Machine Learning Solutions

Forecasting & time series

Demand, risk and capacity forecasts that beat your current baseline, backtested honestly before anyone plans against them.

Scope a backtest

Forecasts you can plan against

Every company forecasts, mostly in spreadsheets: last year plus a percentage. The question is not whether ML can forecast, it is whether it beats what you do today by enough to change decisions: stock levels, staffing, cash planning, risk limits. We start every forecasting engagement by measuring your current baseline, because that is the number to beat.

The work runs on backtesting: models are trained on your history and evaluated as if they had been running for the past year, quarter by quarter, with only the information you had at the time. No leakage, no cherry-picked windows. You see error metrics per product, region or segment before anything touches planning.

In production, forecasts arrive on schedule through an API or straight into your planning tables, with uncertainty ranges rather than single numbers. Monitoring compares forecasts against actuals as they land and flags degradation early, and retraining is a scheduled pipeline rather than a rescue project.

What you gain

Baseline first

We measure your current forecast accuracy before modeling, so improvement is a real number, not a claim.

Honest backtesting

Walk-forward evaluation on your history with strict cutoffs, exactly as the model would have run live.

Uncertainty included

Ranges and quantiles instead of single points, so safety stock and staffing decisions reflect the actual risk.

Granular where it matters

Per-SKU, per-site or per-segment forecasts, with hierarchies reconciled so the pieces sum to the totals.

Runs itself

Scheduled retraining, automated data checks and drift alerts keep the forecast fresh without a data scientist on call.

Machine Learning

From baseline to live forecasts

Measure the baseline

Quantify the accuracy and cost of today's forecast, and agree which decisions better numbers would change.

Backtest candidates

Classical statistics, gradient boosting and deep models compete on your history under identical rules.

Wire into planning

Forecasts land where decisions happen: ERP, planning sheets or an API, with uncertainty bands attached.

Monitor and retrain

Accuracy is tracked live against actuals, with alerts and scheduled retraining when the world shifts.

What you get

Backtest report comparing models against your current baseline

Forecasting pipeline with scheduled retraining

Forecast API or direct integration into planning tools

Accuracy monitoring with alerts against actuals

Documentation and runbook for your team

Questions, answered

Our demand is volatile. Can this still work?

Volatility is why quantile forecasts matter: they price the uncertainty instead of hiding it. The backtest shows honestly what is predictable and what is noise, and even a modest error reduction usually pays for itself in stock or staffing.

How much history do we need?

Two to three years captures seasonality well, but shorter histories work with external signals and cross-learning across products. The feasibility study runs on whatever you have and reports what it supports.

ML forecast or the planner's judgment?

Both. The model handles thousands of series consistently; planners override where they know things the data does not, and overrides are tracked so you learn when each side wins.