Data Discovery
Inventory of data sources, mapping of existing systems, data quality baseline. What data sits where, how does it flow, what is its quality?
D-CAT AI METHOD · BI-DRIVEN AI ENGINEERING
20 Years of BI Data.
20 Years of BI Data.
AI Engineering Tailored to Your Industry.
The depth of a rock-solid BI foundation + industry-specific data engineering + production-ready AI models. Diagnostic · Predictive · Prescriptive.
01Data Discovery
D-CAT AI Method7 Disciplines. 1 Loop. AI tailored to your industry.
AI projects don't start with models. They start with data. Our team — reading the DNA of enterprise data for 20 years — runs AI projects through 7 disciplined stages. Each stage builds on the last; if the data is wrong, the model is nothing.
Stakeholder Workshop
Goal-setting with domain experts, sharpening business questions, success criteria. “What question are we actually answering?”
Data Quality & Preparation
Data cleansing, standardisation, missing values, outlier treatment. If the data is wrong, the model is nothing — this stage is critical.
Feature Engineering
Internal features (industry business data) + external features (weather, match days, religious holidays, public holidays, school calendars). Enriched with domain knowledge.
Model Selection & Training
The right model for the problem — LightGBM, XGBoost, time series, deep learning. Baseline → iterative improvement. Hyperparameter tuning.
Pipeline Design & Orchestration
Production-ready AI pipeline architecture. Data flows, batch vs real-time, observability, reproducibility. MLOps best practices.
Deployment & Monitoring
Move to production, model versioning, drift detection, performance monitoring. Then back to 01 — the model is updated, the data shifts, the cycle repeats.
From 07, back to 01. AI projects don't end — they evolve. For D-CAT, model deployment is the beginning, not the end.
No phase is skipped. No phase is rushed. This 7-discipline process is the backbone of our AI projects.
D-CAT AI · Diagnostic
Diagnostic AIRoot-Cause Finders
Not reports that tell you “what happened” — models that uncover “why it happened.” They detect anomalies, surface statistically significant signals, and automate hypothesis tests.
Tech Stack
PythonpandasNumPySciPyIsolation ForestDBSCANChi-squareANOVABootstrapProphetSTL
Typical Use Cases
- RetailSales-decline root-cause analysis — variance detection by category, store, or campaign.
- PromotionRoot-cause analysis of unexpected campaign results.
- HealthcareOperational anomaly detection — bed occupancy, wait time, resource-utilisation deviations.
- EnterpriseAutomated root-cause reporting for KPI deviations, dashboard-integrated alerting systems.
D-CAT AI · Predictive
Predictive AIForecasters of the Future
Trend, demand, risk and behaviour forecasts. With 20 years of industry data distilled into feature engineering, we reach accuracy competitors can't touch.
Tech Stack
LightGBMXGBoostProphetstatsmodelsscikit-learnPyTorchMLflowDarts
Typical Use Cases
- RetailStore- and SKU-level demand forecasting, stock-optimisation recommendations.
- PromotionPre-campaign ROI and impact forecasting.
- HealthcarePatient-density and bed-occupancy projection.
- EnterpriseForward-looking forecasting of operational KPIs, deviation alerts.
D-CAT AI · Prescriptive
Prescriptive AIDecision Engines
Engines that answer “what should we do?” With optimisation solvers they recommend the best action; with what-if simulation they compare scenarios — agentic decision engines.
Tech Stack
Google OR-ToolsPuLPCVXPYSimPyReinforcement LearningAgentic Orchestration
Typical Use Cases
- RetailStock-distribution optimisation — which product, to which store, in what quantity.
- PromotionPromotion-mix optimisation — which discount set delivers the best result for which category.
- HealthcareOperational resource recommendation systems.
- EnterpriseScenario comparison via what-if simulation, agentic decision support.
Why D-CAT?
Not what every ML firm says — our concrete difference.
20 Years of Industry Depth
A team that knows every industry's data DNA. Finance, health, retail, manufacturing, insurance — not generic, sector-specific.
334+ Enterprises · 9 Sectors · 1,000+ Projects
The data we already know from the BI layer — when we move it to AI, we grasp in a week what competitors take 6 months to learn.
Sector-Specific Feature Engineering
Competitors don't have it. Per-industry custom features — that's where accuracy gains come from. Enriched with external features (weather, holidays, match days).
Production-Ready MLOps
Projects that don't die in pilot. Model versioning, drift monitoring, rollback, CI/CD — they go live.
Technology Stack
Open-source first, free of vendor lock-in, production-tested — tools that have earned their place over years.
| Data Processing | PythonpandasPolarsSpark |
| ML Frameworks | scikit-learnLightGBMXGBoostPyTorch |
| Time Series | ProphetstatsmodelsDarts |
| Optimisation | Google OR-ToolsPuLPCVXPY |
| MLOps | MLflowDVCAirflowKubeflow |
| Deployment | DockerKubernetesAzure MLAWS SageMaker |
| Monitoring | Evidently AIGreat Expectations |
Tell Us About Your Project.
See What 20 Years of Experience Can Do.
Got a Diagnostic, Predictive or Prescriptive AI project specific to your industry? Book a call — we'll look at your data, and tell you where the fastest path to value lies.