Data analysis and forecasting your team actually uses
Data analysis, models and dashboards that turn operational data into forecasts and decisions: demand planning, capacity, churn, anomalies. Built to be understood on the day of the decision — not to win a Kaggle competition.
Forecasting projects fail in the last mile: the model is accurate, but the dashboard is ignored. We start from the decision you need to make, work backwards to the data, and deliver forecasts with error bars and plain-language explanations alongside the numbers.
We build on your existing stack where possible — SQL warehouses, spreadsheets your team already opens, BI tools already licensed — and add machine learning where it beats simple baselines. Every pipeline is documented and maintained, so the forecast is still running next quarter.
What is included
Forecasting
Demand, capacity and revenue forecasts with quantified uncertainty, validated against real baselines.
Anomaly detection
Automatic flags when metrics drift — before the monthly report finds out.
Decision-ready dashboards
The number, the trend and the confidence on one screen, in the tool your team already uses.
Maintainable pipelines
Documented, scheduled data pipelines that keep working when people change.
Baselines before models
We prove the simple method first, then add machine learning only where it measurably wins.
How we work
- 1
Define the decision
Which decision, how often, on what data. The forecast is shaped by the decision, not the other way round.
- 2
Model and validate
Baselines, then models — evaluated on held-out periods with error bars you can quote.
- 3
Deliver and maintain
Dashboards and scheduled runs in your stack, plus handover so your team owns the pipeline.
Frequently asked questions
How accurate are forecasting models?
Honest answer: as accurate as your data allows, and we quantify it. Every forecast ships with validated error margins measured on held-out periods, so you know how much to trust it before you plan on it.
Do we need a data warehouse first?
No. We work with what you have — databases, spreadsheets, exports — and recommend a warehouse only when volume or governance actually demand it.
Which tools do you use?
Your stack first: SQL, Python, your BI tool. We add forecasting and anomaly-detection libraries where they beat baselines, documented so your team can maintain them.