Why Choose Ratech
We deliver business-focused ML solutions that increase revenue and reduce costs
7+
years of ML production experience in our core team
3x
faster delivery than traditional development agencies
Business-first
we speak your language, not just data science jargon
Long-term
we focus on partnership approach to improve continuously
Fast iteration
working models in 4-6 weeks, not 6 months
ROI in 2-3 Months
measurable impact on your bottom line
Business Problems We Solve with ML
You're losing customers but don't know why until it's too late
Predictive churn models that identify at-risk customers 30-60 days before they leave. Get alerts with specific retention actions: discount offers, feature recommendations, or account manager outreach.
Your pricing strategy is guesswork costing you millions
Dynamic pricing models that optimize for maximum revenue. Predict price elasticity, identify willing-to-pay segments, and adjust prices automatically based on demand, inventory, competition, and customer value.
Marketing budget wasted on customers who'll never convert
Lead scoring models that predict conversion probability with 85%+ accuracy. Focus sales efforts on high-value prospects and stop burning resources on leads that will never close.
Inventory sitting in warehouses while other products stock out
Demand forecasting that predicts what customers will buy, where, and when. Optimize stock levels, reduce carrying costs by 30%, and eliminate lost sales from stockouts.
Customer support drowning in tickets with no prioritization
Intelligent ticket routing that predicts urgency, sentiment, and required expertise. High-value angry customers get immediate attention; simple queries get automated responses.
Fraud losses eating into profits while false positives kill conversions
Real-time fraud detection that catches 95% of fraudulent transactions while approving 98% of legitimate ones. Adaptive models that evolve with new fraud patterns automatically.
Machine Learning Services
From predictive analytics to recommendation engines — ML capabilities that drive measurable business outcomes
Let’s talkML Consulting & Strategy Building
We provide machine learning consulting services to help you identify the right ML solutions for your business needs. Our team of experts analyzes data, assesses feasibility, and prepares a custom roadmap to ensure the successful implementation of machine learning into your business.
Predictive Analytics & Forecasting
Build models that forecast sales, demand, churn risk, and customer lifetime value. Make proactive decisions based on what will happen, not just what happened. Optimize inventory, staffing, and marketing spend.
Recommendation Systems
Netflix-style recommendation engines for your products or content. Increase basket size, time on site, and repeat purchases by showing customers what they actually want before they search for it.
Customer Segmentation & Personalization
Discover hidden customer segments based on behavior patterns. Deliver personalized experiences, product recommendations, and targeted offers that increase conversion by 20-40% and customer satisfaction.
Churn Prediction & Prevention
Identify customers likely to leave before they do. Trigger automated retention campaigns or alert account managers with specific actions proven to work for similar customers.
Industries We Serve
Applying ML to solve revenue-critical challenges in your industry
Marketing
Recruitment & HR
E-commerce
SaaS
Real Estate
Construction
Banking & Finance
Healthcare
Logistics
Education
Travel & Hospitality
Web Agencies
See all industries AI solutions
Marketing ML Solutions
Stop wasting ad spend on the wrong people
ML models predict which prospects will convert before you spend a dollar. Score every lead automatically and route high-probability converters to sales while nurturing lukewarm prospects differently.
Know which campaigns will work before launch
Predictive models analyze past campaign performance, audience signals, and market conditions to forecast ROI. Kill losers in testing phase and double down on winners.
Personalize at scale without manual segmentation
ML clusters customers by behavior patterns you'd never spot manually. Each segment gets different messaging, offers, and timing — all optimized automatically for maximum engagement.
Predict customer lifetime value on day one
Know which new customers will be worth $10K vs $100 within their first purchase. Adjust acquisition costs, onboarding, and retention efforts based on predicted value, not guesswork.
Recruitment & HR ML Solutions
Hire winners, not resumes
ML models trained on your top performers predict which candidates will succeed in your culture. Screen thousands of applicants and surface hidden gems that match your success profile.
