Data and AI
Prepare data. Generate insights. Operate AI.
Move and analyze business data, develop AI applications, and establish the practices needed to evaluate and operate models.
Use reporting, forecasting, and analytical models to understand performance and inform business decisions.
- Business intelligence and customer analytics
- Predictive and prescriptive analytics
- Text analysis and applied machine learning
Where to startFragmented reporting, uncertain forecasts, or decisions that need better evidence.
Explore data analytics↗
Transfer and validate data as you replace legacy systems, consolidate databases, or move to the cloud.
- Source assessment and data mapping
- Controlled transfer and reconciliation
- Cutover planning and transition support
Where to startA platform replacement, database consolidation, or cloud transition.
Explore data migration↗
Develop and integrate AI components into applications, with evaluation and production readiness built into the scope.
- Use case and data assessment
- Model development and adaptation
- Application integration and production readiness
Where to startAn AI-enabled product, a prediction task, or a pilot ready for integration.
Explore AI engineering↗
Standardize model releases, monitor production behavior, and manage the systems that support machine learning.
- Model deployment and CI/CD
- Model, data, and service monitoring
- Infrastructure optimization and governance
Where to startManual model releases, changing data, or gaps in production monitoring.
Explore MLOps↗
Assess generative AI opportunities, test applications, and define how they will fit your business workflows.
- Use case discovery and feasibility
- Prototyping, evaluation, and model adaptation
- Workflow integration and human review
Where to startA knowledge, content, or summarization task with outputs your team can evaluate.
Explore GenAI consulting↗