Cleo

What is Cleo?

Cleo is an AI product operator for founders and small product teams that turns customer feedback and product signals into evidence-backed product decisions and execution artifacts. Its core purpose is to convert messy inputs into a weekly plan—what to ship, what to validate, what to defer, and what to ignore—while keeping recommendations tied to the underlying sources.

Cleo connects the product loop end to end: listen to signals, synthesize them, decide, ship (via generated launch/spec work), and measure outcomes with ongoing impact monitoring. The site positions Cleo as more than a feedback dashboard by grounding each recommendation in evidence and producing drafts like specs, tickets, and launch copy.

Key Features

• Evidence-linked weekly call with ranked confidence: Consolidates customer signals into one decision-focused view and explains the evidence behind recommendations.
• Auto-drafted specs and tickets: Generates draft specification and ticket outputs associated with an opportunity and their confidence level.
• Action queue for prioritization: Produces an ordered plan (e.g., ship/validate/defer/ignore paths) so teams know what to do next and in what order.
• Launch copy generation: Creates clear, on-brand launch copy tied to the work coming out of decisions.
• Honest impact monitoring: Tracks outcomes over time (example metrics shown include NPS, churn risk, adoption, and ticket volume).
• Audit trail of sources and claims: Maintains an audit trail linking recommendations to originating inputs such as inbox messages, Intercom conversations, Slack feedback, and sales call notes.

How to Use Cleo

• Join the waitlist to get access (waitlist is open; onboarding is described as happening for a few teams each week).
• Once onboarded, use Cleo’s loop workspace to view signals coming in (e.g., inbox) and ask Cleo about customers, opportunities, or impact.
• Review the “weekly call” style output that summarizes opportunities with evidence, confidence, and an actionable plan.
• Use the auto-drafted artifacts (specs, tickets) and the action queue to move selected items into production, then rely on impact monitoring to track outcomes.

Use Cases

• Prioritizing sprint work from customer signals: When multiple issues compete for attention, Cleo consolidates signals into ranked opportunities and recommends what to ship, validate, or defer.
• Converting qualitative feedback into execution artifacts: A team can take customer requests (e.g., approval flow concerns) and generate a draft spec and ticket ready for engineering work.
• Managing rollout timing with evidence and confidence: Teams can compare confidence levels and evidence coverage to decide whether to ship now, hold for a later sprint, or apply a known workaround.
• Coordinating ongoing release and measurement: After shipping, Cleo monitors outcomes over a recent window (e.g., NPS and adoption) so the next week’s decisions can incorporate results.
• Creating launch updates from prioritized work: Once an opportunity is selected, teams can generate launch copy aligned to what was decided and what is being shipped.

FAQ

• Is Cleo a feedback dashboard? No. The site explicitly frames Cleo as “more than” a feedback dashboard, emphasizing a full loop from signal to specs/tickets and then to outcome monitoring.

• What kind of inputs does Cleo use? The page references customer feedback and product signals, with an evidence-linked audit trail drawing on sources like inbox messages, Intercom conversations, Slack feedback, and sales call notes.

• What outputs does Cleo generate? The site mentions evidence-backed decisions, a weekly plan, draft specs and tickets, and launch copy, along with impact/outcome monitoring.

• Does Cleo provide an audit trail? Yes. The page describes an audit trail that ties claims back to source signals.

• How do teams get started? Access begins via the waitlist; the page notes onboarding a few teams each week.

Alternatives

• Product feedback and survey tools without end-to-end execution: Tools focused only on collecting and visualizing customer feedback may not generate evidence-linked specs or tickets, nor provide an outcome monitoring loop.
• General-purpose AI assistants for writing and summarization: A chatbot can draft text, but it typically won’t provide a structured product loop linking evidence to ranked weekly decisions, ticket-ready outputs, and impact tracking.
• Product analytics and experimentation platforms: These can measure outcomes and user behavior, but they may not automatically synthesize customer signals into a prioritized ship/validate/defer plan with drafted execution artifacts.
• Roadmap and prioritization systems: Prioritization tools can rank features, but without Cleo’s evidence-linked recommendations and auto-drafted specs and tickets tied to customer sources, they may require more manual translation from feedback to execution.