product knowledge in three connected layers. The Glossary builds a shared vocabulary from a team's own docs so terminology stays consistent across people and AI. Living Stories keep each story's decisions, feedback, implementation notes, usage analytics, and feature flags attached, so context doesn't disappear after sprint planning. AI Context captures design decisions, technical investigations, and implementation changes directly on stories via Atono's MCP server, and coding agents can read that rationale over MCP before writing code.
Atono works as one workspace from plan to production, with stories, epics, subtasks, Kanban and Scrum boards, bug tracking, built-in feature flags, feature engagement analytics, cycle-time reports, and an AI story assistant. It can be used as an intelligence layer alongside Jira or Linear, starting with the Glossary, or as a full platform that replaces separate project management, feature flag, and analytics tools. It integrates with Claude, Cursor, and Copilot, and offers a free plan for up to 25 users plus paid Starter and Growth tiers.
- CategoryAI Knowledge Management
- Pricing ModelFreemium
- DeploymentCloud
- PlatformsWeb
Atono's Core Features
Glossary for shared product vocabulary from your docs
Living Stories that keep decisions and analytics attached
AI Context captured on stories via an MCP server
Built-in feature flags with release and rollback control
Feature engagement analytics connected to what shipped
AI story assistant that drafts using your actual terms
Stories, epics, subtasks, Kanban, Scrum, and bug tracking
Works alongside Jira or Linear, or as a full platform
How to use Atono?
Build your Glossary: Create a shared product vocabulary from your own docs so AI and people use the same terms.
Author living stories: Write stories that keep decisions, feedback, analytics, and feature flags attached.
Capture AI context: Record design and technical decisions on stories via Atono's MCP server.
Connect your AI tools: Let Claude, Cursor, or Copilot read and write context over MCP before building.
Ship and measure: Control releases with built-in feature flags and track engagement on the same story.
Atono's Use Cases
- Grounding AI in your product
- Story authoring
- Feature flag management
- Connecting planning to delivery
- Replacing a fragmented stack
