New · We are an implementation partner for AgenticObjects: analytics agents that work on top of your data warehouse, starting with a 4-week pilot. See how the pilot works
Contact us
An analyst reviewing financial data on multiple screens
ENTERPRISE SOLUTIONS

BI-Driven AI: AI built on your business intelligence

A solid business intelligence (BI) foundation, industry-specific data engineering and production-ready AI models: diagnostic, predictive and prescriptive.

Tell us about your projectSee the method
D-CAT AI METHOD

D-CAT AI Method: a disciplined seven-stage approach

AI projects start with data, not with the model. Each stage builds on the one before it, and no stage is skipped.

  1. 01

    Data discovery

    An inventory of data sources, a map of existing systems and a data quality baseline: which data sits where, how it flows and how good it is.

  2. 02

    Stakeholder workshop

    Setting goals with industry experts, sharpening the business questions and agreeing on success criteria: which question are we trying to answer?

  3. 03

    Data quality and preparation

    Cleansing, standardization, and handling of missing values and outliers. If the data is wrong, the model is wrong, which makes this stage critical.

  4. 04

    Feature engineering

    Internal features (industry business data) and external features (weather, match days, religious holidays, public holidays, school calendars), enriched with industry knowledge.

  5. 05

    Model selection and training

    The right model for the problem: LightGBM, XGBoost, time series models or deep learning. We start with a baseline, improve step by step and tune hyperparameters.

  6. 06

    Pipeline design and orchestration

    A production-ready AI pipeline architecture: data feeds, batch or real-time processing, observability, reproducibility and MLOps best practices.

  7. 07

    Deployment and monitoring

    Moving to production, model versioning, drift detection and performance monitoring. Then the cycle starts again: data changes, the model is updated. Go-live is the beginning, not the end.

The BI-Driven AI manifesto

THREE APPROACHES

The three approaches of BI-Driven AI: diagnostic, predictive, prescriptive

Beyond “what happened”: models that analyze possible drivers, forecast what comes next and compare the options.

An engineer reviewing data on a tablet on the production line
Diagnostic

Diagnostic AI

Analyzes possible drivers

Models that go beyond reports showing only what happened and analyze the possible drivers behind a deviation. They detect anomalies in the data set, surface statistically significant signals and automate hypothesis testing.

Technology: Python, pandas, NumPy, SciPy, Isolation Forest, DBSCAN, Chi-square, ANOVA, Bootstrap, Prophet, STL.

  • Retail: detecting deviations by category, store or campaign when sales drop, and analyzing possible drivers.
  • Promotions: analyzing possible drivers behind unexpected campaign results.
  • Healthcare: operational anomaly detection, including deviations in bed occupancy, waiting times and resource use.
  • Enterprise: automated reporting of possible drivers behind KPI deviations, with alerts built into dashboards.
Supermarket shelves
Predictive

Predictive AI

What could happen?

Forecasts of trends, demand, risk and behavior. With feature engineering drawn from industry data, models are built around the real dynamics of your business.

Technology: LightGBM, XGBoost, Prophet, statsmodels, scikit-learn, PyTorch, MLflow, Darts.

  • Retail: demand forecasting by store and SKU, with inventory optimization recommendations.
  • Promotions: ROI and impact forecasts before a campaign starts.
  • Healthcare: projections of patient volume and bed occupancy.
  • Enterprise: forward-looking forecasts of operational KPIs, with deviation alerts.
A productized example: Tracta Promotion Intelligence
A presentation in a management meeting
Prescriptive

Prescriptive AI

Which option should be considered?

Models that provide decision support on what should be done. They evaluate options with optimization solvers, compare scenarios through what-if simulation and build decision-support flows with AI agents. The decision stays with people.

Technology: Google OR-Tools, PuLP, CVXPY, SimPy, reinforcement learning, agent orchestration.

  • Retail: inventory allocation optimization: which product, to which store, in what quantity.
  • Promotions: promotion mix optimization: which set of discounts could work better in which category.
  • Healthcare: recommendation systems for operational resources.
  • Enterprise: scenario comparison with what-if simulations, and decision support with AI agents.
WHY D-CAT

Why D-CAT

Build your AI project with a business intelligence team that already knows your data.

20 years of BI and industry depth

A team that knows how enterprise data is structured in finance, healthcare, retail, manufacturing and insurance. Industry-specific features are enriched with external data (weather, holidays, match days).

On top of your existing data warehouse

331+ enterprise customers, 9 industries, 1,000+ projects. The data we know from the BI layer carries over to the AI layer without starting from scratch.

On-premises or in the cloud

Depending on the deployment model, it runs on-premises or on cloud platforms such as Azure ML and AWS SageMaker.

Disciplined method, production-ready MLOps

Projects that don't get stuck in the pilot: they go live with model versioning, drift monitoring, rollback and CI/CD.

TECHNOLOGY

The technologies we use

Mostly open-source tools, tested in production and free of vendor lock-in.

Tools used in BI-Driven AI projects
LayerTools
Data processingPython, pandas, Polars, Spark
ML frameworksscikit-learn, LightGBM, XGBoost, PyTorch
Time seriesProphet, statsmodels, Darts
OptimizationGoogle OR-Tools, PuLP, CVXPY
MLOpsMLflow, DVC, Airflow, Kubeflow
DeploymentDocker, Kubernetes, Azure ML, AWS SageMaker
MonitoringEvidently AI, Great Expectations
STARTING POINT

Which analytics layer is your organization in?

What happened, why it happened, what could happen, which option to choose: before starting an AI project, see where your data foundation stands with a 2-week Health Check.

DiagnosticPredictivePrescriptive
Request a Health CheckAnalytics maturity guide

Frequently asked questions

Short answers to the questions we hear most often on this topic.

What kinds of projects does BI-Driven AI cover?

Diagnostic, predictive and prescriptive AI projects in retail, promotions, healthcare and enterprise use cases.

Is our existing business intelligence infrastructure used?

Yes. The AI layer is built on your existing data warehouse and the data we know from the BI layer; the data carries over to the AI layer without starting from scratch.

Which tools do you work with?

A mostly open-source technology stack: Python, pandas, scikit-learn, LightGBM, XGBoost, PyTorch, Prophet, Google OR-Tools, MLflow, Airflow, Docker and Kubernetes, plus Azure ML and AWS SageMaker in the cloud.

What happens after the model goes live?

The model is versioned, and drift and performance are monitored. As the data changes, the cycle starts again and the model is updated.

Tell us about your project

Do you have a diagnostic, predictive or prescriptive AI project specific to your industry? Let's look at your data structure together and discuss the best approach.

Tell us about your projectinfo@dcat.com.tr