8:20 am · Predictive analytics
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Predictive analytics that tells you what is likely next
We build machine learning models that forecast demand, churn, failures and risk from your past data, with live dashboards, alerts and automatic retraining.
The short answer
Predictive analytics runs machine learning on your historical data to forecast what is likely to happen next, such as which customers may leave, how much stock you'll need or which machine may fail. We build the models and connect them to your systems as live scoring. Then we keep them accurate as your data changes.
How it works
We find the questions worth answering.
We review your data and goals, pick the use cases worth the most to you, and check they can be done. This usually takes one to two weeks.
We prepare the data.
We clean the raw data and turn it into features a model can learn from, and we set up pipelines that keep it fresh for training. That usually takes two to three weeks.
We build and train the models.
We use custom machine learning and AutoML, with ensemble methods, tuning and validation on data the model hasn't seen. This usually takes three to four weeks.
We deploy and connect them.
The models run as real-time scoring through an API, with dashboards, alerts and links to your existing systems. Expect two to three weeks.
We monitor and retrain.
Automatic checks watch for drift, and the model is retrained when new data arrives, so accuracy holds over time.
What you get
Screens are mock-ups
A data review with the high-value use cases ranked against your goals
Custom machine learning models, tested and validated
A real-time prediction pipeline that handles large volumes
AutoML so your team can run simple forecasts themselves
Dashboards showing predictions and outcomes side by side
REST APIs and real-time scoring linked to your systems
Monitoring that detects model drift and loss of accuracy
Automatic retraining as new data and outcomes come in
What the AI adds
Agents that act on predictions.
Within rules you set, AI agents can take the next step on their own, like reordering stock or alerting a customer manager.
Predictions at the edge.
Sensor and IoT data gets scored on site in under a second, so you can respond fast to failures or changes.
Digital twins.
A virtual copy of your operation runs many what-if scenarios before you commit.
Industry models.
We start from models already tuned for manufacturing, healthcare, finance and retail.
In numbers
- 10xfaster decision-making
- 500+projects completed
- 98%client success rate
- 24/7expert support
The market
- $14.1BCurrent market size
- 43.7%CAGR growth rate
- Digital twin integrationSimulate millions of scenarios
Success story
ManufacturePro
85% equipment failure prevention with AI predictive analytics
We built an autonomous AI system for ManufacturePro that reads real-time sensor data and runs predictive maintenance models. It prevented 85% of equipment failures, cut downtime by 50% and saves $2.3M a year in costs.
Results
- 85%Failure prevention rate
- 50%Downtime reduction
- 87%Forecast accuracy
- $2.3MAnnual savings
Tools we work with
TensorFlow and PyTorch, scikit-learn and XGBoost, Prophet and ARIMA for time series, H2O.ai for AutoML, AWS SageMaker, Google Cloud AI and Azure Machine Learning in the cloud, Databricks MLflow for model tracking, Apache Spark, Kafka and Airflow for data, and Kubernetes to run it all.
Where it fits
Predictive analytics fits a business that has a few years of records and a decision it makes often, like how much to order, who to call or when to service a machine. If your data is spread across many systems, start with big data services. For a first forecast or dashboard on a modest dataset, see data and machine learning.
Price
We quote every project in writing after a free consultation, and we agree the timeline in the same quote. Cloud costs are paid separately. You'll find full details on the pricing page.
FAQ
What can predictive analytics forecast?
Common examples are demand and sales, customer churn, equipment failures, late payments, fraud risk and stock levels. If you have a record of what happened and the conditions around it, a model can usually learn the pattern.
How accurate will the predictions be?
It depends on your data. We test every model on past data it hasn't seen and show you the accuracy before you rely on it. After launch, monitoring catches any drop so the model gets retrained.
Can people without a data background use it?
Yes. Predictions show up in dashboards, alerts and the tools your team already uses. AutoML lets non-technical staff run simple forecasts themselves.
Can it explain why it made a prediction?
Yes. We use explainable models and keep an audit trail, so you can see which factors drove each prediction. That matters when the decision is about customers, credit or safety.
How much does predictive analytics cost?
We quote every project in writing after a free consultation, based on your data, the number of models and how they connect to your systems. Cloud costs are paid separately.
Related
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Pictures are illustrations
Next step
Tell us the decision you'd like to see coming, and we'll check whether your data can predict it.
Book a free consultation
