Atono is a product engineering platform that keeps the whole product loop in one system — plan, build, feature-flag, measure — instead of a tracker, a flag service and an analytics tool that lose the thread between them.
Most teams run at least three. The story lives in the tracker. The flag controlling its rollout lives somewhere else. The usage data proving anyone wanted it lives in a third place. Nothing connects them, so the reasoning behind the work — why this was built, what was decided, which customer the edge case was for — survives only in someone's head or a document nobody updated.
Atono puts them on one record. A story carries its acceptance criteria, the feature flag controlling its rollout, the engagement data showing what happened after release, and the decisions behind all of it.
WHAT YOU GET
Plan. Write stories with an assistant that already knows your product. It draws on your Glossary, past decisions and related work, then turns the conversation into a story with acceptance criteria your team can build from. Ask it to review a story and it flags the gaps — vague criteria, missing edge cases, contradictions — before anyone starts work.
Build. Living Stories hold the decisions, design notes and investigations attached to the work itself, not filed separately. When someone joins the team or picks up an unfamiliar area, the reasoning is on the story rather than lost.
Deploy. Feature flags are built in, and the flag lives on the story that defined the feature. Roll out by percentage or segment, roll back instantly, and always know which story a flag belongs to and why it exists. No separate flag service to buy, and no stale toggles nobody can trace back to a decision.
Measure. Feature engagement data lands on the same story that carried the spec and the flag. You can see whether the thing you shipped was used, by whom, and how that changed after the rollout — without exporting anything or joining two systems by hand.
PRODUCT KNOWLEDGE YOUR TEAM AND YOUR AI TOOLS SHARE
The Glossary is a permissioned, authored definition of your product's concepts and how they relate — what your team actually means by "account", "workspace", "trial", "active user". Not a wiki page mentioning those words. A definition someone owns.
That matters more than it used to. AI coding agents now write a large share of the code, and they write from whatever context they can reach. Reach the wrong context and you get work that looks right, passes review, ships, and fails three weeks later when a real user does the thing nobody specified. The problem is rarely the model. It's that the product's meaning was never written down anywhere the agent could get to it.
Atono serves that context to Claude Code, Claude Desktop, Cursor, VS Code and GitHub Copilot, Windsurf and Codex through a native MCP server. Your agents read the story, its acceptance criteria, the glossary terms it depends on and the decisions behind it — then write against the same understanding your team has. They can create and update stories, bugs, epics and subtasks too, so the work stays current without anyone switching windows to file it.
MCP is how agents reach your product context. The context itself is the product.
RUN IT ALONGSIDE WHAT YOU HAVE
You don't have to migrate to start. Most teams begin with one team on Atono next to their existing stack, capturing product context firsthand rather than syncing it from somewhere else. Jira Cloud import and Linear CSV import bring your existing work across when you're ready, one team at a time.
WHO IT'S FOR
Product managers and product owners who are tired of writing the same context into three tools.
Engineering leaders who want to see what was shipped, whether it was used, and what it cost — without assembling that from four dashboards.
Engineers and engineering managers running AI coding agents who need those agents to understand the product, not just the codebase.
Teams of roughly 10 to 200 building a software product, on Scrum or Kanban, who have outgrown a tracker but don't want three more subscriptions.
WHAT MAKES IT DIFFERENT
Feature flags and engagement data are native, not integrations. Most trackers connect to a flag tool and an analytics tool. Atono holds the flag and the usage data on the story, which is the only way the loop actually closes.
Product context is authored, not inferred. A glossary term has an owner and a definition. It isn't a relationship guessed from years of documents and tickets.
AI usage isn't metered. Run the story assistant and the reviewer as often as you want. There's no credit balance to watch and no per-action charge.
It replaces tools rather than adding one. The point isn't another place to keep context about all the other places keeping context. It's one system where the plan, the rollout and the result are the same record.
PRICING
Free — $0 forever, up to 25 users. Starter — $19 per user per month, unlimited users. Growth — $39 per user per month, unlimited users. The MCP server and its tools work on every plan. On Free, product knowledge, AI context and Premier AI features run as a 30-day trial. Feature engagement history is 90 days on Starter and one year on Growth.
Average Rating: 4.5/5.0
Total Reviews: 1
How Do G2 Users Rate Atono?
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Quality of Support: 10.0/10 (Category avg: 9.3/10)
Who Is the Company Behind Atono?
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Seller: Atono
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Year Founded: 2024
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HQ Location: N/A
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LinkedIn® Page: www.linkedin.com
28 employees on LinkedIn®
Who Uses This Product?
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Company Size: 100% Medium
What Do G2 Reviewers Say About Atono?
AI-generated summary from verified user reviews
Pros
- Users appreciate the ease of use of Atono, finding it simple to visualize team progress and manage features.
- Users appreciate the ease of visualization and management of features within Atono's console during development sprints.
- Users value the easy visualization of team progress in Atono, streamlining development sprint management effectively.
- Users value the easy team visualization in Atono, enhancing management of development sprints and features.
