Your data already knows what happens next.
Trained on your data. Validated against your error thresholds.
Predictions that drive action.
Built for your data. Deployed in your workflow.
Demand & Sales Forecasting
Revenue projections, inventory requirements, and staffing load trained on your historical data and tuned to your seasonality, product mix, and market signals. Updated automatically as new data comes in.
Churn & Risk Scoring
Customer health scores and likelihood-to-lapse models that surface accounts at risk before they go quiet. Updated in near real-time as engagement, transaction, and support signals change across your CRM.
Anomaly & Fault Detection
Equipment failure prediction, fraud signal detection, and quality control flags built from your operational logs and sensor data. Catches the pattern before the incident, not after the damage report.
Propensity & Lead Scoring
Likelihood-to-convert scores, upsell readiness signals, and next-best-action rankings trained on your pipeline history. Turns your CRM into a ranked action list rather than a list of names.
Need something different?
If your prediction problem does not fit a standard category, that is not unusual. We scope custom models from scratch. Bring the business question and we will tell you if it is solvable.
Simpler than you think.
Three steps. No data warehouse required upfront.
Your raw data
CRM records, transaction logs, sensor readings, support tickets. Whatever you already collect. We work with what exists, not what you wish you had.
Trained model
A prediction model built specifically on your data, validated against your historical outcomes, and tuned to the confidence threshold that matters for your decisions.
Prediction in your workflow
Scores in your CRM, flags in your dashboard, alerts in your ops tools, or a clean API your team calls directly. No new interface to learn.
Your raw data
CRM records, transaction logs, sensor readings, support tickets. Whatever you already collect. We work with what exists, not what you wish you had.
Trained model
A prediction model built specifically on your data, validated against your historical outcomes, and tuned to the confidence threshold that matters for your decisions.
Prediction in your workflow
Scores in your CRM, flags in your dashboard, alerts in your ops tools, or a clean API your team calls directly. No new interface to learn.
Not a black box. Not a one-off.
Three things true about every engagement.
Explainability built in.
Every model we deliver includes a feature importance report explaining which signals drive each prediction. You can defend the output to stakeholders, regulators, or your own team.
Deployed where you work.
Predictions surface in your CRM, BI dashboard, operational system, or via a clean API. Not in a separate ML platform your team has to log into.
Built with a maintenance cadence.
We agree on how often we will retrain your model from the beginning, so you do not find out it has gone stale six months after it happened.
Explainability built in.
Every model we deliver includes a feature importance report explaining which signals drive each prediction. You can defend the output to stakeholders, regulators, or your own team.
Deployed where you work.
Predictions surface in your CRM, BI dashboard, operational system, or via a clean API. Not in a separate ML platform your team has to log into.
Built with a maintenance cadence.
We agree on how often we will retrain your model from the beginning, so you do not find out it has gone stale six months after it happened.
From problem to prediction.
Same sequence every time. Scoped to your data.
Start with the decision, not the data.
Problem Definition
We define exactly what you are predicting, what the cost of a wrong prediction is, and what confidence threshold makes the output actionable. This shapes every technical choice that follows: model type, feature selection, and evaluation criteria.
Find the signal in what you already have.
Data Mapping
We audit your existing data sources, identify the features that carry predictive power, and engineer the inputs the model needs. We work with what you have. We do not wait for a perfect data warehouse that never arrives.
Accuracy benchmarks agreed before training starts.
Model Training & Validation
The model is trained, tested, and validated against holdout data from your own history. Accuracy, precision, recall, and business-level error cost are all measured. The model does not proceed to deployment until it clears the thresholds we agreed at the start.
Live where your team already works.
Integration & Monitoring
We deploy the prediction where it is used: your CRM, dashboard, operations tool, or API. Drift detection and retraining triggers are built in from day one, so the model stays accurate as your data and business conditions change.
Results that hold.
Real engagements. Measurable outcomes. Client details withheld by agreement.
39%
Late delivery reduction
Reduced late deliveries by 39% using a predictive delay model built on 4 years of shipment and operational data
A US logistics operator running a ground distribution network was struggling with late delivery rates that had been climbing for 18 months. Amorisoft built a predictive ML model that flagged at-risk shipments 36 hours before scheduled delivery, giving dispatchers a window to act before a delay became a failure.
We were reacting to late deliveries, not preventing them. Amorisoft's predictive model gives us a heads-up 36 hours in advance. Now we can reroute, reschedule, or proactively communicate with customers before a delay happens.
VP of Network Operations
Ground Logistics Operator, Chicago, USA
We were reacting to late deliveries, not preventing them. Amorisoft's predictive model gives us a heads-up 36 hours in advance. Now we can reroute, reschedule, or proactively communicate with customers before a delay happens.
VP of Network Operations
Ground Logistics Operator, Chicago, USA
You already have the data.
One conversation is enough to know whether a prediction model makes sense for your situation.