Atono

Atono is positioned as a product engineering platform for software teams. Its core idea is that a story holds decisions, feedback, implementation notes, usage analytics, and feature flags together, so teams can see what shipped and what got used. It offers a Glossary built from a team's own docs to ground AI tools in product vocabulary, Living Stories that keep context attached after sprint planning, and AI Context captured on stories via an MCP server for tools such as Claude, Cursor, and Copilot. The page describes two ways to use it: as an intelligence layer alongside existing tools like Jira or Linear, or as a full platform intended to replace Jira, a feature flag tool, and an analytics tool. Stated plan-to-measure stages are Plan, Build, Deploy, and Measure. The page also lists a changelog, a Story assistant, and a Glossary feature, and includes a customer quote from Ryden Sun, Head of Product.

Key features

• Glossary: shared product vocabulary built from your own docs, with terminology and relationships between concepts
• Living Stories with decisions, feedback, implementation notes, usage analytics, and feature flags attached
• AI Context: design decisions and technical changes captured on stories via an MCP server
• MCP integration to read and write context from Claude, Cursor, or any AI tool
• AI-drafted specs and stories grounded in the Glossary
• Feature flags attached to the story that defined the feature
• Engagement and usage data stored on the story
• Epics, subtasks, visual timelines, and cycle time reporting
• Ask Capy for finding past decisions, related work, and context
• Story assistant, tagging and filters, epic bulk actions and history, workspace-level releases, and Java SDK support listed in the changelog
• Bug reports with diagnostic context (per customer quote)

Best suited for

• Engineering leaders
• Engineering managers
• Engineers
• Product leaders
• Product managers

Pricing

Not stated on the website