Applied AI
Research in artificial intelligence, from custom models and fine-tuning to machine learning applications and intelligent automation
Our AI Research Areas
Custom Model Development
AI models built from the ground up for a specific kind of data and use case. From neural networks to ensemble methods, we build models that deliver measurable results, including the vision models behind ARGUS.
Model Fine-Tuning
Adapting state-of-the-art foundation models to specific domains. We fine-tune LLMs, vision transformers, and other pre-trained models to achieve superior performance on specialised tasks.
ML Applications
End-to-end machine learning applications that solve real-world problems. From predictive analytics to recommendation systems, we study how to build production-ready ML that scales.
Intelligent Automation
Automating complex processes with AI. From document processing to decision automation, we research how intelligent automation brings efficiency gains and cost savings.
Custom Model Development Workflow
01
Requirements Analysis
Define the problem, the data landscape, and the outcomes a model must reach
02
Data Preparation
Clean, transform, and engineer features from raw data to get the best model performance
03
Model Development
Design and train custom AI models for a specific use case and its constraints
04
Evaluation & Tuning
Rigorously test model performance and fine-tune hyperparameters for optimal results
05
Deployment
Deploy models to production with monitoring, versioning, and continuous improvement
Model Fine-Tuning Workflow
01
Base Model Selection
Choose the right foundation model for the task, domain, and performance requirements
02
Dataset Curation
Collect, clean, and label high-quality training data for the target domain
03
Fine-Tuning
Adapt pre-trained models to a specific use case with transfer learning techniques
04
Validation
Comprehensive testing against domain-specific benchmarks and real-world scenarios
05
Integration
Integrate fine-tuned models into real software and workflows
ML Application Development Workflow
01
Use Case Discovery
Identify problems where machine learning has the highest impact
02
Proof of Concept
Rapid prototyping to validate feasibility and real-world value
03
Application Development
Build production-grade ML applications with robust architecture and scalability
04
Testing & QA
Ensure reliability, accuracy, and performance through comprehensive testing
05
Production Deployment
Launch ML applications with monitoring, auto-scaling, and continuous updates
Intelligent Automation Workflow
01
Process Analysis
Map current workflows and identify automation opportunities with highest ROI
02
AI Solution Design
Design intelligent automation systems combining ML, NLP, and computer vision
03
Implementation
Build and integrate AI-powered automation into real infrastructure
04
Optimization
Continuously improve automation efficiency and adapt to changing requirements
05
Scaling
Expand automation across departments with governance and best practices
From Research to Product
ARGUS, our AI video search software for CCTV, is built on this research
Explore ARGUS