Stop attrition before exit interviews
Predict which employees will quit 3-6 months before they do based on engagement patterns, tenure, compensation, and role changes. Intervene with targeted retention actions.
Optimize compensation to retain stars
ML analyzes market data, flight risk, and performance to recommend optimal compensation packages. Keep high performers happy while managing payroll budget efficiently.
Identify future leaders in your organization
Predictive models spot employees with leadership potential years before promotion time. Build succession pipeline and development plans based on objective data, not manager bias.
E-commerce ML Solutions
Know what customers will buy before they do
Recommendation engines that predict next purchase with scary accuracy. Show products at exact right moment in browsing journey to maximize conversion and basket size.
Dynamic pricing that maximizes profit, not just sales
ML finds optimal price point for every product, customer segment, and situation. Increase margins on items with low price sensitivity while offering strategic discounts where they drive volume.
Predict which customers will return products
Identify return-prone purchases before shipping. Offer incentives to reduce returns or adjust inventory planning for high-return items to manage costs.
Inventory optimization that eliminates stockouts
Forecast demand by SKU, location, and season with 90%+ accuracy. Reduce carrying costs by 30% while increasing in-stock rate to 98%+.
SaaS ML Solutions
Know who's churning before they cancel
Predictive models analyze usage patterns, support tickets, and engagement to identify at-risk accounts 60 days early. Trigger automated saves or alert customer success with specific intervention plays.
Upsell at the perfect moment
ML detects when customers are ready for higher plans based on feature usage and growth trajectory. Recommend upgrades when probability is highest, not on random renewal dates.
Predict expansion revenue by account
Forecast which existing customers will grow spending next quarter. Focus expansion efforts on high-potential accounts instead of spreading resources equally.
Optimize trial-to-paid conversion
Models identify which trial users will convert and which won't. Personalize trial experience, adjust sales touchpoints, and extend trials strategically based on conversion probability.
Real Estate ML Solutions
Price properties to sell fast at top dollar
ML models analyze comparable sales, market trends, and property features to recommend optimal listing price. Properties priced by ML sell 15% faster at 3% higher prices.
Predict which leads will actually buy
Score inquiries based on browsing behavior, demographics, and engagement to identify serious buyers. Agents focus on high-probability deals instead of tire-kickers.
Forecast neighborhood appreciation
ML analyzes development patterns, demographics, schools, and economic indicators to predict 3-5 year appreciation. Advise buyers and investors with data-driven neighborhood recommendations.
Match properties to buyers automatically
Recommendation engines that understand buyer preferences better than agents. Surface listings that match unstated preferences based on viewing history and similar buyer behavior.
Construction ML Solutions
Predict project delays before they happen
ML analyzes weather, subcontractor performance, supply chain signals, and historical data to forecast delays 30-60 days early. Adjust schedules and resources proactively.
Estimate costs with contractor-level accuracy
Models trained on your completed projects predict true cost within 5% before bidding. Stop underpricing jobs or losing bids with inflated estimates.
Optimize equipment utilization across sites
Predict equipment needs by project and phase. Reduce rental costs by 25% through better allocation and timing of equipment between sites.
Forecast material price increases
ML models analyze commodity markets, supplier data, and economic indicators to predict price spikes. Time bulk purchases to minimize cost increases.
Banking & Finance ML Solutions
Approve more good loans, decline more bad ones
Credit scoring models that predict default risk with higher accuracy than traditional FICO. Expand lending to underserved customers profitably while reducing losses.
Catch fraud that rule-based systems miss
ML detects complex fraud patterns and learns from new schemes automatically. Reduce fraud losses by 40% while approving 99% of legitimate transactions instantly.
Predict which customers will buy next product
Cross-sell models that identify high-probability opportunities for credit cards, loans, investments. Increase product adoption by 30% without annoying customers with irrelevant offers.
