1. AI Strategy & Engineering: Design and implement AI strategies, platforms, and architectures tailored to business needs. By orchestrating AI agents, proprietary platforms, and third-party tools on purpose-built infrastructure, enterprises can move beyond experimentation to establish a unified, enterprise-wide AI operating model.
  2. Data for AI: Prepare enterprise data, both structured and unstructured, for AI model readiness. By building AI-ready data platforms and applying AI-grade data engineering (including finger printing and synthetic data services), organizations can convert raw data into a trusted strategic asset that fuels advanced analytics, predictive intelligence, and more informed, real-time decision-making.
  3. Process AI: Transform core business processes by integrating AI agents and human expertise, with a focus on redesigning end-to-end workflows. It enables domain-aware agents to work alongside humans to drive step-change improvements in efficiency, experience and deliver business outcomes across functions and industries.
  4. Agentic Legacy Modernization: Leverage AI agents to reverse-engineer existing estates, understand their intent and progressively modernize them without disruption. This helps enterprises reduce technical debt while gaining the agility to respond to changing business demands.
  5. Physical AI: Design intelligent products and embed AI into physical devices so they can capture sensor data, interpret signals, and take real-time action. By combining digital twins, robotics, autonomous systems, and edge intelligence, organizations can reimagine products, operations, and experiences where digital and physical converge.
  6. AI Trust: Ensure AI systems and agents embrace responsible, secure, and ethical AI practices across their entire lifecycle. From embedding risk assessments and policy design to security testing and governance enterprises can scale AI with confidence while meeting regulatory, ethical, and risk expectations.