Demand, churn and capacity forecasts with confidence intervals, backtested against what you do today, delivered into the dashboards and planning tools your team already opens.
The forecast lives in a spreadsheet and one person's head
Most planning runs on last year plus a percentage, adjusted by whoever knows the business best. It works until that person is on leave, demand shifts, or the spreadsheet grows a tab nobody understands. Nobody can say how confident the number is, so stock, staff and budgets are padded to be safe.
A forecasting pipeline does not remove judgement. It gives planners a statistically honest starting point, a range around it and a record of how good past forecasts were, so the padding can shrink.
How it works
From history to a number with a range
The model produces a range, not a point. Planners adjust it with a reason, and both the forecast and the override are compared with actuals to improve the next cycle.
1. One clean history
Sales, usage, tickets and capacity records are joined, cleaned and stored as features with their dates, so every model and every planner works from the same numbers.
2. Models that admit uncertainty
Time-series and gradient-boosted models produce a central forecast and an interval. Backtesting over past periods shows how often the actual fell inside it.
3. Planners stay in charge
The forecast, its drivers and its accuracy history appear in the planning tool. Overrides are recorded with a reason and scored later, which is how trust is earned.
What you get
A forecasting capability, not a one-off model
Data pipeline and feature store
Automated, tested and documented, built by our AI data engineering team so the forecast refreshes itself when new data lands.
Forecast and scoring models
Demand, churn propensity, capacity or cash-flow models with confidence intervals, versioned and retrained on a schedule.
Backtest report
Accuracy by product, region or segment against the naive baseline and your current method, so the decision to ship is made on evidence.
Dashboards and integration
Forecasts, intervals and accuracy tracking in your BI tool, with writes into planning, ERP or CRM where a number needs to drive an action. Marketing teams can combine this with AI analytics.
Plain-language explanations
An LLM layer that explains why this week's forecast moved and answers planners' questions about the data, grounded in the model's own outputs.
Guardrails we build in
Accuracy thresholds: a model ships only when backtests beat the naive baseline and the current method on held-out periods, by a margin agreed in advance. Human-in-the-loop: forecasts inform decisions; planners make them, and overrides are first-class data. Audit trail: every forecast version, input snapshot and override is stored so any past decision can be reconstructed. PII handling: churn and propensity models use pseudonymised customer features with a documented lawful basis and fairness checks, following our AI security and governance practice and AI data privacy controls.
Typical rollout
Twelve weeks from history to a trusted number
Frame (2 weeks)
Pick one forecast with a clear decision behind it, agree the KPI and the baseline, and audit the history for gaps and quirks.
Pipeline and first model (4 weeks)
Build the data pipeline, engineer features, train candidate models and backtest them. Present the trade-offs, not just the winner.
Parallel run (4 weeks)
Planners see the model forecast beside their own for a full cycle. Overrides and actuals are captured and compared weekly.
Adopt and extend (2 weeks)
Integrate into the planning tool, schedule retraining, hand over documentation, and shortlist the next forecast on the same foundation.
Illustrative example
What a first release usually looks like
Illustrative scenario, not a client case
A distributor plans weekly stock for a few thousand product lines using last year's figures and a buyer's instinct. In the parallel run, the model's forecast is compared with the buyer's for a full season; it proves more accurate for steady lines and less accurate for promotional ones, where the buyer knows what marketing plans. The go-live rule follows that evidence: the model drives the steady lines, the buyer keeps the promoted ones with the model's range as a sanity check, and overrides are logged. Safety stock is reduced on the lines where the interval is narrow. Similar work in logistics uses the same mechanism for capacity.
How much history do we need before forecasting is worth it?
For weekly demand, two to three years gives seasonality a fair chance; for monthly churn, a year of customer outcomes is a reasonable start. Less history is workable with simpler models and wider confidence intervals. We tell you honestly in discovery if the data cannot support the question, and point you to our data readiness guide.
What does "beat the baseline" mean?
Every model is compared with the naive forecast, such as same period last year or a moving average, and with whatever your planners do today. If it does not beat both on held-out periods, we do not ship it. The comparison is in the handover pack.
Can planners override the forecast?
Yes, and they should. Overrides are captured with a reason, applied in the planning system and later compared with what actually happened. That feedback improves both the model and the planners' judgement, and it is how trust in the number is built.
Where do LLMs fit in predictive analytics?
Mostly around the edges: explaining a forecast in plain language, extracting signals from text such as reviews or tickets, and letting planners ask questions of the data. The forecast itself comes from statistical or gradient-boosted models that are cheaper, faster and easier to validate.
How do you measure the business value?
Against the KPI agreed in week one: stock-outs, overtime hours, retained customers, forecast error. Our guide to measuring AI ROI describes the method we use to attribute the change.
Ready to make AI work in production?
Tell us about your systems, your data and your constraints. We reply within one business day with a straight answer and a suggested next step.