Forecast cash flow and liquidity needs
Predictive models analyze transaction patterns, seasonal trends, and economic indicators to forecast daily cash positions. Optimize reserves and reduce idle cash.
Healthcare ML Solutions
Predict no-shows before appointment day
ML identifies patients likely to miss appointments 48 hours in advance. Overbook strategically, send targeted reminders, or offer reschedule incentives to maximize provider utilization.
Forecast patient volume by department and hour
Accurate staffing predictions that reduce overtime by 20% while maintaining service levels. Know exactly how many nurses you need next Tuesday at 2pm.
Identify high-risk patients before complications:
Predictive models flag patients likely to readmit or develop complications. Trigger preventive interventions, extra monitoring, or discharge planning.
Optimize appointment scheduling
ML balances appointment length predictions, provider efficiency, and patient preferences to maximize throughput while minimizing wait times and overtime.
Logistics ML Solutions
Predict delivery delays before they happen
ML analyzes traffic, weather, driver performance, and historical patterns to forecast late deliveries. Proactively notify customers and reroute when possible.
Optimize routes beyond basic GPS
ML factors in traffic predictions, delivery time windows, truck capacity, and driver hours to create routes that save 15-20% in fuel and time.
Forecast shipping volume to optimize capacity
Predict daily/weekly volume by route and customer. Book carriers at optimal rates, schedule staff efficiently, and avoid scrambling for last-minute capacity.
Predict equipment failure before breakdowns
Sensors and ML models forecast when trucks, forklifts, and warehouse equipment will fail. Schedule maintenance during off-hours to avoid operational disruption.
Education ML Solutions
Predict which students will drop out
Early warning system that identifies at-risk students based on grades, attendance, engagement, and demographic factors. Intervene with support before they leave.
Personalize learning paths for better outcomes
ML adapts content difficulty and pace to individual student needs. Struggling students get extra practice; advanced students move faster — both achieve better results.
Forecast enrollment to optimize resources
Predict applicant yield, course enrollment, and class sizes. Right-size staffing, classrooms, and resources without over or under-investing.
Optimize financial aid allocation
Models predict which students need aid to enroll and which will come regardless. Maximize enrollment within financial aid budget through strategic allocation.
Travel & Hospitality ML Solutions
Dynamic pricing that fills rooms at maximum rate
ML predicts demand by room type, season, and event to optimize prices daily. Increase revenue per available room by 18-25% compared to static pricing.
Predict cancellations to overbook intelligently
Forecast which reservations will cancel based on booking patterns, customer history, and external factors. Overbook just enough to maintain 100% occupancy without turning away guests.
Personalize offers that drive direct bookings
Recommendation engines suggest room upgrades, packages, and services each guest is most likely to purchase. Increase ancillary revenue by 30% through targeted upselling.
Forecast demand spikes from events and trends
ML analyzes event calendars, social media, and booking trends to predict demand surges. Adjust pricing and inventory allocation before competitors react.
Web Agencies ML Solutions
Predict which prospects will become high-value clients
Lead scoring based on industry, project scope signals, budget indicators, and engagement patterns. Focus business development on high-probability, high-value opportunities.
Forecast project profitability before signing
ML trained on past projects predicts actual hours, change orders, and client satisfaction. Avoid money-losing projects or price them appropriately upfront.
Churn prediction for retainer clients
Identify accounts at risk of leaving 60-90 days early based on engagement, satisfaction signals, and usage patterns. Proactively address issues before they become deal-breakers.
Resource allocation optimization
Predict project needs and staff availability to optimize team utilization. Reduce bench time while avoiding burnout from over-allocation.
Service Options
Start with ML feasibility study to validate use case, or jump into full model development
Consulting
Not sure if ML will work for your use case? Start with a low-risk consulting engagement where we analyze your data, validate ML viability, and create a roadmap with expected business impact and ROI.
