Most people don't quit. They drift. ONNEXT detects the earliest signs that you are moving away from your goals and tells you the next action to take, before a small deviation becomes a costly outcome.
Most people don't fail because they lack information. They fail because they notice the problem too late. ONNEXT detects the pattern before it becomes the outcome, and it works the same way everywhere, because drift looks the same everywhere.
Everything is a signal before it becomes an outcome. That is the territory ONNEXT owns.
Most people wait until the consequence arrives. Motivation disappears. Performance drops. Revenue falls. Relationships strain. Health suffers. By then the fix is expensive: a restart, a rebuild, a recovery.
ONNEXT identifies the signal earlier and recommends the smallest action that keeps you moving forward. The earlier you act, the easier the recovery. That is the entire economics of this product.
On rhythm. Sleeping well. Showing up.
One miss. "I'll catch up tomorrow."
Signal pulls off baseline. One small correction, today.
"I'll restart Monday." Three months disappear.
Not another dashboard of charts you scroll past. An instrument panel: your signal, your trajectory, and the single recommended action, computed against your own baseline rather than a population average.
Your sleep is dipping. Today is about protecting energy, not pushing harder.
Mood, energy, stress, sleep. One honest line. The smallest action that keeps you in today.
Load is up while recovery is down. The gym is the symptom, not the cause.
ONNEXT runs a continuously updating detection architecture over longitudinal behavioural and biometric telemetry: sleep, training, recovery, language, check-ins, mood, adherence. Every reading is computed against the person's own rolling baseline. Imagine one hundred thousand people, three years, daily behaviour: longitudinal data at that depth surfaces patterns no human, and no single-snapshot system, can see.
A continuously evolving representation of the person's normal operating range, with confidence and stability scores. Never population-derived.
Small departures from baseline that look insignificant alone and predictive together, weighted by outcome relationships.
Quantifies disorder in the personal routine. Entropy rises before conventional metrics deteriorate.
Direction of travel, velocity, divergence and convergence. Status can look fine while the heading is wrong.
Phase classification in motion: stable, emerging drift, accelerating drift, recovering, returning.
A calibratable probability with a time horizon and named primary and secondary contributing factors.
Thousands of heterogeneous signals in, one ranked action hierarchy out, with the compression ratio reported on every response.
Optimises for return, not perfection. Detects the comeback and anchors it with the smallest effective intervention.
DETERMINISTIC, AUDITABLE RULES TIER LIVE TODAY · LEARNED TIERS ACTIVATE AS OUTCOME LABELS ACCUMULATE · NO BLACK BOX
ONNEXT ships as a detection-and-action service that host apps and AI agents compose with. A proprietary MCP pipeline structures, secures and translates raw behavioural and biometric telemetry into compact context tokens, so partner systems consume one canonical state object instead of raw data streams. One engine, thin adapters: the same ingestion, the same baseline-deviation features, the same output object and the same intervention loop serve every area. Only the connectors and thresholds change. Live now on the production API.
| Tool | What it does |
|---|---|
| get_risk | The canonical risk object for a person and area: score, window, reason, recommended move, confidence. |
| list_at_risk | Rank a cohort. A gym gets the 143 members most likely to cancel in 30 days, each with the reason and the move that keeps them. |
| explain_risk | Why: contributing signals, derived features, cross-area attribution. |
| recommend_action | The best next intervention for the cause, with expected lift. |
| simulate_intervention | Projected risk delta of an action before you spend it. |
| log_outcome | Feed back what happened. The learning loop, and the compounding data moat. |
| set_thresholds | Tune flag sensitivity to your precision and recall target. |
| connect_source | Authorised, read-only ingestion from wearables and host platforms. |
Retention is a timing problem. ONNEXT solves the timing: predict disengagement before it happens and intervene before it costs you. Every label maps to a measured signal. No black box, no poetry without data.
GET https://www.onnext.ai/v1/tools · partner keys on request
Connect the tools you already use. ONNEXT continuously monitors the signals behind your goals and identifies when behaviour starts moving away from your baseline. Then it tells you exactly what to do next. Not more data. Better intervention.
Wearables, training, check-ins, or your own platform through the API. Read-only. ONNEXT never posts anything back.
Who you are when you are consistent: your rhythm, your recovery, your language, your usual way of falling off. Every reading is measured against you, not an average.
The moment your signal pulls off baseline you get the one smallest correction, with the reason attached. Not a guilt trip. Not another plan.
Session, login and check-in frequency slipping against your own rolling baseline.
Doesn't cause drift. Predicts vulnerability to it, days before you feel it.
Disengaging psychologically usually happens before quitting practically.
The words shift first. ONNEXT reads the tone, not the content.
Fitness is the first use case, not the ceiling. The same engine extends to every domain where humans drift gradually rather than fail instantly.
Catch attendance and program drift, and under-recovery, before the cancel. Stay ahead of the drop-off.
Adherence, relapse risk and program drop-off. Clinical-grade discipline, advisory outputs, validated before claimed.
Goal abandonment, over-function and disengagement, with recovery as the upstream cause. Reinforce momentum.
Course and enrolment drop-off, with sleep and routine as the early signals. Spot the slip while support still works.
Relational drift, grounded in established relationship science. Supportive, consented, human first. Notice the wobble.
Churn, dropout and relapse as one predictable vector across the whole book. Continuation as a platform.
Safety line. ONNEXT does not detect or predict domestic violence or abuse. That is a safety-critical domain for trained professionals and specialist services. Relational features ship as supportive, consented and human-escalated, never as an autonomous detector.
See the signal. Change the outcome.
Early access is opening for the first cohort. No streaks, no shame, no countdowns. Just earlier awareness and the smallest action that changes the outcome.