How much is your forecast not telling you?
Most teams discover forecast failure after the damage is done. A 30-minute demo shows you what your current stack is missing.
Reduced forecast error by 34% for a retail client — stockout patterns the previous model missed entirely
Trusted where forecasting failures have real consequences • TB-scale data • SOC 2-aligned • Deploy anywhere
Every industry has a forecasting blind spot.
These are the failure modes we see across enterprises. Most teams don't know they exist until they cost real money.
Logistics
Expose
- Demand blind spots across lead times
- Hidden carrying costs from over-ordering
- Stockout cascades your current model misses
Maritime & Shipping
Uncover
- Port congestion your forecast didn't see
- Vessel arrival drift compounding daily
- Freight rate exposure from stale models
Aviation & Airports
Catch
- Passenger volume swings that break scheduling
- Crew gaps that cascade into cancellations
- Turnaround delays that were forecastable
Manufacturing
Detect
- Output variance hiding in noisy data
- Equipment failure windows narrowing unnoticed
- Line disruptions that pattern-match to prior incidents
Retail
Surface
- SKU-level demand drift across stores
- Seasonal shifts your last model didn't learn
- Replenishment timing errors costing margin
Energy
Reveal
- Consumption spikes outside your confidence band
- Grid imbalances from uncalibrated forecasts
- Price volatility your model underestimates
Finance
Quantify
- Cross-asset correlation your model ignores
- Horizon-dependent bias in your projections
- Confidence intervals that aren't actually calibrated
Trusted by forward-thinking teams
Based on industry patterns and published research.
Your forecasting stack has a shelf life. Most teams don't check the expiration date.
Here's what we consistently see when we audit enterprise forecasting infrastructure. These aren't edge cases — they're the norm.
The status quo
- Your data pipeline was accurate 18 months ago. Nobody has validated it since.
- Point forecasts give your team false confidence. They can't see how wrong they might be.
- Your best data scientist is a single point of failure. When they leave, the model breaks.
- Drift detection isn't running. Your model's accuracy is degrading and nobody knows.
With eomer
- Continuous pipeline validation catches silent data drift before it reaches your forecast.
- Full probabilistic output — prediction intervals, quantiles — so you see the actual risk envelope.
- A foundation model that doesn't depend on one person's feature engineering choices.
- Automated backtesting and distribution monitoring flag degradation in real time.
From raw data to production forecasts.
Three steps. No ML engineering. No bespoke model design.
Connect Your Data
Point eomer at your time series - S3, databases, CSV, XLSX, or Parquet. Schema detection, alignment, and validation happen automatically.
Prepare & Enrich
Automated cleaning, imputation, and feature engineering. Review data quality metrics before you commit to a run.
Forecast & Ship
eomer runs inference across all your series in parallel. Monitor progress, inspect results, and export predictions - ready for production.
Arrives with prior knowledge.
Pre-trained on massive tabular datasets, eomer understands temporal patterns out of the box. Here's what that unlocks.
Zero-Shot Forecasting
Most models need months of domain-specific training before they're useful. During that time, you're flying blind. eomer arrives pre-trained — no cold start, no gap in coverage.
Calibrated Uncertainty
Point forecasts hide risk. When your model says '10,000 units' but the actual range is 6,000–14,000, every downstream decision is built on false precision. eomer gives you the full distribution.
Continuous Validation
The most dangerous forecast is one that used to be right. Without continuous backtesting and drift detection, you won't know your model has degraded until the P&L tells you.
Hierarchical Reconciliation
When store-level forecasts don't add up to the region, someone manually forces them to agree. That manual adjustment introduces bias nobody tracks. eomer reconciles automatically.
Elastic Compute
When forecasting demand spikes — peak season, market shocks — you need results now, not after a 3-day queue. Elastic compute means you never hit a wall at the worst possible time.
Universal Connectors
Data silos don't just slow you down — they create blind spots. When your model can't see all the data, it can't warn you about what it's missing.
Your data stays yours - we never train models on customer data
Encrypted end-to-end with AES-256 at rest and TLS 1.2+ in transit
Deploy on our managed cloud or in your VPC
SOC 2-aligned controls and GDPR-ready from day one
See it in action
See the signal. Not the noise.
Built for teams that take security seriously.
Enterprise-grade security controls, compliance alignment, and deployment flexibility - because your data is your competitive advantage.
SOC 2-Ready Posture
Controls aligned with SOC 2 Type II requirements. Audit-ready from day one.
ISO 27001 Framework
Internationally certified partners with systematic, risk-based information security management.
Encryption Everywhere
AES-256 at rest, TLS 1.2+ in transit. Managed key lifecycle with encrypted backups.
Tenant Isolation
Isolated execution environments per client. Your data never mixes with other tenants.
Privacy by Design
No model training on customer data. Your data is processed only to deliver your forecasts.
GDPR-Aligned
GDPR-aligned data handling practices with support for internal IT risk assessments.
Audit Logging
Audit logging of data access, model runs, and configuration changes.
Data Residency
Control where your data lives. Regional deployments via private cloud (VPC) for data sovereignty.
Deploy your way.
From fully managed to private cloud - choose the deployment model that fits your security and compliance requirements.
Managed Cloud
Get started in minutes. We handle the infrastructure so you can focus on insights.
- Zero infrastructure management
- Auto-scaling distributed compute
- High availability with uptime commitments
- Managed updates and patches
Private Cloud (VPC)
Dedicated compute in your own virtual private cloud. Full isolation, full control.
- Runs in your AWS/GCP/Azure VPC
- Network-level isolation
- Custom security policies
- Dedicated support channel
Custom Deployment
Need additional deployment options? Talk to us about your specific regulatory and infrastructure requirements.
- Tailored to your compliance needs
- Data sovereignty consulting
- Dedicated architecture review
- Enterprise support agreement
What silent forecast failure actually looks like.
Illustrative scenarios based on patterns we observe across enterprises.
“We thought our forecasting was fine — accuracy looked stable in the dashboard. What we didn't see was that three of our pipelines had been retraining on stale data for months. By the time we caught it, we'd already over-ordered $1.8M in seasonal inventory we couldn't move.”
Sarah Chen
VP of Data Science, Example Retail Co.
Exposure identified
$1.8M
Detection gap closed
3 months → real-time
“Our forecast said service levels were at 94%. What it didn't tell us was that the confidence intervals were miscalibrated — the actual variance was 3x what the model reported. We were one bad quarter away from violating SLAs with our two largest customers.”
Marcus Webb
Head of Supply Chain, Example Manufacturing Inc.
Hidden variance exposed
3x underreported
SLA breach risk eliminated
2 accounts, $4.2M ARR
Meet the Founders
Built by engineers who understand both the science and the business of forecasting.
Lukas Voss
Co-Founder
Lukas brings experience in machine learning, quantitative finance, and strategy, with work spanning quantitative hedge funds, strategy consulting, and research in theoretical physics. He holds a double Master’s degree in Physics and Management and has published his academic work from NUS.
Thomas Kopfmüller
Co-Founder
Thomas brings over seven years of experience in applied AI for process industries, with leadership roles spanning AI implementation and portfolio management across EMEA. His background combines research in supply chain analysis, chemical engineering and business administration from TU Munich and MIT.
What's new at eomer.
Product launches, engineering insights, and roadmap updates.
Your forecasts are wrong. You just don't know it yet.
200 stores. 200 demand models. Which one is silently wrong?
Most enterprise forecasting stacks are blind to their own failure modes. Stale pipelines, uncalibrated uncertainty, silent model drift. eomer uses tabular foundation models that see what your current stack can't.