AI & Automation

ML & Predictive Analytics

Predictive machine learning uses your historical data — sales, transactions, sensor readings, support tickets — to forecast what happens next: demand, churn, fraud risk, or equipment failure. We build custom models trained on your own data rather than generic industry benchmarks, so predictions reflect how your business actually behaves.

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Where This Fits

Different from generative AI, on purpose

RAG and LLM-based tools are good at understanding and generating language. Predictive ML is a different discipline: it finds statistical patterns in structured historical data to forecast a number or a category — units to stock next month, which customers are likely to churn, which transactions look fraudulent. Both can live in the same product, but they solve different problems and use different techniques.

Forecasting

Predicting a future number — demand, revenue, footfall — from historical time-series data.

Classification

Predicting a category — will this customer churn, is this transaction fraudulent, will this machine fail soon.

Anomaly detection

Flagging data points that deviate from normal patterns — unusual spend, sensor readings out of range.

Our Process

How we build a predictive model

Data audit

Check volume, history length and quality of your historical data — this determines what's realistically predictable.

Feature engineering

Turn raw data into signals the model can actually learn from — seasonality, trends, lagged values, categorical encodings.

Model training & validation

Train and back-test against historical periods the model hasn't seen, to get an honest read on real-world accuracy.

Deployment & monitoring

Deploy as an API or scheduled job, with monitoring for prediction drift as real-world patterns shift over time.

Use Cases

What we build predictive models for

Demand & inventory forecasting

Predict stock needs by SKU and location to reduce both stockouts and overstock carrying cost.

FMCGRetail

Fraud & risk detection

Score transactions or applications in real time for fraud or default risk based on historical patterns.

FintechBFSI

Predictive maintenance

Flag machinery likely to fail soon based on sensor and usage data, before it causes unplanned downtime.

Manufacturing

Churn prediction

Identify accounts likely to churn early enough for a retention team to actually act on it.

SaaS

Technology Stack

Tools we build predictive models with

Python scikit-learn XGBoost Prophet PyTorch Pandas FastAPI PostgreSQL

Benefits

Why a custom model over a generic tool

Trained on your own data, so predictions reflect your actual customers and operations, not industry averages.

Deployed as an API your existing systems can call directly, not a standalone dashboard nobody checks.

Back-tested against real historical periods, so accuracy claims are honest, not theoretical.

Monitored for drift, so the model gets retrained before accuracy quietly degrades.

Related Case Studies

Predictive models we've shipped

Real deployments

Demand forecasting for an FMCG brand → Inventory forecasting for a retail chain → Predictive maintenance for a factory → Fraud detection for a fintech →

FAQs

Frequently asked questions

How much historical data do we need for a predictive model?

It depends on the use case, but most forecasting and classification models need at least 12-18 months of consistent historical data to capture seasonality and enough examples of the outcome you're predicting.

How accurate will the predictions be?

Accuracy is measured through back-testing against real historical periods before deployment, and we report that number honestly rather than promising a fixed figure up front — it depends heavily on data quality and volume.

Is this the same as using an LLM like GPT-4o?

No — predictive ML uses statistical models trained on structured historical data to forecast numbers or categories, which is a different technique from LLMs, which are built for language understanding and generation. We use whichever is right for the task, sometimes both together.

Can the model be retrained as new data comes in?

Yes — we set up monitoring for prediction drift and a retraining schedule so the model stays accurate as real-world patterns shift.

How is the model deployed into our systems?

Typically as an API endpoint your existing application can call, or as a scheduled batch job that writes predictions back into your database or CRM.

How long does a predictive ML project take?

A focused single-use-case model typically takes 4-8 weeks including data audit, feature engineering, training and back-testing, depending on data readiness.

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Sitting on years of data nobody's using to predict anything?

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