Machine Learning and Predictive Analytics

Why Machine Learning? Why Now?

In fast-moving markets, reacting is too slow. Predictive systems help companies get ahead:

Anticipate customer behavior

Spot patterns in customer actions to predict future behavior and guide business decisions.

Forecast inventory or supply chain needs

Use historical and real-time data to stay ahead of demand shifts and supply chain risks.

Detect anomalies before they disrupt operations

Identify unusual patterns early, reducing the risk of costly disruptions.

Optimize resource allocation and planning

Support better use of assets, budgets, and workforce through accurate predictions.

Built for Production, Not Just Prototypes

We deliver machine learning systems that work in real environments, not just in testing:

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Trained on your business context and goals

Models are built with a deep understanding of your data and your priorities.

Deployed into real systems and workflows

Machine learning outputs connect with your existing tools and day-to-day processes.

Monitored, retrained, and improved continuously

We track model performance over time and update it as your business evolves.

What We Deliver

Whether you need a full machine learning solution or targeted support, we cover the entire lifecycle:

Data Pipeline Design & Cleaning

We prepare your data for machine learning by building pipelines that ingest and transform both structured and unstructured data. This foundation helps models produce reliable results that drive business decisions.

Model Training & Evaluation

We develop machine learning models that fit your goals — from demand forecasting to customer classification. Each model is trained and evaluated using proven frameworks to deliver accurate and actionable insights.

Deployment & Monitoring

We integrate models into your business systems. Whether through APIs, inside applications, or on cloud platforms, models are monitored in production and continuously retrained as your data evolves. This helps keep predictions aligned with changing business needs.

Computer Vision App for Automotive

A proof-of-concept AR try-on app for automotive parts, built with ARKit and Unity. The project addressed a key industry challenge: delivering a stable, high-quality AR experience in unpredictable lighting and environments. We trained a custom AI model with on-device object recognition for better performance compared to cloud processing. Extensive testing with LiDAR and AI improved object recognition and positioning accuracy.

The solution revealed opportunities to boost AR performance through better AI models that improved the user experience. For companies exploring AR in complex settings, this project showcases how to use AR and achieve reliable, high-impact applications.

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Example Use Cases

Advanced AI models can unlock powerful gains across many business areas — from optimizing operations and resource use to increasing revenue and improving customer retention. Below are some of the most effective applications we help companies implement:

Demand Forecasting for Retail & Supply Chain

Use predictive models to drive smarter inventory decisions and reduce revenue loss from both stockouts and excess inventory, helping retailers and manufacturers respond faster to real-world demand shifts and market fluctuations.

Churn Prediction & Customer Segmentation

Spot early signals of customer disengagement by analyzing behavior patterns over time, so marketing and product teams can create precise interventions that improve retention and lifetime value.

Pricing Optimization

Leverage real-time and historical data to adjust pricing strategies dynamically, enabling businesses to stay competitive across changing market conditions, seasonal trends, and shifting consumer demand.

Operational Efficiency

Automate and enhance complex decision-making processes across core operational areas such as logistics, human resources, and manufacturing workflows — freeing up valuable time and reducing the cost of manual oversight.

Predictive Maintenance

Detect early warning signs of equipment wear or potential failure using sensor data and machine learning, allowing for proactive scheduling of maintenance and preventing costly unplanned downtime.

Our Tech Stack

We use modern ML tools and infrastructure that scale with your needs:

ML Frameworks

We use advanced frameworks to develop models that deliver real business value. Whether your goal is to forecast demand or improve customer insights, we match the right approach to your data and priorities.

pandas

XGBoost

scikit-learn

PyTorch

TensorFlow

Cloud Platforms

Our cloud-based deployments give you the flexibility to process large volumes of data and adapt to shifting business needs. Models can scale smoothly as your operations grow.

Azure ML

Amazon SageMaker

Google Cloud

MLOps Tools

We manage your machine learning systems across their entire lifecycle — from development through production. This helps keep models accurate and aligned with your business, even as your data changes over time.

Databricks

Weights & Biases

MLflow

Why Choose nomtek?

Experience in product, data science, and full-stack engineering

Our team combines expertise across AI, data science, UX/UI, mobile, and full-stack development — which means you won’t be left managing gaps between strategy, design, and engineering. We help you move from idea to deployment without risking misalignment or stalled initiatives.

ISO 27001 certified: security and governance you can trust

We operate under rigorous information security standards, so your sensitive data, proprietary models, and business logic are always protected. Our processes meet enterprise-grade compliance needs, removing one of the most common barriers to adopting AI in production environments.

Flexible engagement: from advisory to execution

Whether you’re starting with exploratory prototypes or scaling AI across critical systems, we adapt to your needs. You can engage us for expert advisory, targeted development support, or fully managed delivery. Your team gains exactly the right level of support at each stage, without being locked into a rigid or oversized engagement.

Turn Your Data Into a Smart Asset

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Frequently Asked Questions

Dedicated team

Unpack your idea with dedicated feasibility agents. A team of three experts will work on all angles to check technical feasibility and prepare an initial roadmap. Each iteration lasts one week.

High transparency

The whole process is highly transparent — you’re in the loop at every moment. If the first week reveals additional areas for exploration, we continue iterating. This way we generate enough valuable data to speed up your decision-making.

Rich deliverables

Deliverables — Every tech feasibility sprint ends with a report, roadmap, or prototype, depending on the duration of the workshop and our findings.