Book 30 min call
End-to-end ML Development
Entrust the entire ML lifecycle to us: data analysis, model development, validation, integration into your systems, deployment to production, and continuous monitoring and retraining.
Contact UsRatech Expertise and Benefits of Machine Learning services
Deep AI/ML Expertise
Our engineers have over 7 years of experience in AI and Machine Learning, delivering E-commerce, Fintech, SaaS solutions. Proficient in ML frameworks, programming languages and skilled project management we ensure top-quality.
Lean ML Development
We start small — with clear goals and fast validation. Through discovery, rapid prototyping, and ROI-based prioritization, we identify where ML brings the biggest impact. You get fast results, minimal risk, and clear ROI.
Secure and Compliant AI
Ratech follows strict compliance with GDPR, HIPAA, and ISO. Every project starts with NDA and data protection protocols. Your AI product remains legally safe, transparent, and fully aligned with international data standards. We handle security from day one.
Always-On Maintenance
We continuously retrain and optimize your models, adapting to new data and business changes. Your system stays accurate, secure, and profitable 24/7. We provide proactive monitoring, and improvements to keep your competitive edge.
All-in-One
We handle everything — strategy, development, integration, and long-term support. One accountable partner driving your ML success from concept to scale. No coordination between multiple vendors, no gaps in responsibility.
Cost Efficiency
By automating manual workflows, improving prediction accuracy, and reducing decision time, you save up to 40% in operational expenses within months after launch. We leverage existing AI platforms when possible, build custom when necessary.
Use Cases and Solutions you get with Ratech
From demand forecasting to customer lifetime value — practical ML capabilities that drive bottom-line results
Data-driven decision making
AI transforms raw business data into actionable insights. Identify trends, predict outcomes, and make strategic decisions based on evidence — not intuition. Empower every department with real-time intelligence.
Customer Lifetime Value (CLV) Prediction
Know which customers will be most valuable over their relationship with you. Adjust acquisition spending, retention efforts, and service levels based on predicted lifetime value, not just initial purchase.
Next Best Action Recommendation
ML suggests optimal next step for each customer: which product to recommend, what offer to make, when to reach out. Increase conversion rates by 25-40% through personalized actions.
Demand Forecasting & Inventory Optimization
Predict future demand by product, location, and time period. Optimize inventory levels to reduce carrying costs by 30% while maintaining 98%+ in-stock rates on best sellers.
Customer Segmentation Beyond Demographics
Discover behavioral segments based on usage patterns, purchase history, and engagement. Target marketing, products, and experiences to segments that actually predict behavior.
Propensity Modeling for Marketing
Predict which customers are most likely to respond to specific campaigns, buy certain products, or churn. Focus marketing spend on high-propensity audiences for maximum ROI.
Alex Zhytnykov
Machine Learning Expert at Ratech
Get a free consultation from our expert to discover how ML can increase your revenue and reduce costs
Get a free consultationStep-by-step process: Machine Learning
1
Business Discovery & Data Assessment
Business analysis
Problem definition: Identify specific business problem ML will solve and define success metrics in revenue, cost, or efficiency terms.
Current state evaluation: Understand current decision-making process, performance, and economic impact of the problem you're solving.
ROI modeling: Estimate potential business value from ML solution based on expected accuracy improvement and operational impact.
Stakeholder alignment: Ensure key decision-makers agree on objectives, success criteria, and how ML outputs will be used.
Data evaluation
Data discovery: Catalog available data sources, formats, volume, and quality. Identify gaps that might limit model performance.
Feature analysis: Explore which data points correlate with outcomes you want to predict. Validate that sufficient signal exists.
Data quality audit: Assess completeness, accuracy, and reliability of data. Identify cleaning and enrichment needs before modeling.
Feasibility validation: Confirm ML is appropriate solution and that your data can support required prediction accuracy.
2
Data Preparation & Feature Engineering
Data collection
Pipeline building: Extract data from databases, APIs, files, and third-party sources. Automate ongoing data collection for model retraining
Historical aggregation: Compile training datasets representing various scenarios, time periods, and outcomes for robust model learning.
