Forecasts your operators actually trust and honest about their uncertainty.
Time-series and tabular models that don't just predict — they tell you how sure they are, and when you should ignore them.
- Typical duration
- 8–12 weeks
- Squad
- 2–4 senior engineers
- Starting at
- $32,000 / month
- False-positive rate
- 9%False-positive rate
- Forecast horizon
- 72 hrsForecast horizon
- Interval coverage accuracy
- ±2 ptsInterval coverage accuracy
An uncalibrated forecast is worse than no forecast.
A point estimate with no uncertainty invites false confidence. Operators learn quickly whether a model deserves trust, and once they stop believing it they route around it permanently. Calibration — knowing how often an 80% prediction is actually right — is what earns and keeps that trust.
Sound familiar?
- Forecasts the planning team quietly overrides every cycle
- Models that were accurate at launch and silently decayed
- No confidence intervals, so no way to size a buffer
- Accuracy reported on the training window, not on live data
How we deliver predictive analytics.
Four phases, each with a written definition of done. You will always know which phase we are in and what has to be true to leave it.
Frame the decision
We start from the decision the forecast informs, because that determines the horizon, the granularity, and the cost of being wrong in each direction.
Backtest honestly
Rolling-origin evaluation against naive and seasonal baselines. If we can't beat the baseline, we say so rather than ship complexity.
Calibrate
Prediction intervals validated against realized coverage — an 80% interval that contains the truth 80% of the time, not 55%.
Monitor drift
Input distribution and accuracy monitors with a documented retraining trigger, so decay is caught by a dashboard and not by a stockout.
What is included.
Every engagement is scoped to your problem, but these are the capabilities we bring to the table.
Demand forecasting
Hierarchical, seasonality-aware forecasts that reconcile across SKU, region, and channel so the numbers add up at every level.
Churn & retention modeling
Survival and uplift models that separate customers who will leave from customers an intervention can actually save.
Risk scoring
Calibrated probability outputs with reason codes, built to satisfy model-risk review in regulated environments.
Uncertainty quantification
Conformal prediction intervals with validated empirical coverage, so downstream buffers and thresholds are sized on evidence.
Feature & data pipelines
Point-in-time-correct feature stores that eliminate leakage — the single most common cause of models that look great and fail live.
Drift detection
Population stability, accuracy decay, and concept-drift monitors wired to alerts and a written retraining policy.
Technology we typically reach for.
Chosen per engagement against your constraints — never because it is the fashionable choice this quarter.
- Python
- PyTorch
- scikit-learn
- Snowflake
- dbt
- Airflow
What this looks like in production.
Predicting equipment failure 72 hours out
Vertex Manufacturing
- Problem
- Unplanned downtime cost the company an estimated $42M annually across 14 production lines.
- Solution
- A multimodal model combining SCADA telemetry, vibration sensors, and floor-camera vision to forecast failures.
- Outcome
- False-positive rate dropped to 9%, downtime reduced 34%, and OEE improved by 6 percentage points.
- False-positive rate
- 9%False-positive rate
- Downtime reduction
- 34%Downtime reduction
- OEE lift
- +6 ptsOEE lift
Capabilities that pair well with this one.
Tell us what you're trying to solve.
A 30-minute call with a senior engineer — no SDRs, no discovery deck. You will leave with an honest read on whether this is the right capability and what it would take.