Predictions that earn their placein production.
Tabular Foundation Models for forecasting, regression, and classification - one stack, one model, calibrated outputs. Connect your data, ship predictions with honest uncertainty bounds, and run on infrastructure that scales the way production demands.
A shorter pathto production.
eomer integrates the modeling, adaptation, validation, and deployment capabilities between enterprise data and production intelligence.
From DataFrame to forecast,in a few lines of Python.
The eomer SDK wraps the platform behind a familiar, scikit-style interface. Point it at a pandas DataFrame and get back calibrated, multi-series forecasts — no infrastructure to stand up, no model to train.
Data in, forecast out
Hand it a long-format DataFrame and get back calibrated, multi-series forecasts. No feature engineering, no per-SKU model zoo, no ML expertise required.
Known-future drivers
Fold in promotions, holidays, or prices through future_df — or forecast from history alone. The interface stays the same either way.
Pick your tier
eomer_pulse for low-latency runs, eomer_horizon for maximum accuracy. One line switches between them.
$ pip install eomer1from eomer import EomerForecastClient2 3# One client, two model tiers: eomer_pulse · eomer_horizon4client = EomerForecastClient(model="eomer_horizon")5 6forecast = client.forecast(7 df=sales,8 prediction_length=28,9 id_column="store_id",10 timestamp_column="date",11 target="sales",12 quantile_levels=[0.1, 0.5, 0.9],13)Returns a tidy DataFrame · store_id · date · predictions · 0.1 · 0.5 · 0.9
The Operating System for Data Science.From data to decisions.
- 01
- 02
A complete intelligence stack
Every layer required to move from enterprise data to probabilistic forecasts and operational decisions.
The Operating System for Data Science.From data to decisions.
eomer combines foundation models, contextual intelligence, enterprise infrastructure, and decision agents in one deployable platform.
A complete intelligence stack
Every layer required to move from enterprise data to probabilistic forecasts and operational decisions.
- 01Infrastructure
- 02Foundation models
- 03Covariate intelligence
- 04Decision agents
- 05Enterprise workflows
Modular by design
Each capability can be used independently, while the full stack works as one integrated system.
- InfrastructureCloud, private VPC, on-premise, and confidential deployment.
- Foundation modelsProbabilistic forecasting across datasets, markets, and time horizons.
- Covariate intelligenceWeather, prices, demand, calendars, promotions, sensor data, and market fundamentals.
- Decision agentsValidation, scenario analysis, orchestration, and automated recommendations.
- Enterprise workflowsAPIs, dashboards, ERP, EMS, trading systems, and internal tools.
Co-built withyour domain team.
eomer is more than a model. We pair our foundation TFM with your team's industry expertise to fine-tune a bespoke model on your data, in close collaboration with your subject-matter experts. The platform below runs the same way for every customer; the model that runs it is yours.
Foundation TFM
One pre-trained tabular foundation model handles forecasting, regression, and classification. The starting line is the same for every customer.
Bespoke per vertical
We fine-tune the foundation on your data and your workflow. The model that ships is shaped to your industry - retail, energy, finance, supply chain - not a one-size-fits-all average.
Co-built with your experts
Every engagement pairs our team with your subject-matter experts. Their judgment about what 'right' looks like in your domain is encoded into the model and the calibration.
Connect, don't migrate
Native adapters for warehouses, lakehouses, operational databases, and CSV. No data movement, no second copy of truth, no vendor lock-in.
Production discipline
Versioned jobs, scheduled retrains, calibrated outputs, drift alerts, and audit trails. The boring parts that keep things running on Tuesday at 3am.
Not benchmarks.Bottom lines.
Cut prediction error
Median error reduction across migrations from classical baselines - same number, whether the target is a forecast, a regression, or a calibrated classification probability.
Compress time-to-deploy
From CSV in to predictions out, in days instead of quarters. One foundation model across the three task types eliminates the per-target tuning loop that eats data-science calendars.
Reclaim FTE capacity
Average modeling FTEs released back to higher-leverage work after switching from a per-target zoo. Boring jobs get automated; humans review exceptions.
Built aroundthe work.
P50 line + P10 - P90 band over a hold-out window
Calibration coverage at nominal interval
Forecasting use cases
Demand, capacity, energy, price - replace the per-SKU model zoo with a single foundation forecast and calibrated bounds at hourly grain.
Bring your hardest target.We'll show you the floor.
A 30-minute working session against your real data - forecast, regression, or classification. You leave with a calibrated baseline, an honest read on where the floor is, and a conversation about what would change if you cleared it.