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. Like all our work, the build itself is done with AI tooling, so it is live in days rather than months.
- Fixed price
- from $200
- Delivery
- 2 to 10 working days
- Billing
- Agreed before work starts
- If not live in 10 working days
- We work for free
Is machine learning the right fit?
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: the model can't learn)
Perfect use cases
- Historical data (at least 6-12 months, ideally 2+ years)
- Clear business problem (what are you trying to predict or optimize?)
- Measurable ROI (can quantify impact)
- Volume (enough data points to train a model: typically 1000+ examples)
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 and monitoring
Automated retraining, A/B testing, model performance tracking, data drift detection. Keep models accurate and production-ready.
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.
ML success principles
Start with the 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 the model makes its predictions.
AI/ML packages
Three sizes, each with a fixed price range and a fixed delivery time. We confirm the exact figure in writing before work starts.
ML Starter
First ML project, proof of concept (1-2 use cases)
- Problem definition and scoping
- Data assessment and preparation
- Model development (1-2 use cases)
- Model training and validation
- Deployment to production
- Monitoring setup
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- Documentation
- 60-day support
ML Professional
Multiple ML initiatives (3-5 use cases)
- Everything in Starter, plus:
- Multiple use cases (3-5 models)
- Advanced feature engineering
- Model optimization (hyperparameter tuning)
- A/B testing framework
- Automated retraining pipelines
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- Model performance dashboards
- Integration with existing systems
- 90-day support
ML Enterprise
AI/ML transformation (5+ use cases)
- Everything in Professional, plus:
- Comprehensive ML platform
- MLOps infrastructure
- Advanced deep learning models
- Real-time inference at scale
- AutoML capabilities
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- Model governance and compliance
- Dedicated ML engineer (6-12 months)
- Ongoing model improvement
- 180-day support
Fixed scope, fixed price, fixed date. Live in 10 working days, or we keep working for free.
Ready for AI/ML?
Schedule an ML consultation. We'll discuss your business problem, data availability, expected ROI, and ML approach feasibility.
No obligation. Live in 10 working days, or we keep working for free.