Not another agent. A twin of your business that watches your data, tells you what's about to
happen, and recommends what to do — just talk to it.
Want more accuracy? Train a model on
your own data in minutes, no data team required.
Every business question used to end in a chart. Then in a chatbot. Now it ends in a prediction. Phaeth is the tool that gets you there in the same afternoon — a twin trained on your data, not another agent reasoning over text.
The dashboard told you what happened.
Phaeth tells you what to do.
The agent category is crowded — and every one of them reasons over text you already wrote. None of them train a predictive model of your business. That's the gap Phaeth fills.
Agent platforms gave every team a co-pilot.
Phaeth gives every team a forecaster.
The twin works the moment your data is connected. The agent handles the data-science work if you ever need it — you stay in plain language the whole way, and the model stays optional.
CSV, Parquet, or a database. Phaeth profiles it, fixes typing errors, and validates structure — locally — and you instantly have a twin of your data.
Out of the box, the twin watches your data, predicts what's coming, and fires alerts with a recommended action — to a notification, a webhook, or a downstream agent. Ask it anything in plain language. For many use cases, this is all you need.
When a use case needs sharper prediction, ask the twin to train a model on your own data — it picks the features, trains it, and tells you what drives it. Optional: both paths work, and the twin keeps learning from real outcomes either way.
Phaeth works on any prediction problem you can describe. These are the use cases we've already invested in — the ones where we've shaped the tool to know which signals predict which outcomes, and which traps to avoid. Bring your own; every new use case our users try makes Phaeth smarter for the next one.
Athletic readiness, injury risk, fatigue, recovery. Built around HRV, training load, ACWR, and sleep — the signals that actually move the needle.
See sports use casesPredictive maintenance, remaining useful life, anomaly detection. Vibration, temperature, oil analysis — modelled with the right detection horizons.
See industrial use casesDemand forecasting, delivery ETA, churn and CLV. Stockout-aware, promotion-aware, seasonality-aware — the things naive models get wrong.
See e-commerce use casesDon't see your industry? Open the app and try your use case anyway — Phaeth's core works on any tabular prediction problem.
Phaeth runs locally on your machine or on your own server. Raw data is processed on your hardware — only encrypted structural metadata is ever used by the AI layer.
All raw data is processed on your machine or your private server. Your datasets never leave your environment.
Only structural metadata — column names, types, summaries — reaches the AI layer. Never rows, never values.
Every trained model is captured as a deterministic recipe. Reproduce any prediction byte-for-byte.
Local machine, your remote server, or Phaeth's managed cloud — the same recipe runs identically in all three.
Build the model, deploy as a Twin API, embed in your app. Your brand, your customers, your margin — usage-based pricing that grows with your business.
Open the app, connect a dataset, and have a twin watching your data in minutes — train a model when you want more accuracy.
Want to talk it through first? Email us at [email protected].