Frequently Asked Questions

No. Any application claiming 100% accuracy is not being truthful. The Aviator game operates on a Provably Fair system where each round is determined cryptographically and independently. No external tool can guarantee the outcome of any individual round.

Aviator AI Predictor is a probabilistic system. It aims to predict better than random selection over a sufficient sample. That is a specific, measurable claim. It is also the only kind of claim that can be made honestly about a system like this.

It means the model produces a probability estimate rather than a certain answer. When the prediction says a round will close below 1.20x, the model is saying the probability of that outcome is high enough to make it the prediction. It does not mean the outcome is guaranteed.

Over a large enough sample, the model aims to be correct more often than random chance would produce. Whether it achieves that in any given round is unknown in advance. That uncertainty is the definition of a probabilistic system, not a flaw in it.

Predicting a specific multiplier value would require the model to be correct about an exact number rather than which side of a line the outcome falls on. That is a significantly harder problem with lower reliability.

1.20x is also a statistically meaningful threshold. At this point in Aviator's multiplier distribution, there are enough examples of both outcomes in the dataset for the model to learn from both sides. A threshold too low leaves insufficient data on one side; a threshold too high makes the rare-event problem worse.

After each round, the predicted class is compared to the actual outcome. Correct or incorrect. That record accumulates. Accuracy is calculated as the percentage of correct predictions over a rolling window of recent rounds.

A rolling window is used rather than a cumulative lifetime figure because the current model's performance matters more than historical averages. A model that performed well two years ago but has since drifted would still look fine under cumulative tracking. Rolling window measurement catches that.

Round results are collected from Aviator's publicly available output. Each data point is the multiplier value and timestamp from a completed round. The collection process reads outputs only. It does not interact with the game's backend, RNG, or any internal system.

Hundreds of thousands of real Aviator round results. This volume is necessary. Aviator's multiplier distribution has a long tail: very high multipliers are rare and only carry statistical weight after accumulating across thousands of examples. A model trained on a smaller sample would be learning from a dataset that does not adequately represent the full distribution.

Two update cycles run on separate schedules. After each round, the new result enters the dataset and the prediction engine recalculates. This happens before the next round begins.

Full model retraining, where the model re-learns from the entire accumulated dataset, runs on a longer scheduled cycle. Retraining is more computationally intensive than a dataset update, which is why the two processes are separated. Both run automatically without manual intervention.

The model picks it up as new data accumulates. A static model trained once and never updated would eventually become obsolete if the underlying distribution shifted. Aviator AI Predictor retrains against the full dataset on a regular schedule, which means changes in the data are reflected in the model's behavior over time.

That is expected behavior, not a bug. Each new round adds a data point and the prediction engine updates accordingly. The prediction is a snapshot of what the model currently estimates. Checking the app at different points during a session may show different predictions because the underlying data has changed.

Yes. Registration requires a valid email address. No payment information, phone number, or other personal data is collected at registration.

Yes. The app is compatible with both desktop and mobile platforms without a separate version for either. The interface adapts to screen size. The prediction engine runs server-side, so device performance has no effect on the output or the speed at which it is delivered.

Yes. The prediction engine runs server-side and requires an active connection to deliver results. The app does not function offline.

Nothing in particular. The prediction is one data point from a probabilistic system. It will be wrong on individual rounds. The model's value, if any, comes from performance over a large sample rather than from any single prediction. Disagreeing with a prediction and acting against it is entirely the user's decision.

No. Spribe developed the Aviator game. Aviator AI Predictor has no connection to Spribe. There is no licensing agreement, business relationship, or data-sharing arrangement of any kind. The tool processes publicly available round output independently.

No. The app reads publicly available round output. It does not interact with the game's backend, modify any game parameter, or affect the outcome of any round in any way. The Provably Fair system that determines each round's outcome operates entirely independently of this tool.

Aviator AI Predictor is a statistical analysis tool that processes publicly available data. It does not interfere with any game mechanism. Whether use of such a tool is lawful depends on the laws of the user's country or region. Users are responsible for confirming that their use complies with local law before accessing the service.

Most apps calling themselves predictor tools produce output from a random number generator. The interface looks like a prediction tool; the output is random. A random output cannot improve with data because it has no data. It cannot update because there is nothing to update.

Aviator AI Predictor processes real round data through a trained classification model. That is a measurable difference. Either the model performs better than chance over a sufficient sample, or it does not. A random number generator cannot make that claim at all.

Because 90% accuracy in predicting a Provably Fair RNG system is not achievable. Any app making that claim is either measuring something other than actual prediction accuracy, fabricating the figure, or misrepresenting what the number means.

Aviator AI Predictor tracks real performance against real outcomes. Publishing a figure that cannot be independently verified would not be an honest representation of the system.

The model is trained on historical data and looks for patterns in multiplier sequences. Whether meaningful patterns exist in a Provably Fair system is an open question, and the honest answer is that no one knows for certain. What can be said is that the model's output is based on data analysis rather than random generation, and its performance can be measured against actual outcomes over time. That measurability is the core distinction.