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MACHINE LEARNING · PREDICTIVE AI · MLOPS

Predict. Classify. Optimise.

DotStark builds custom machine learning models that turn your data into business decisions — predictive analytics, classification engines, NLP pipelines, and production MLOps infrastructure that actually scales.

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✓ Microsoft Partner ✓ Azure ML Certified ✓ Production ML Delivery
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ML Models in Production
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Avg Model Accuracy
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Countries Served
Azure ML
Primary Platform
ML CAPABILITIES

Machine Learning That Solves Real Business Problems

We build ML models around your data and business goals — not generic benchmarks.

Predictive Analytics
Forecast customer churn, sales demand, equipment failure, and financial risk — models trained on your historical data and deployed into your existing workflows.
Churn predictionDemand forecastingRisk scoring
Classification & Categorisation
Automatically classify documents, support tickets, products, and content into categories — at scale, with accuracy that beats manual review.
Document classificationNLPMulti-class
Computer Vision
Detect, classify, and extract information from images and video — quality control, document OCR, medical imaging, and visual inspection at enterprise scale.
Object detectionOCRImage classification
Natural Language Processing
Extract meaning from unstructured text — sentiment analysis, named entity recognition, document summarisation, and intent classification across your content.
Sentiment analysisNERText classification
Recommendation Engines
Personalised product, content, and action recommendations — collaborative filtering, content-based, and hybrid approaches deployed on Azure ML.
Collaborative filteringPersonalisationAzure ML
MLOps & Model Deployment
Build the infrastructure that keeps ML models healthy in production — monitoring pipelines, drift detection, automated retraining, and CI/CD for ML.
Azure MLOpsModel monitoringAuto-retrain
INDUSTRY USE CASES

ML in the Real World

Industry-specific ML applications DotStark has delivered for enterprise clients.

CREDIT RISK MODEL
728
Credit Score
Payment History94%
Credit Utilisation42%
Account Age7yr
Credit Risk ScoringReal-time ML scoring of loan applications — 3× faster than manual review with 40% reduction in defaults
Fraud DetectionAnomaly detection on transaction streams — identifying fraudulent patterns with sub-100ms latency
Customer Churn Prediction90-day churn prediction with 91% accuracy — enabling proactive retention campaigns before customers leave
Algorithmic Portfolio OptimisationML-driven portfolio rebalancing aligned to risk profiles, market signals, and regulatory constraints
PATIENT RISK CLASSIFIER
Patient AHIGH RISK94%
Patient BMEDIUM67%
Patient CLOW RISK12%
Readmission Risk Prediction30-day readmission risk scoring — enabling care teams to prioritise at-risk patients before discharge
Medical Image AnalysisComputer vision models for radiology image screening — flagging anomalies for specialist review
Clinical NLPExtract structured data from unstructured clinical notes — diagnosis codes, medications, and treatment plans automatically
Drug Interaction DetectionML models that identify potential drug interaction risks across complex patient medication profiles
EQUIPMENT HEALTH MONITOR
Motor Vibration
Normal
Temperature
Elevated
Bearing Wear
Replace
⚠ Failure predicted in 8 days
Predictive MaintenancePredict equipment failure 7–14 days ahead using sensor telemetry — reducing unplanned downtime by up to 60%
Quality Control VisionComputer vision inspection at production line speed — detecting defects humans miss at 2000 units/hour
Demand ForecastingSupply chain demand prediction aligned to seasonal patterns, promotions, and external signals
Energy OptimisationML models that reduce energy consumption across manufacturing facilities — typically 15–25% reduction
RECOMMENDATION ENGINE
JD
Jordan D. High Value
Running Shoes94%
Sport Socks87%
Water Bottle79%
Personalised RecommendationsReal-time product recommendations based on behaviour, purchase history, and segment — average 23% uplift in basket value
Churn PredictionIdentify customers at risk of lapsing 60 days before churn — enabling targeted retention offers with 4× better ROI than blanket campaigns
Price OptimisationDynamic pricing ML aligned to demand signals, competitor pricing, and margin targets — real-time pricing decisions at SKU level
Inventory ForecastingSKU-level demand prediction that reduces overstock by 30% and stockouts by 45% across retail networks
HOW WE WORK

From raw data to production model.

A structured ML engagement that de-risks every stage — from data audit to live model in your infrastructure.

01Wk 1–2
Data Audit
Assess data quality, volume, and readiness. Identify gaps before model work begins.
02Wk 2–4
Feature Engineering
Transform raw data into ML-ready features. The most important step most teams skip.
03Wk 4–8
Model Development
Train, evaluate, and compare models. Baseline first, then iterate toward target accuracy.
04Wk 8–10
Validation & Testing
Rigorous validation against held-out data, business scenarios, and edge cases.
05Wk 10–12
Deploy & Monitor
Production deployment on Azure ML, monitoring pipeline, drift detection, and retraining triggers.

Engineering-led. Data-honest.

We Start With Data, Not Models

Most ML projects fail because of data quality, not algorithms. DotStark always begins with a data audit — if your data can't support an ML model, we tell you before you invest, not after.

Azure ML — Enterprise Infrastructure

As a Microsoft Partner, DotStark deploys ML models on Azure ML — scalable, secure, monitored, and integrated with your existing Microsoft data and cloud infrastructure.

Models Built to Last in Production

We don't hand over Jupyter notebooks. Every DotStark ML model includes monitoring pipelines, drift detection, retraining triggers, and documentation — engineered for production from day one.

FAQS

Questions, answered.

Machine learning is a branch of AI where models learn patterns from data to make predictions or decisions without being explicitly programmed. Your business should invest in ML when you have a repeated decision that relies on patterns in data — such as predicting customer behaviour, classifying documents, detecting anomalies, or optimising operations. DotStark's scoping session identifies whether your use case and data are genuinely ML-ready before any investment begins.

The required data volume depends entirely on the problem type and complexity. A binary classification model (churn prediction, fraud detection) can work well with 10,000–50,000 labelled examples. Computer vision models typically need 1,000+ images per class. NLP models can leverage transfer learning to work with smaller datasets. DotStark's data audit — included in every engagement — assesses your data volume and quality before committing to a model approach.

A focused ML model — from data audit to production deployment — typically takes 10–14 weeks for a mid-complexity use case. Simple predictive models can be delivered in 6–8 weeks. Complex computer vision or multi-model systems may take 16–24 weeks. DotStark provides a realistic timeline after the scoping session, not before we understand your data.

Production ML models degrade over time as real-world data drifts from training data. DotStark includes monitoring pipelines, drift detection, and retraining triggers as standard in every production deployment. We also offer ongoing MLOps support retainers to keep models accurate, monitored, and aligned with evolving business requirements.

Ready to build your first ML model?

Book a free 30-minute scoping session. We'll assess your data, identify the right ML approach, and give you an honest view of what's achievable — before any commitment.

Book Free ML Scoping Session → Talk to Our Team →
✓ Free scoping session✓ Azure ML certified✓ Data-first approach
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