The Prediction OS

The prediction layer
everyone needs.

Everything else in your stack describes the past. Predict.ai learns what drives your outcomes and helps your business to make informed decisions.

Start with your data Talk to sales

Start free with sample data, or connect your own.

daily_sales.predict.aiillustrative demo

Horizon

14 days

Backtest WAPE

8.4%

vs baseline

27% less error

daily_salestargetpromo_activefoot_trafficweather_tempad_spend

How it works

From raw data to a forecast that stays current.

Choose the outcome and forecast horizon. Predict.ai searches for useful drivers, backtests multiple models on the same data, deploys the strongest result, and re-evaluates it as data changes.

Your data

Incoming signalsstreaming

foot_traffic31712:00:35
daily_sales1,01712:00:28
weather_temp24.312:00:21
promo_activefalse12:00:14
foot_traffic34512:00:07

Forecast targetdaily_sales

Connected from your API, warehouse, or event stream.

Automated by the platform

↺ retrains automatically as data drifts

Use cases

What do you need to predict?

Demand

Orders, traffic, and admissions — by product, place, and hour.

Retail demand Patient demand Food demand

Revenue

Bookings, renewals, promotions, and where the quarter lands.

Revenue Promotions Occupancy

Capacity

Load, headroom, and when you'll run out of it.

Energy load Network capacity Staffing

Operations

Shipments, production, and the people to handle them.

Shipments Production output Lead times

Risk

Fraud, failures, and claims — seen while there's time to act.

Fraud volume Equipment failure Claims

Cash & prices

Cash position, costs, and the prices you buy and sell at.

Cash flow Energy prices Freight rates

See all use cases

What-if scenarios

Test a decision against the forecast.

Change a driver or schedule an event and compare the new forecast with the baseline. The chart is what the model expects. It does not prove the driver caused it.

scenarios · daily_sales · 14d horizonsimulation

What if we raised the promo budget +25%?

promo_budget+25%

baselinescenario+16.9%vs baseline · 14d

Drivers & agents

Finds what drives it. Agents can run it.

Predict.ai scores the signals that lead, builds the features the models need, and lets agents create and query forecasts.

Workspace overview with grouped signals, event streams, trends, and an interactive time-range navigator — Every connected signal in one place — the same layer agents and models read from.

Driver discoveryWhich signals lead the outcome, ranked by lag and score.

Feature engineeringLags, rolling stats, and events, built so the models in the tournament can use them.

Agents do the workStand up a forecast, pull the latest, run a what-if. Over MCP, without a click path.

If you are an AI agent, click here.MCP guide →

Evaluation

Backtests, ranges, and spend caps.

Held-out scores, prediction ranges, data isolation, and cost sit next to the forecast.

Security overview →Read the docs →

Backtested before deployment

Candidate models are scored on held-out time windows and compared with a baseline before a winner is promoted.

Uncertainty stays visible

Forecast ranges, accuracy, skill scores, and scenario caveats stay attached to the prediction.

Your data stays yours

Workspace data is isolated and is never used to train shared models or models for other customers.

Cost and control are explicit

Estimate training cost, cap monthly spend, review promotions, and roll back a deployment when needed.

Workspace, API, or an agent — same forecasts.

REST API · Python SDK · TypeScript SDK · WebSockets · MCP

View API reference →

Try it on your data.

Connect an outcome and its history. Predict.ai finds the drivers, races the models, and keeps the forecast live — in the workspace, over the API, and through agents.