01 · Prediction
Behavioral prediction models
ML models that score every user, every session. Will they convert, churn, or activate? Predictions update in real time.
Platform
Behavioral model infrastructure, from first-party data to a tested decision.
Moveo One trains a behavioral model of every customer, predicts each customer's next move at runtime, attaches actions to the cohorts it predicts, and simulates changes on synthetic cohorts calibrated to them.
- 01
Model
Behavioral models trained on your first-party data. Every customer, every session.
- 02
Predict
Per-customer probability of conversion, churn, activation or any behavior, at runtime, with the reason.
- 03
Act
Webhooks, API, Slack, email and experiments attached to predicted cohorts.
- 04
Simulate
Synthetic customers calibrated to your real cohorts, run against any change before launch.
See it in motion
A 90-second tour of Moveo in action.
Watch how Moveo turns raw behavioral data into per-user predictions, and how those predictions plug into your existing product loops.
app.moveo.one · live demo
Four pillars
Model. Predict. Act. Simulate.
One loop: first-party behavior becomes a model, the model predicts and acts at runtime, and the same model powers the simulation of what comes next.
Model
A behavioral model of every customer
Moveo One trains outcome models on your own first-party behavioral data (engagement velocity, hesitation, attention drop, journey similarity to past converters) with proprietary features for attention and cognitive load inherited from clinical behavioral research.
- Trained on your data, not generic benchmarks
- Any outcome: conversion, churn, activation, or any event you define
- Retrained on drift, with nothing for your team to run
Predict
Runtime probability, with the reason
Every session gets a live probability for each outcome you model, with the explanation behind it: UX friction, price sensitivity, missing feature or weak support, cross-referenced with cognitive-load and attention signals.
- Per-session prediction scores updated in real time
- Root cause per prediction, in plain language
- Confidence intervals so you know when to act vs. observe
Act
Actions attached to predicted cohorts
Predictions leave through the API and webhooks the moment a customer crosses a threshold, into onboarding nudges, offers, sales outreach or in-app prompts. Experiments are scoped to the cohort predicted to respond, with a control group by default.
- Webhooks fire when a customer crosses a risk threshold
- Slack, email, HubSpot, Customer.io and more
- Targeted experiments with an automatic control group
Simulate
Synthetic customers calibrated to your real cohorts
The same models, inverted. Moveo One generates synthetic customers per behavioral cohort, tunes each cohort until it reproduces the outcomes you observe, then runs it against any version of your product in a real browser or app. The impact of a change is measured before a real customer sees it.
- Cohorts derived from your own behavioral data, not prompted personas
- Calibration loop corrects drift after every run
- Onboarding, checkout, pricing and redesigns tested pre-release
- Content too: films, social posts, banners and packs shown to the same calibrated audience
Capabilities
Everything in the box
The capabilities underneath the four pillars. One product, deployed as managed cloud, single-tenant or on-premise.
02 · Prediction
Real-time friction detection
Catch hesitation, decision fatigue, and confusion the moment it happens, not in next week's retrospective.
03 · Integration
Zero-tagging instrumentation
Connect datasource or drop in the SDK. Events, flows, and screens are auto-detected. No taxonomy meetings, no broken pipelines.
04 · Analytics
Automatic journey mapping
Every path users take, visualized. See which flows lead to conversion, which dead-end, and why.
05 · Analytics
Cognitive load scoring
Proprietary signals that flag screens overloading users before they bounce or rage-tap.
06 · Workflow
AYDA: ask your data anything
Natural-language questions, immediate answers. 'Why did Tuesday's signups stall?' Answered.
07 · Analytics
Predictive cohorts
Build segments by future behavior, not past clicks. 'Users likely to churn this week' is a real cohort.
08 · Integration
Predictions API & webhooks
Stream prediction scores into your stack to trigger emails, in-app prompts, or sales outreach when it matters.
09 · Integration
One SDK, every platform
Web, iOS, Android, React Native, Flutter. Identical signals across surfaces, consistent conclusions.
10 · Prediction
Synthetic user testing
Simulate users on a flow before launch. Predict UX outcomes pre-release, not after the bad reviews.
11 · Prediction
Content simulation
Show a film, post, banner or pack to a calibrated audience before it runs. Per segment: who stopped, what they took away, and whether they would act.
12 · Workflow
Unlimited team workspaces
Bring product, design, growth, and engineering into the same view. No seat tax, no role gating.
13 · Integration
MCP server for your AI assistant
Connect Claude, Cursor, or any MCP client to your workspace. It installs the SDK in your repo, checks events are arriving, answers questions about your data, and runs simulations from your editor.
14 · Integration
Self-hosted option
Keep behavioral data inside your own cloud. Full feature parity, your perimeter, your rules.
Model Context Protocol
Your AI assistant can drive Moveo One.
Moveo One ships an MCP server. Point Claude, Claude Code, Cursor, or any MCP client at your workspace and it works inside the product on your behalf, with no dashboard round-trip.
- Installs the SDK in your codebase and tells you what to change
- Verifies events are actually arriving before you ship
- Answers questions about your analytics and dashboards in plain language
- Builds cohorts and runs Quantum simulations from your editor
Connect it
$ uvx moveo-one-mcp loginOpens your browser, you approve the connection, and your assistant is talking to your workspace. Personal API tokens are available for headless clients like n8n or CI.
See all integrationsData in, decisions out
Raw data in. Predictions out.
Moveo One sits on the warehouse and analytics you already run and turns first-party behavior into a forward-looking signal.
- 01
Connect your data
Connect the warehouse first (BigQuery, Snowflake, Redshift, Segment), with product analytics or the SDK next to it. No schema migration, no rip-and-replace.
Warehouses, then product analytics
- BigQuery
- Snowflake
- Redshift
- Segment
- PostHog
- Amplitude
- Mixpanel
Or let your AI assistant do it: our MCP server installs the SDK for you
$ uvx moveo-one-mcp login - 02
Train predictive models
Moveo One trains conversion, churn and general behavioral models on your first-party data, and retrains them when behavior drifts.
example: The Reason for churn
- UX0.36
- Support0.82
- Price0.41
- Missing feature0.58
- 03
Act on predictions
Trigger offers, nudges, emails and experiments scoped to the customers predicted to respond. Measure lift against a control group.
Act with
- Webhook
- Slack
- Intercom
- n8n
- PostHog
- Database
04Beta
Simulate before you ship.
Your models are already running. Moveo uses them to calibrate synthetic users against your real behavioral buckets. Point them at any new feature, paywall, or redesign and watch how each cohort responds, before a single real user touches the change.
- Calibrated to match your live user distribution
- Calibration zone marks the predicted success rate
- Catch regressions for power users before launch
Simulation run · staging build
liveGoal · agent landingsReached
Complete onboardingtarget → 82%
82%−18% lost
Activate key featuretarget → 61%
61%−39% lost
Reach paywalltarget → 44%
44%−56% lost
Convert to paidtarget → 19%
19%−81% lost
1,248 synthetic sessionsΔ −4.2% vs. production
39% of mid-funnel agents drop before activation · 56% of at-risk agents stall at paywall
Test the next decision on your customers before they see it.
A 30-minute briefing on how Moveo One would model your customer base, where it would deploy, and what to simulate first.