Predictive analytics and forecasting
AI for demand forecasting, anomaly detection, risk scoring, and time-series prediction.
Predictive AI turns historical data into forward-looking insights. Demand forecasting, anomaly detection, churn prediction, and risk scoring - the models learn patterns and extrapolate. For businesses with time-series data (sales, usage, sensor readings), this unlocks proactive decisions instead of reactive ones.
I build predictive pipelines that fit your data and your questions. That might mean demand forecasting for inventory, anomaly detection on production metrics, or churn prediction for subscription services. The work is feature engineering, model selection, and integrating predictions into your workflows - not just building models in isolation.
Example AI integrations
AI services and tools I've integrated for businesses include:
Arize
ML observability and monitoring for production models. For predictive analytics, it monitors model performance and drift in production.
WhyLabs
AI monitoring and data drift detection. For predictive analytics, it detects data drift and monitors model health.
Fiddler
ML explainability and model monitoring. For predictive analytics, it explains predictions and monitors model behaviour.
Pandas AI
Natural language to dataframe queries for analytics. For predictive analytics, it lets analysts explore and forecast with natural language.
Prophet
Time-series forecasting for trend and seasonality. For predictive analytics, it forecasts demand and time-series with trend and seasonality.
Hugging Face
Time-series and tabular models for prediction. For predictive analytics, it provides pre-trained forecasting and tabular models.
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Types of businesses I work with
- Logistics and supply chain - Route optimisation, demand forecasting, inventory planning, and shipment tracking with AI.
- Education and training - AI for learning platforms, assessment, content generation, and personalised learning paths.
- B2B SaaS and tech companies - Adding AI features to existing products, building internal tools, or prototyping new ideas with a clear path to production.
Frequently asked questions
- It depends on the pattern. For demand forecasting with seasonal trends, two to three years of data is ideal. For anomaly detection, a few months of normal operation can be enough. I assess your data early and tell you honestly whether there is enough signal to build something useful.
- Yes, if you have customer activity data - logins, purchases, support tickets, usage patterns. The AI identifies patterns that precede churn and flags at-risk customers so your team can intervene. It works for subscriptions, SaaS, and repeat-purchase businesses.
- Anomaly detection spots unusual patterns in your data - a sudden drop in production quality, an unexpected spike in returns, or unusual transaction patterns. It alerts your team to problems early, before they escalate into costly issues.
- No. I build the models, integrate predictions into your existing tools, and set up dashboards or alerts. Your team uses the outputs - forecasts, risk scores, anomaly alerts - without needing to understand the underlying models.
How much historical data do I need for AI forecasting?
Can AI predict customer churn for my business?
What is anomaly detection and how can it help my business?
Do I need a data science team to use predictive analytics?
Want to discuss AI for your business?
I help businesses integrate AI into their workflows. Get in touch to talk through your specific situation.