Data enrichment: Enhance raw data with external sources (market data, weather, demographics) that improve prediction accuracy.
Quality assurance: Verifying data completeness, consistency, and suitability for model development.
Feature engineering
Variable creation: Transform raw data into predictive features — ratios, trends, aggregates, time-based patterns, interaction terms.
Feature selection: Identify most predictive variables using statistical tests and domain expertise to reduce noise and improve model performance.
Encoding and scaling: Convert categorical data to numerical form, normalize ranges, and prepare features for algorithm consumption.
Train/test splitting: Separate data into training, validation, and test sets ensuring temporal integrity and no data leakage.
3
Model Development & Training
Algorithm development
Approach evaluation: Test multiple algorithm types (linear models, tree ensembles, neural networks) to find best fit for your problem.
Baseline establishment: Create simple benchmark model to measure improvement from more sophisticated approaches.
Prototype development: Build quick proof-of-concept models to validate approach and demonstrate feasibility early.
Architecture design: Design model structure, hyperparameters, and training strategy for optimal performance.
Model training
Iterative refinement: Train models on historical data, evaluate performance, adjust features and parameters, retrain until target accuracy achieved.
Cross-validation: Test models on multiple data subsets to ensure predictions generalize to new data
Ensemble methods: Combine multiple models when appropriate to achieve higher accuracy than single best model.
Performance optimization: Tune for both accuracy and speed to meet business requirements for prediction quality and response time.
4
Validation & Testing
Training process
Parameter configuration: Setting optimal hyperparameters, learning rates, and training strategies.
Progressive learning: Conducting iterative training cycles to achieve target accuracy levels.
Real-time tracking: Monitoring performance metrics and adjusting approach dynamically.
Generalization focus: Implementing techniques to prevent overfitting and ensure robust predictions.
Quality assurance
Performance testing: Evaluating model accuracy on unseen data to verify real-world effectiveness.
Multi-fold validation: Running comprehensive tests across diverse data segments for reliability.
Variant comparison: Testing different model configurations to identify optimal solution.
Failure investigation: Analyzing prediction errors to refine and enhance model capabilities.
5
Deployment & Monitoring
Launch process
Environment preparation: Setting up production infrastructure with servers, containers, and cloud resources.
Business integration: Connecting AI capabilities seamlessly into your operational systems.
Monitoring setup: Installing tracking tools for uptime, performance, and accuracy metrics.
Team enablement: Training your staff to effectively utilize and manage the AI solution.
Continuous maintenance
Performance oversight: Actively monitoring production behavior and addressing issues promptly.
Periodic updates: Retraining models with new data to maintain precision and relevance.
System enhancement: Scaling infrastructure and optimizing performance as demand increases.
Dedicated support: Providing ongoing technical assistance and implementing improvements.
Looking for something quicker and easier? We’ve got you covered!
Let’s talkEngagement models
Select the collaboration format that aligns with your ML maturity and business objectives
Dedicated Team
A complete ML team — including data scientists, ML engineers, and data engineers — working exclusively on your predictive models from business discovery to production deployment and continuous improvement.
Time & Materials
Pay only for actual development time and resources used, with complete flexibility to adjust ML scope, explore new use cases, and pivot priorities as you discover what drives most business value.
Augmented Team
Strengthen your existing data team with our ML specialists who integrate seamlessly into your workflows, filling technical gaps and accelerating your predictive analytics initiatives without hiring overhead.
Ratech — Your Partner in Growth & ML Development
~35%
average revenue increase from ML personalization
50%
cost reduction through predictive maintenance
90%+
prediction accuracy on business outcomes
2-4
months typical time to measurable ROI
Technology stack
Ratech uses proven ML frameworks and tools to build accurate, scalable predictive models
ML Algorithms and Approaches
ML Frameworks
Data Processing
Databases & Data Storage
Feature Engineering
Model Development
Languages
Deployment
Monitoring
Cloud Platforms
ML Algorithms and Approaches
Supervised Learning (Classification, Regression, etc.)
