Product
The application and intelligence layer for tabular foundation models
Apply one platform to forecasting, regression, and classification, then integrate calibrated outputs with the domain workflows that use each decision.
Forecast every series without a model zoo
One pre-trained tabular foundation model handles demand, capacity, energy, price, and other temporal targets across series and frequencies.
Hand eomer a long-format DataFrame and receive calibrated forecasts without a training cycle for each series.
Prediction output
P10 · P50 · P90
Estimate continuous outcomes with honest bounds
Use the same platform for lifetime value, ramp curves, claim severity, and other continuous targets that need more than a point estimate.
Estimate numeric outcomes through the same tabular interface across commercial, financial, and operating datasets.
Shared interface
One API
Route decisions with calibrated probabilities
Score fraud, churn, propensity, and operational risk with probability outputs that support review queues and explicit decision rules.
Score several outcomes in one request and retain the probability assigned to every available class.
Model output
Class probabilities
Adapt the foundation model to domain data
Fine-tune from the dashboard, monitor each job, compare model versions, and retain a zero-shot baseline for every experiment.
Submit domain data through a managed job that records configuration, compute, and evaluation outputs.
Experiment record
Data · model · metrics
Operate predictions as a shared enterprise service
Move from a local result to distributed inference, organization workspaces, API keys, webhooks, notifications, and audit evidence.
Provision parallel compute for each workload and return prediction batches through one service interface.
Deployment path
Local · parallel · cloud
From data to production
A direct path from baseline to service
01
Connect data
Upload a dataset or connect object storage and database sources.
02
Run a baseline
Use zero-shot inference with a fixed holdout and explicit metrics.
03
Review outputs
Check error, calibration, feature analysis, runtime, and cost.
04
Deploy
Serve the selected model through API, webhooks, and organization controls.
Evaluate a representative workload with eomer.
A 30-minute session covers baseline results, uncertainty, deployment options, and the limits of production use.
Request demo