Use machine learning on your business history to forecast demand, sales, cash flow, churn and maintenance needs, with accuracy you can measure.
Predictive AI uses historical data and machine learning models to estimate what is likely to happen next. DO ALL helps you assess whether your ERP, CRM, inventory, finance and maintenance data can support useful forecasts, then build, test and monitor models against measured error rates. Good predictive analytics explains its inputs, shows uncertainty and leaves the final decision with an accountable person.
Highlight likely changes in demand, payments or risk before they appear in month-end reports.
Support stock, staffing, cash and maintenance plans with tested forecasts.
Track forecast accuracy over time so teams know when to rely on a model.
Forecast item demand by location and period using sales history, seasonality and known events to support reorder and replenishment planning.
Estimate pipeline conversion, revenue and collections using CRM stages, invoices and payment history.
Score customers by likelihood of churn or late payment, with the main contributing factors shown for review.
Assess failure risk from service history, runtime and condition readings where equipment data is sufficient and failure examples exist.
Flag unusual transactions, consumption or performance patterns for investigation rather than automatic action.
Present forecasts with ranges, key drivers and accuracy history, and alert owners when model performance drifts.
Forecast demand by branch and item to reduce stock-outs and slow-moving inventory.
Anticipate cash position and collection risk to plan payments and follow-up.
Prioritise inspections for assets showing early signs of failure risk.
We confirm requirements, dependencies and acceptance criteria with your team. The proposal sets out the included capabilities, integrations and responsibilities.
Review history, completeness and the business events that affect the target.
Train candidate models and compare them with simple baselines on past periods.
Show forecasts, ranges and drivers inside the workflow where decisions are made.
Track accuracy, retrain when patterns change and review results with owners.
Predictive AI estimates likely outcomes from historical patterns. A useful forecast states its error range and the decision it supports, and is compared with a simple baseline before anyone relies on it.
Combine sales history, seasonality, promotions and lead times to forecast demand by item and location. Measure forecast error by product group and keep planners able to override with documented reasons.
Use CRM pipeline stages, invoice history and payment behaviour to estimate revenue, collections and customer churn risk. Show the main contributing factors so finance and sales teams can challenge a score.
Failure prediction requires reliable runtime, condition and service records with enough recorded failures. Where that evidence is missing, start with preventive and condition-based maintenance in a CMMS and collect better data.
Extend predictive analytics with accuracy reporting, scenario comparison and clear ownership so forecasts improve planning rather than adding another unchecked number.
Explore these extensions during discovery. Availability, configuration, integrations and service commitments are confirmed in your agreed scope.
Discuss tracking forecast error by product, branch or customer segment over time, compared with a simple baseline, so planners know where the model adds value.
Explore comparing forecasts under different assumptions, such as price changes, promotions or new branches, while keeping the base forecast and its assumptions visible.
Agree how often models are reviewed, which signals trigger retraining and who approves a changed model before it replaces the current one.
Predictive AI uses statistical and machine learning models trained on historical data to estimate future outcomes, such as demand, revenue or failure risk. It produces probabilities and ranges, not certainties.
It depends on the target. Seasonal demand forecasting usually needs at least one to two years of consistent history, while rare events such as failures need enough recorded examples. A data readiness review confirms this before modelling.
Accuracy cannot be promised in advance. Models are back-tested on your past data and compared with simple baselines, and the measured error is shown so you can decide where to rely on them.
No. Predictive AI estimates numbers or risk scores from historical patterns. Generative AI creates new content such as text. Many businesses use both for different tasks.
They can feed workflows such as reorder suggestions or inspection tasks. We recommend human approval for financial, safety or customer-impacting actions until accuracy is proven.
Models can lose accuracy when patterns shift. Monitoring compares predictions with actual results and flags drift so the model can be reviewed or retrained.
Yes. Planners can adjust forecasts with recorded reasons, and comparing adjusted and model forecasts over time shows where each performs better.
Sales, purchasing, inventory, finance, CRM and maintenance records are common sources. External data such as holidays or weather can be assessed where it is relevant and reliable.
Timing depends on data readiness. A data assessment and back-test on historical periods show likely usefulness before forecasts are used in live planning.
Feedback is sent privately to the DO ALL team by email. Public review publishing is currently paused.
Explore business analytics, BI dashboards and AI reporting around reconciled source data. Define KPIs, permissions, refresh timing and review controls before relying on a generated recommendation.
Learn more →Know what you have, where it is and when to reorder across every warehouse and store.
Learn more →Bring fleet maintenance, equipment service schedules, work orders and spare parts into one coordinated maintenance workflow. Plan preventive maintenance, track repair costs and give technicians a clear next action across vehicles, machinery and facilities.
Learn more →Walk us through your locations, users and daily workflows for a focused demo.