Semi-Supervised Learning
Unsupervised Learning (Clustering, Outlier Detection, etc.)
Reinforcement Learning
Probabilistic ML
Deep Learning Neural Networks, Transformers, Diffusion Models
Computer Vision (Image Classification, Image Segmentation, Object Detection, OCR, etc.)
NLP (Text Classification, Sentiment Analysis, Text Summarization, Text Generation, etc.)
ML Frameworks
Scikit-learn
XGBoost
SciPy
LightGBM
CatBoost
TensorFlow
Keras
PyTorch
PyTorch Lightning
Amazon SageMaker
Data Processing
Pandas
NumPy
Dask
Apache Spark
PySpark
Polars
Databases & Data Storage
PostgreSQL
MySQL
SQLite
MongoDB
Redis
Elasticsearch
Snowflake
BigQuery
Feature Engineering
Feature-engine
Category encoders
TPOT
Featuretools
Model Development
Jupyter Notebook
Google Colab
DataSpell
MLflow
Weights & Biases
Optuna
Languages
Python
JavaScript (Node.JS)
SQL
C++
Deployment
FastAPI
Flask
TensorFlow Serving
TorchServe
BentoML
AWS SageMaker
Monitoring
Evidently AI
Prometheus
Grafana
Datadog
New Relic
Sentry
Cloud Platforms
AWS SageMaker
Google AI Platform
Azure ML
Databricks
FAQ
What is machine learning?
Machine learning is an area of technology focused on developing algorithms that enable machines to learn from data. Once trained, these machines can make decisions and predictions on their own when they see new information. For example, a machine trained with images of various vehicles can learn to identify whether an image contains a car, a truck, or a motorcycle.
What is the difference between ML and AI?
Artificial intelligence (AI) is a system designed to perform tasks that would normally require human intelligence, such as understanding natural language or recognizing patterns. Machine learning is a subset of AI where the system learns and improves on its own by analyzing data and making adjustments without human intervention.
What kind of applications can you build using machine learning?
Machine learning can be applied to create diverse applications such as predictive healthcare diagnostics, financial market analysis, personalized retail recommendations, and content personalization for streaming services, to name a few.
What does a machine learning developer do?
A machine learning developer designs and builds algorithms that enable computers to learn from and make decisions based on data. They are responsible for creating models, training them on data sets, integrating them into existing systems, and continuously improving their performance based on feedback.
How do I know if my business problem needs ML?
ML excels at finding patterns in large datasets to make predictions. If your problem involves forecasting (sales, churn, demand), classification (fraud, quality, sentiment), or optimization (pricing, recommendations), and you have historical data, ML likely helps. During discovery, we validate if ML will deliver ROI before any development starts.
How much data do we need for machine learning?
Requirements vary by problem complexity. Simple models may work with 1,000-10,000 examples, while complex predictions need 100,000+. More important than volume is data quality and relevance. We assess your specific situation during discovery and can often augment limited data with transfer learning or synthetic data techniques.
Will ML models work with our existing systems?
Yes. We integrate ML predictions into your workflows via APIs, database updates, or file exports. Models can trigger actions in CRMs, adjust prices in e-commerce platforms, score leads in marketing automation - whatever your systems support.
What if the model makes wrong predictions?
All ML models make errors - the key is ensuring errors don't catastrophically harm business. We implement confidence thresholds, human-in-loop reviews for high-stakes decisions, and fallback logic. Models also improve over time as we retrain on new data including corrected predictions.
How do we maintain models after launch?
We provide ongoing monitoring and retraining services. Models automatically alert when accuracy degrades, we investigate root causes, retrain on fresh data, and redeploy improved versions. You receive regular reports on model performance and business impact.