AI services
AI Advisory Services
Infrastructure Readiness and Technology Selection
Assess infrastructure gaps and guide AI technology stack selection
Use Case Advisory and ROI Prioritization
Identify and sequence AI use cases by P&L impact, customer experience gain, and 12-month ROI
AI Readiness and Capability Assessment
Assess AI maturity across people, process, and technology — and roadmap capability gaps
Intelligent Process Reengineering
Redesign core processes with AI for step-change gains in efficiency, quality, and speed
AI Data Services
Data Readiness
Audit and cleanse data across sources for model-ready AI deployment
Data Transformation
Automate data processing, enrichment, and standardization across enterprise pipelines
Unified Data Fabric Strategy
Connect disparate sources into a unified, governed, AI-accessible data fabric
Adaptive Data Integration Pipelines
Ingest, transform, and route data across systems — adapting to changing inputs and formats
Agentic AI Implementation Services
AI/ML Model Selection and Development
Evaluate, select, and develop ML and predictive models — from scoping through deployment
Customized AI Solutions
Deploy AI solutions and use cases tailored to your specific needs, context and workflows
Generative AI Services
Implement GenAI solutions covering LLM integration, prompt engineering, RAG, and fine-tuning
Analytics AI
Embed AI into analytics to automate reporting and enable predictive decision-making
Responsible AI and Governance
Unified AI Risk and Governance
Assess, benchmark, and implement AI Governance tailored to an organization’s AI adoption maturity. Leading frameworks — EU AI Act, ISO 42001, and NIST AI RMF — harmonized into existing ISMS and GRC ecosystems, delivering a clear operating model and a structured path to governance maturity
AI and Agentic Security
Stress-test GenAI applications and autonomous agents. Red-team LLMs against prompt injection, jailbreaks, and adversarial inputs — with defensive control validation across guardrails, SIEM, SOAR, and DLP
Model Security and Risk Management
Assess ML and LLM risk across the full model lifecycle — covering bias, fairness, adversarial stress testing, and continuous KPI monitoring for drift, degradation, and hallucination
Shadow AI Security
Identify unauthorized GenAI tool usage across the enterprise, enforce usage policies, and assess third-party AI risk — reducing exposure to data leakage, IP loss, and compliance blind spots
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