Forecasting
P50 line + P10-P90 band over a hold-out window
Time-series predictions with calibrated quantile bounds. Demand, capacity, energy, price - at series counts and frequencies a per-SKU model zoo can't keep up with.
Foundation Models
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.
Product
One model,three task types.
01 / 05
Forecasting
Time-series predictions with calibrated quantile bounds - at series counts and frequencies a per-SKU model zoo can't keep up with.
Ecosystem
Trusted where tabular decisionsmove money.
500M+Predictions served across forecasting, regression, and classification
99.9%Pipeline uptime across deployments
Global Retailer · EMEATier-1 BankFortune 500 EnergyTop-3 LogisticsB2B SaaSIndustrial OEMAsset ManagerPublic UtilitySpecialty InsurerHealthcare NetworkGlobal Retailer · EMEATier-1 BankFortune 500 EnergyTop-3 LogisticsB2B SaaSIndustrial OEMAsset ManagerPublic UtilitySpecialty InsurerHealthcare Network
Capabilities
Three task types,one model.
One foundation model handles the three workloads enterprise data teams ship most. No bespoke architecture per task. No per-customer training loop. The same calibrated-uncertainty discipline applied everywhere.
Regression
Observations + fitted P50 + prediction interval
Continuous targets where the number matters and the bound matters more. LTV, ramp curves, headcount, churn revenue - point estimates with honest predictive intervals.
Classification
Class probability histograms + decision threshold
Calibrated probabilities, not raw scores. Fraud, lead scoring, ticket triage, propensity - outputs your reviewer queue and your decision logic can actually trust.
Ground Truth
Production data is messy.That's the point.
Real planning happens against incomplete, late-arriving, and sometimes contradictory data. eomer is trained on that reality - billions of points from production warehouses, plants, and supply chains. The forecasts that come out are the ones that survive the audit on Monday morning.
Foundation-model coverage with an honest read on uncertainty. Static dashboards become operating systems for the planning team.
Calibration verifiedENV: PRODUCTION-01
HD
Evidence
The numbers,unedited.
Aggregate metrics from production deployments across all three task types. Calibrated, monitored, and honest about the cases where classical models still win.
011.2B+Tabular rows trained on across the three task types
02<200msMedian inference latency at scale
0398.4%Calibration coverage across forecasting, regression, and classification
0412xFaster from data to deployment vs. classical pipelines
0530+Industries with at least one production deployment
Stack
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.
/ 01
Foundation TFM
One pre-trained tabular foundation model handles forecasting, regression, and classification. The starting line is the same for every customer.
/ 02
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.
/ 03
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.
/ 04
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.
/ 05
Production discipline
Versioned jobs, scheduled retrains, calibrated outputs, drift alerts, and audit trails. The boring parts that keep things running on Tuesday at 3am.
Visual intelligence
Calibration,first-class.
eomer doesn't just produce point predictions - across forecasting, regression, and classification it gives planners an honest confidence number, surfaces drift the moment it appears, and stays callable from an agent loop.
Most stacks treat uncertainty as an afterthought. eomer ships calibrated intervals (forecasting and regression), calibrated probabilities (classification), drift charts, and audit trails as first-class outputs - and exposes them to your existing tools without a glue-code project.
/ 01
Foundation reasoning
One pretrained tabular foundation transformer carries domain priors learned across billions of rows, so cold-start performance lands close to a hand-tuned baseline - without a per-target training loop.
Pretrained tabular foundation
Impact
Not benchmarks.Bottom lines.
/ 0132% Error Reduction
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.
32%
/ 0212x Time-to-Deploy
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.
12x
/ 033.5 FTE Reclaimed
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.
3.5
Solutions
Built aroundthe work.
P50 line + P10 - P90 band over a hold-out window
Calibration coverage at nominal interval
/ 01
Forecasting use cases
Demand, capacity, energy, price - replace the per-SKU model zoo with a single foundation forecast and calibrated bounds at hourly grain.
Outcomes
ROI,receipts attached.
/ 01Operating costs
Lower overhead.
1 foundation deployment replaces a per-task model zoo. Customers save up to €69,000 per month against the previous baseline.
3.5
FTE equivalent
€69k
Monthly savings
/ 02Throughput
Increase volume.
98% calibration coverage at nominal interval, with peak speeds above 650 series scored per second on standard infra.
98%
Success rate
650+
UPH peak
/ 03Supervision
Scale autonomously.
24/7 production monitoring with sub-1% manual intervention rate after stabilization. Predictable performance at scale.
24/7
Uptime
<1%
Intervention
Field reports
Hear it fromthe operators.
“We replaced six months of forecasting feature engineering with three days of integration. The hourly demand forecasts came out better than our hand-tuned baseline.”
Head of Demand Planning
Global retailer, EMEA
“It nailed our LTV regression on the first attempt. Quantile bounds finally gave finance a number they could plan against, not a single estimate to argue over.”
VP Data
B2B SaaS
“Calibrated probabilities - not raw scores - for our fraud queue. The triage workflow finally makes sense, and the audit trail is what compliance asked for on day one.”
Director of MLOps
Fortune 500 financial services
Start
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.