Cleo is your AI product engineer. It continuously turns customer signal, product usage, and agent traces into shipped improvement bets, ships them through Cursor, Claude Code, Devin, or Cline, then proves what actually moved. End to end, on repeat.
Planning cycles assumed humans wrote the code and customers told you what to build. Both assumptions broke. Coding agents write the code now, and your production already shows you what to build next. The bottleneck moved. It is no longer throughput or even prioritization. It is feeding agents the right context, sourced from production, at the moment a decision has to be made. The team that wins stops deciding what to build and starts running agents against production truth.
A noisy alert on a path no one hits ranks below a quiet opening in the revenue funnel. Cleo scores every signal, usage cohorts, agent traces, and customer asks, by users affected times dollars in play, so the bet at the top is the one that moves the number.
No bet exists without the production evidence attached: the usage cohort, the agent trace, the customer ask that corroborates it. The argument happens against the data, not against whoever has the most conviction in the room. Decisions become reviewable, not political.
A one-line ticket loses the context by the time an agent reads it. Cleo packages the usage cohorts, the spec, the test cases, and the win-condition gate into one bundle and hands it to Cursor, Claude Code, Devin, or Cline. Your team approves; the agent ships; the loop closes against the same metric that opened it.
Cleo isn’t a dashboard. It reads what production is telling you, picks the improvement worth shipping, and hands a grounded context bundle to whichever coding agent your team runs.
Usage from Mixpanel and Amplitude, traces from LangSmith, customer asks from Intercom, revenue from Stripe. Cleo groups by behavior, not by tool.
Signals rank into bets, weighted by impact and confidence. Each bet carries a hypothesis and a sourced win condition tied to the metric.
Cleo packages the usage cohorts, the spec, the tests, the win-condition gate. Then hands off to Cursor, Claude Code, Devin, Cline.
Cleo watches the same metric that justified the bet. If it moved, the loop closes and the learning compounds. If not, it says so and re-opens.
Each moment is the same operator at a different point in the loop. Listen to every signal, place the bet, hand off the context, prove the impact. Not four products. One.
Mixpanel shows planner users retain 2.3× better and expand 1.8× faster, yet only 19% of new workspaces ever reach it. LangSmith rates planner runs the highest flow at 0.91 satisfaction. Win condition: planner reach 19% → 40%+.
Cleo reads from your observability and product tools, then writes to your coding agents and trackers. No rip and replace.
Or bring your own. Cleo speaks REST and MCP, so any source or sink your team runs can join the loop.
Same team. Same prod. Same coding agent. Two completely different cycles.
Six tabs open: Mixpanel, Amplitude, LangSmith, Intercom, Linear, Cursor. The signal that the planner drives retention is sitting right there. Nobody has stitched it into a bet yet.
By 09:14 the bet is packaged. Usage cohorts, traces, spec, win condition. Cursor has the full context bundle before the engineer touches the keyboard.
Cleo is in private beta with a handful of AI-native B2B teams. The engineer runs continuously. The numbers below are how it runs in those workspaces today.
Cleo is built for AI-native B2B teams whose production system is the most sensitive surface they own. Every control below treats it that way.
Cleo learns from your production signal to run your loop, and that is where it stays. Your traces, telemetry, and code context never train a shared or foundation model. Not ours, not a vendor's.
We are mid-audit on SOC 2 Type II and will share the report under NDA when it lands. SSO and SCIM are on the near-term roadmap. We will tell you exactly where each control stands, no certs we don't hold.
Click any bet, see the exact production signal that triggered it. Usage cohort, trace, metric, the ship that moved it. The audit trail runs end to end, so every decision is reconstructable months later.
Run Cleo in your own cloud or bring your own model keys. You keep data residency control and decide which providers ever see a token. Cleo runs inside your perimeter, not around it.
Not an observability tool. Not a chatbot. Not an AI PM. The new role on AI-native teams: watch production, ship the fix through coding agents, and prove the impact.
The full loop, step by step. With templates for the production signal feed, the bet brief, and the Cursor context bundle.
Capture every signal continuously, decide on a weekly rhythm. The hybrid cadence for teams shipping with coding agents.