AI is almost right. That's the harder problem.
Your AI tools are fast and confident, but they're guessing at how your product actually works. Atono gives them the product knowledge they're missing so the output is right, not almost right.
Works with every AI tool your team uses
Better prompts won't fix this.
Your AI gives a confident answer. You correct it. It updates and makes another assumption. You correct that too. It's not a prompting problem - it's working from incomplete context. So you end up babysitting every session.
We tested our own AI specs. 60% needed changes.
Doug leads product at Atono. Like most PMs, he drafts with AI. The output looked right - the terminology didn't. So we built the Glossary - a shared vocabulary that grounds your team and every AI tool in your actual product. Then ran it on his own work. 60% needed changes before a single engineer could pick them up.
A collaborator that already knows your product.
When your AI knows your product, authoring a story becomes a conversation - not a correction loop. It asks the right questions, thinks through the tradeoffs with you, and captures every decision as it goes. That's what makes the Slack thread quiet.
How Atono builds product knowledge
Your product knowledge lives in three layers. Every AI tool and every person on your team draws from the same source.
Glossary
Your product's vocabulary, built from your own docs. Consistent terminology and clear relationships between concepts so your AI stops assuming and starts understanding.
Living Stories
Every story keeps its decisions, feedback, implementation notes, usage analytics and feature flags attached so context doesn't disappear after sprint planning. The story stays connected to reality as your product grows.
AI Context
Design decisions, technical investigations, and implementation changes captured directly on stories via Atono's MCP server. Rationale carries forward automatically across sessions and agents.
One workspace from plan to production.
Plan, build, deploy, and measure on the same story with the full context behind it.
A connected workflow.
Built on shared product knowledge so your team and AI stay aligned.
Product teams, in sync
Across planning, delivery, and learning.
Product knowledge
Give every AI tool on the team the same understanding of your product.
AI context
Design decisions and technical changes persist on the story, not in Slack.
Cycle time report
Reveal delivery trends to identify friction.
Product knowledge
AI writes stories using your actual terms, personas, and concepts.
Design decisions
Scoping rationale captured in the story – visible to engineers and agents.
Epics
Keep AI consistent across a feature, not just a story.
AI context
Requirements, decisions, and changes stay on the story so work doesn't stall.
Visual timelines
Track progress and spot risks before they turn into delivery issues.
Subtasks
Break stories into focused implementation steps for engineers and agents.
AI context
Your coding agent reads decisions before writing code.
MCP integration
Read and write context from Claude, Cursor, or any AI tool.
Feature flags
Control releases and rollbacks directly from the story.
Product glossary
First drafts you can refine, not rewrite.
Design decisions
Capture why you scoped it this way – engineers and agents read it before building.
Ask Capy
Find past decisions, related work, and context instantly.
Two ways to use Atono.
Replace your stack or upgrade it.
Intelligence Layer
Keep your tools and add what Atono knows.
Works alongside Jira, Linear or your existing stack so your team keeps its workflow. Start with the Glossary and expand into Atono as it earns its keep.
Full Platform
One workspace from plan to production.
Plan, build, ship and measure in one place so your team always builds with full product context. Replaces Jira, your feature flag tool, and your analytics tool for less than you pay for any of them.
“It’s refreshing to see a product built with true cross-functional collaboration in mind. The ability to toggle features directly from stories and generate bug reports with full diagnostic context is brilliant – huge time-saver for devs and QA alike.”
Changelog
Thu, May 14, 2026
Agents create epics + bugs
Glossary refresh + history
Risk in backlogs
Wed, Apr 1, 2026
Glossary launch
MCP server delivery
Wed, Feb 18, 2026
Capacity projection
Burndown forecasting
Cross-team risk flags
Stop shipping almost-right.
When the context is right, everything that follows is too.