Your data already knows which customer is leaving, which machine is failing, which campaign will flop. You just hear about it too late. Phaeth turns that data into predictive twins of your customers, actions, and workflows: test changes before they go live, simulate the impact, and get warned while there's still time to act — all in plain language.
Every one of these warnings was sitting in ordinary business data — order tables, sensor feeds, training logs. Nothing was watching. You found out the way everyone does: after it cost you.
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.
Every campaign, price change, and process tweak debuts on real customers — because until now there was nowhere else to rehearse it. Phaeth gives you that somewhere: a population simulation over your entity twins, built from your own history, where any change can fail safely before it goes live.
Validate a new email campaign, survey, support process, or product feature against historical outcomes before customers experience it.
Replay the action across the whole twin population — every customer, machine, or athlete — and read a distribution of outcomes with confidence levels, not a guess.
Once live, the twin keeps watching outcomes, alerts when performance changes, recommends the next-best action, and improves the loop over time.
Anyone can ask an LLM to play your customer.
Phaeth replays the action against the population that actually behaves like them.
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 in the language of your business: entities such as customers or facilities, actions such as emails or surveys, and roles such as managers or support teams. Together they form one loop — blue is the blueprint you simulate against, jade is the action executing live, amber is the pulse that watches reality and feeds it back.
Customers, facilities, assets, tickets, or populations become queryable twins that collect data and can be classified, segmented, and predicted against — the risk-free sandbox for every what-if.
Email campaigns, surveys, product features, and process changes become testable actions you can replay and compare in simulation, then push live when the numbers hold.
A role twin, such as a hotel manager or support lead, watches every entity and action twin, alerts on drift, recommends the response, and feeds real outcomes back into the next simulation.
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, describe the change you want to test, and create a predictive digital twin that simulates impact before launch and improves after it goes live.
Want to talk it through first? Email us at [email protected].