Case studies
Representative applied data-science engagements produced with the Unified Data Science Pipeline Platform. Public datasets are labelled.
Generic / Auto-detect (any tabular dataset)
Generic / Auto-detect (any tabular dataset): regression on 'target' - best Ridge, r2=0.896.
r2: 0.8956
Forecasting: Demand & Energy Time-Series
Forecasting: Demand & Energy Time-Series: regression on 'demand_kw' - best ExtraTrees, r2=0.932.
r2: 0.9318
Industrial AI: APM & Reliability (Remaining Useful Life)
Industrial AI: APM & Reliability (Remaining Useful Life): regression on 'rul_hours' - best ExtraTrees, r2=0.921.
r2: 0.9207
Renewable Energy: Battery Energy Storage (BESS) Health
Renewable Energy: Battery Energy Storage (BESS) Health: regression on 'soh_percentage' - best Ridge, r2=1.0.
r2: 1.0
ISM + Mechatronics: Industrial Robotic Arm Kinematics
ISM + Mechatronics: Industrial Robotic Arm Kinematics: classification on 'calibration_drift' - best LogisticRegression, accuracy=0.852.
accuracy: 0.8515
Renewable Energy: Smart Grid Load & Solar/Wind Forecasting
Renewable Energy: Smart Grid Load & Solar/Wind Forecasting: classification on 'curtailment_risk' - best LightGBM, accuracy=1.0.
accuracy: 1.0
Mechanical Engineering: Predictive Maintenance & Vibration
Mechanical Engineering: Predictive Maintenance & Vibration: classification on 'failure_type' - best LightGBM, accuracy=0.992.
accuracy: 0.9924
ISM + Security: AI Governance & Data Provenance
ISM + Security: AI Governance & Data Provenance: classification on 'is_compliant' - best LogisticRegression, accuracy=1.0.
accuracy: 1.0
Mechanical Engineering: Patent Novelty Graph (NLP)
Mechanical Engineering: Patent Novelty Graph (NLP): regression on 'novelty_index' - best Ridge, r2=0.692.
r2: 0.6922
Robotics + ISM: Autonomous Vehicle Liability
Robotics + ISM: Autonomous Vehicle Liability: classification on 'liability_class' - best XGBoost, accuracy=0.998.
accuracy: 0.9976
AI Usage and Impact
Generic / Auto-detect (any tabular dataset): classification on 'Would_Recommend' - best XGBoost, accuracy=0.61.
accuracy: 0.6102
Spaceship Titanic
Generic / Auto-detect (any tabular dataset): classification on 'Transported' - best LightGBM, accuracy=0.789.
accuracy: 0.789
NYC Yellow Taxi - Fare Prediction
Generic / Auto-detect (any tabular dataset): regression on 'fare_amount' - best LightGBM, r2=0.816.
r2: 0.8164
Engagements
Industrial AI & Data Opportunity Assessment
Identify, prioritise and quantify the 3-5 AI/analytics use cases most likely to create measurable operational value - with a data-readiness check and a 90-day roadmap.
Predictive Operations & Asset Analytics Pilot
Assess your operational/sensor history and build a validated predictive analytics or machine-learning proof of concept (maintenance, reliability, production, quality, forecasting).
Fractional Industrial AI/Data Lead
Senior AI/data leadership 1-2 days per week: AI strategy, data architecture, ML delivery, LLM/RAG applications, MLOps and executive reporting - without a full-time executive hire.
Capabilities
Start a project - discovery enquiry
Tell me about the opportunity. Your entries are saved in this browser, so you can come back and finish later without retyping them.