Custom Process

Throughout the workshop, we integrate custom processes crafted using collected insights and internal expertise. A tailored approach is optimized for confident execution in the later stages.

How does predictive analytics improve supply chain resilience?

Predictive analytics gives supply chain teams the ability to anticipate disruptions, manage risks, and optimize resource allocation. By analyzing historical data and real-time inputs, companies in manufacturing, logistics, and financial services can make faster, more informed decisions that improve supply chain stability and efficiency.

How can artificial intelligence support data-driven decisions in marketing campaigns?

Artificial intelligence helps marketing teams run more effective marketing campaigns by unlocking access to data-driven decisions. Techniques such as sentiment analysis, customer segmentation, and predictive analytics provide improved accuracy in targeting and personalization, leading to better campaign performance.

What are the common challenges in deploying machine learning solutions at scale?

Common challenges include managing large volumes of data points, maintaining model performance over time, and integrating predictive models with existing business operations. Organizations must also address regulatory compliance and ensure that machine learning systems remain interpretable and trustworthy.

How does predictive maintenance reduce operational costs?

Predictive maintenance uses statistical techniques and support vector machines to forecast equipment failures before they happen. This allows companies to schedule repairs proactively, minimizing unplanned downtime and reducing maintenance costs.

What are the key steps to move a machine learning model from prototype to production?

The steps include refining the model with real-world data, automating retraining processes, setting up monitoring for model drift, and integrating with business operations. Moving machine learning models to production requires collaboration between data scientists and engineering teams to maintain accuracy and reliability.

How can machine learning improve demand forecasting and inventory management?

Machine learning improves demand forecasting and inventory management by detecting nonlinear relationships in historical data. By using regression analysis and classification models, businesses can better predict customer demand and reduce stockouts or overstocks.

What business processes can benefit the most from predictive analytics?

Business processes such as inventory management, dynamic pricing, marketing strategies, and customer segmentation gain value when companies use predictive analytics. Predictive models typically uncover hidden patterns in data, helping companies make faster, more accurate decisions.

Financial Services
- Financial services firms apply machine learning algorithms to fraud detection, credit scoring, and customer behavior analysis. Predictive analysis of transactional data supports better risk management and targeted marketing strategies.
Telecom
- Telecom companies use predictive models to improve customer segmentation, reduce churn, and optimize network operations. Predictive analytics work supports the delivery of better services and increased customer retention.
Asset Management
- In asset management, predictive analytics helps forecast market trends and detect anomalies in investment portfolios. This enables companies to gain insights into potential risks and optimize asset allocation.
HR / Human Resources
- HR departments use predictive analytics to forecast workforce needs and improve hiring decisions. By applying regression models to employee data, companies can predict future behavior such as turnover risk.
Healthcare and Medicine
- Healthcare providers use advanced analytics, neural networks, and natural language processing to predict future events such as disease outbreaks or patient deterioration. This leads to better resource planning and patient care.
Fleet Management / Transportation
- Fleet management uses anomaly detection and statistical modeling to predict maintenance needs and optimize routing. This improves operational efficiency and reduces downtime.
Construction
- In construction, predictive analytics helps forecast project timelines, material needs, and equipment failures. Machine learning models let companies manage risks and control costs more effectively.
Insurance
- Insurance firms use machine learning to model complex relationships in customer data and assess risk more accurately. Fraud detection and dynamic pricing are key applications of predictive analytics in this industry.
Supply Chain / Logistics
- Supply chains rely on predictive models to anticipate disruptions, improve inventory management, and streamline logistics. This enables companies to maintain service levels while reducing costs.
Manufacturing
- Manufacturers apply machine learning to predictive maintenance and quality control. Statistical modeling of sensor data helps predict equipment failures, improving efficiency.
Retail
- Retailers use predictive analytics to forecast customer demand, personalize offers, and optimize pricing strategies. Machine learning for business enables better inventory planning and customer experience.
Education
- Educational institutions use learning algorithms and data analytics to predict student performance and personalize learning pathways. Predictive analytics supports better educational outcomes.
Ecommerce
- Ecommerce companies apply predictive analytics to customer behavior analysis, recommendation engines, and fraud detection. Analyzing data points helps marketing teams improve conversion rates and retention.

How to build a predictive model using machine learning?

To build a predictive model using machine learning, you first collect and clean historical data. Next, data scientists select features and apply machine learning algorithms such as regression analysis or neural networks. After training, the model is validated and refined. Deployment allows the model to predict future behavior in real-world settings.

What are the 4 types of business analytics?

The 4 types of business analytics are descriptive, diagnostic, predictive analytics, and prescriptive analytics. Predictive analytics work focuses on using historical data and statistical techniques to predict future outcomes.

What is the 10X rule in machine learning?

The 10X rule in machine learning suggests that small changes in model accuracy can drive a 10X increase in business impact. For example, a slight improvement in fraud detection accuracy can greatly reduce financial losses, helping marketing teams or operations teams achieve significant gains.

What are the 4 types of machine learning?

The 4 types of machine learning are supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.

Supervised learning uses labeled data to train classification models and regression models.
Unsupervised learning identifies patterns with hierarchical clustering and other techniques.
Semi-supervised learning combines both.
Reinforcement learning learns through rewards and penalties.

How is machine learning used in business?

Machine learning for business is applied to find patterns to improve efficiency in a wide array of business areas and automate decisions, refine forecasting, optimize operations. Companies use machine learning models to analyze data and predict future trends, supporting improved decision making across sales, supply chain, customer service, and risk management.