From Raw Data to Real-Time Decisions
You have data. Maybe lots of it. What you need is intelligence—systems that extract, process, learn, and act. I build ML pipelines and automated decision engines, using AI to accelerate everything from data extraction to model deployment.
The same approach that handles 100GB+ datasets and processes millions of events daily.
The Data Challenge
Most organisations are data-rich and insight-poor. The data exists, but turning it into automated intelligence feels impossibly complex.
Data trapped in silos
Insights hidden across formats, APIs, and legacy systems
Analysis too slow for decisions
By the time you have insights, the opportunity has passed
ML feels out of reach
Data science teams are expensive and slow to hire
Manual processes that don't scale
Humans doing work that machines should handle
No real-time capability
Batch processing when you need streaming intelligence
End-to-End Capabilities
From raw data ingestion to production ML systems. Every step of the pipeline, designed to work together.
Data Extraction & Integration
Pull data from any source—APIs, streams, databases, files. Transform and load into analytical databases. AI-accelerated schema inference and mapping.
Feature Engineering
Transform raw data into ML-ready features. Time-series processing, rolling windows, normalisation. The difference between models that work and models that don't.
ML Model Development
Build and train models for your specific problem. LSTM for sequences, XGBoost for tabular data, custom architectures where needed. Validated on your data.
Real-Time Inference
Deploy models that respond in milliseconds. Streaming predictions, not batch jobs. Production-grade with monitoring and fallbacks.
Automated Decision Systems
Turn predictions into actions. Rule engines, threshold management, human-in-the-loop where it matters. Systems that execute while you sleep.
Monitoring & Observability
Know what your system is doing. Performance dashboards, alerting, drift detection. Confidence that your models still work.
ML Model Expertise
The right model for your problem. Not everything needs deep learning—sometimes XGBoost wins. I match architecture to requirements.
LSTM / Sequence Models
Time-series prediction, pattern recognition in sequential data
Example: Predict next values based on historical sequences
XGBoost / Gradient Boosting
Classification, regression, feature importance analysis
Example: Risk scoring, churn prediction, anomaly detection
Custom Architectures
Domain-specific problems that don't fit standard models
Example: Attention mechanisms, ensemble methods, hybrid approaches
Built for Scale
These aren't theoretical capabilities. These are production metrics from live systems.
100GB+
<50ms
<10ms
1M+
AI-Accelerated Development
I use AI assistants to accelerate every stage—not replacing expertise, but multiplying it. What used to take weeks now takes days.
Database Schema Design
Days of analysis and iteration
AI analyses data, proposes schema, iterates in hours
Feature Engineering
Manual hypothesis testing, slow iteration
AI suggests features, validates statistically, refines automatically
API Integration
Read docs, write client code, handle edge cases
AI generates client from docs, handles authentication, error handling
Data Validation
Manual sampling, spreadsheet analysis
AI scans entire dataset, identifies anomalies, suggests fixes
Technical Stack
Ready to Turn Data into Intelligence?
Whether you're starting with raw data or have existing pipelines that need ML integration, let's talk about what's possible.