Frame the business problem
Clarify the decision, workflow or engineering outcome the system must improve before selecting models or architecture.
Grounded in evidence
and value
AI, Data & Intelligent Systems
Turn data, documents and operational information into actionable intelligence.
We design AI, machine-learning and data-driven systems that solve real business and engineering problems. Our approach combines structured data, deterministic software, analytics and selective AI so intelligence remains explainable, controlled and connected to measurable outcomes.
- Generative AI
- Machine Learning
- Data Engineering
- Analytics & Insights
- Computer Vision
- Intelligent Document Processing
- RAG & Knowledge Systems
- Structured AI Workflows
- Human-in-the-Loop
- AI Governance
Our approach
Intelligence Starts With the Problem — Not the Model
Successful AI systems begin with the business question, available evidence and required level of control. We structure the data first, use deterministic logic where possible and introduce AI only where it creates measurable value.
Structure data and evidence
Bring together the required documents, operational data and knowledge sources so intelligence is grounded in reliable information.
Apply the right intelligence
Use analytics, ML, computer vision, GenAI or deterministic rules according to the problem rather than forcing every use case through an LLM.
Validate before scale
Measure quality, confidence, business value and risk, retain human oversight where needed and scale only when the system proves useful.
Intelligence applied to real problems
Selected Case Studies
Explore governed AI, machine learning, research intelligence, market analytics and computer-vision systems.
Governed GenAI Script Conversion Agent
Business Problem
Engineering migration workflows contained custom scripts that required interpretation and conversion, while predictable standard cases did not require AI.
Solution / Approach
Designed and deployed a production GenAI workflow in which deterministic classification separates standard, ignored and genuinely custom cases. Only custom cases are routed to the AI agent, with structured JSON processing, controlled orchestration and downstream merging of results. The solution was reviewed and approved through enterprise AI governance before production deployment.
Outcome
Introduced GenAI into a production engineering workflow without making AI responsible for deterministic cases, reducing unnecessary model usage while preserving controlled processing.
Technology / Capability
- GenAI
- Copilot Agent
- Power Automate
- .NET
- Structured Outputs
- Workflow Orchestration
- Human Governance
Machine Fault Intelligence Using Machine Learning
Business Problem
Large volumes of historical machine data needed to be analyzed to identify patterns associated with equipment faults.
Solution / Approach
Processed approximately 3–4 TB of historical machine data and developed ML-based fault-identification models using structured data preparation, feature analysis and model evaluation.
Outcome
Demonstrated approximately 78–79% fault-identification accuracy during the proof-of-concept work.
Technology / Capability
- Machine Learning
- Data Engineering
- Industrial Analytics
- Predictive Intelligence
- Large-Scale Data Processing
Medical Research Intelligence Platform POC
Business Problem
Research teams needed structured intelligence across publicly available scientific and corporate information covering multiple global organizations.
Solution / Approach
Designed a research-intelligence proof of concept using LLM-assisted extraction and a structured data-platform approach to organize information across approximately 10 global companies.
Outcome
Demonstrated how fragmented public research information could be transformed into structured, comparable intelligence for further analysis.
Technology / Capability
- LLM
- Information Extraction
- Data Architecture
- Research Intelligence
- Structured Data
Diagnostic Competitive Pricing Intelligence
Business Problem
A new diagnostic offering required market and competitor pricing intelligence to support launch decisions.
Solution / Approach
Collected pricing information across 16 diagnostic centres within approximately a 10 km radius and combined web scraping, image processing and analytical/regression techniques to structure and compare market data.
Outcome
Provided actionable competitive pricing intelligence to support launch-price decisions.
Technology / Capability
- Web Scraping
- Image Processing
- Analytics
- Regression
- Competitive Intelligence
Computer Vision for Automated Engineering Validation
Business Problem
Engineering teams needed an automated way to compare legacy and converted graphics despite differences in colour, scale and image boundaries.
Solution / Approach
Developed an automated comparison pipeline using .NET and OpenCV with colour neutralization, scale normalization, black/white transformation, cropping and visual-difference analysis.
Outcome
Enabled repeatable automated visual comparison and reduced reliance on purely manual engineering inspection.
Technology / Capability
- Computer Vision
- OpenCV
- .NET
- Image Processing
- Automated Validation
Start with the business value
Where Could Intelligence Create Real Business Value?
Start with the business decision, workflow or information problem. We can help determine whether analytics, machine learning, computer vision, GenAI or a deterministic solution is the right fit.