Predictive Analytics That Actually Work

AI and machine learning solutions for businesses ready to forecast, automate, and optimize using their data. Not hype - practical applications with measurable ROI.

$30K-$150Kinvestment range
12-20 weeksdeployment timeline
Predictiveanalytics that work
ROI-focusedmeasurable impact
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Practical Use Cases

Real business applications that solve specific problems and deliver measurable results.

Sales Forecasting - Predict revenue by product, region, customer with seasonal trends
Customer Churn Prediction - Identify customers likely to cancel, trigger retention campaigns
Demand Forecasting - Optimize inventory, reduce stockouts and overstock
Price Optimization - Predict price elasticity, maximize revenue with dynamic pricing
Product Recommendations - Personalized suggestions increase conversions and AOV
Fraud Detection - Flag suspicious transactions, unusual patterns, account takeovers
Text Classification - Auto-route support tickets, categorize emails and documents
Chatbots - Customer service automation, FAQ responses, lead qualification
WHAT WE BUILD

AI/ML Capabilities

Predictive Models

Sales forecasting, customer churn prediction, demand forecasting, and price optimization. Predict outcomes to make better decisions.

Recommendation Systems

Product recommendations, content suggestions, next-best-action. Amazon-style personalization for your customers and operations.

Anomaly Detection

Fraud detection, quality monitoring, business anomalies. Automatically flag suspicious transactions, defects, and unusual patterns.

Natural Language Processing

Text classification, sentiment analysis, chatbots, document extraction. Automate ticket routing, categorization, and customer service.

Computer Vision

Image classification, object detection, OCR. Quality inspection, inventory counting, document digitization from photos.

MLOps & Monitoring

Automated retraining, A/B testing, model performance tracking, data drift detection. Keep models accurate and production-ready.

PACKAGES

AI/ML Packages

ML Starter

$30,000 - $50,000
Timeline: 8-12 weeks
Models: 1-2 use cases

Best For: First ML project, proof of concept

  • Problem definition and scoping
  • Data assessment and preparation
  • Model development (1-2 use cases)
  • Model training and validation
  • Deployment to production
  • Monitoring setup
  • Documentation
  • 60-day support
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Most Popular

ML Professional

$70,000 - $120,000
Timeline: 14-18 weeks
Models: 3-5 use cases

Best For: Multiple ML initiatives

  • Everything in Starter, plus:
  • Multiple use cases (3-5 models)
  • Advanced feature engineering
  • Model optimization (hyperparameter tuning)
  • A/B testing framework
  • Automated retraining pipelines
  • Model performance dashboards
  • Integration with existing systems
  • 90-day support
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ML Enterprise

$150,000 - $500,000+
Timeline: 20-32 weeks
Models: 5+ use cases

Best For: AI/ML transformation

  • Everything in Professional, plus:
  • Comprehensive ML platform
  • MLOps infrastructure
  • Advanced deep learning models
  • Real-time inference at scale
  • AutoML capabilities
  • Model governance and compliance
  • Dedicated ML engineer (6-12 months)
  • Ongoing model improvement
  • 180-day support
Get Started
CASE STUDY

FashionHub E-Commerce

Fashion e-commerce, ₹45Cr revenue, 180K customers

Challenge: High customer churn (32% annually), no product recommendations, generic marketing. Losing customers after 1-2 purchases.
Investment: $95,000 | Timeline: 16 weeks
-34%
churn rate reduction (32% → 21%)
+23%
conversion rate on recommended products
₹24.4Cr
total annual benefit
257x
ROI in first year

""We were losing 32% of our customers every year. Didn't know who would leave until they were gone. Maxwize built ML models that predict churn with 84% accuracy. We intervene before customers leave. Reduced churn to 21%, saved ₹16.8 crore in the first year. The recommendation engine is equally impressive—customers love the personalization. This is competitive advantage built on our own data.""

— CEO, FashionHub E-Commerce

Perfect Use Cases:

  • Historical data (at least 6-12 months, ideally 2+ years)
  • Clear business problem (what are you trying to predict/optimize?)
  • Measurable ROI (can quantify impact)
  • Volume (enough data points to train model - typically 1000+ examples)

Not Good Use Cases:

  • No data or limited data
  • Vague goals ("be more AI-driven")
  • Rare events (10 examples of fraud in 3 years - not enough)
  • Constant change (business model changes monthly - model can't learn)
BEST PRACTICES

ML Success Principles

Start with Business Problem - Don't do ML for the sake of ML. Solve specific, valuable business problems.
Data Quality Matters Most - "Garbage in, garbage out." Clean data beats fancy algorithms every time.
Start Simple - Begin with simple models (logistic regression, decision trees). Often 80% of value for 20% of effort.
Monitor and Retrain - Models degrade over time as business changes. Monitor performance, retrain regularly.
Explainability Matters - Especially for critical decisions, understand why model makes predictions.

Ready for AI/ML?

Schedule an ML consultation. We'll discuss your business problem, data availability, expected ROI, and ML approach feasibility.

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