Custom Programs
Our learning catalog spans the full knowledge stack required to accelerate your AI adoption journey, contextualized to your industry, adapted to your functions, and sequenced around your strategic priorities.
Topics
A comprehensive module library spanning the full knowledge stack for AI adoption, covering both the business and technology dimensions.
AI Change Management
Measure AI maturity, improve AI capabilities and AI adoption; make your organization AI first.
AI Economics & ROI Measurement
Quantify value, cost, implementation requirements, as well as implementation risks for AI initiatives.
AI Governance, Risk & Compliance
Develop understanding of key governance considerations and how they can be managed.
AI Strategy & Roadmap Design
Define an AI vision, set priorities, and build a realistic AI transformation roadmap.
AI Unit Economics
Calculate what each AI interaction actually costs your business: per query, per user, per workflow.
Building & Leading AI Teams
Decide what to build in-house, what to outsource, who you actually need to hire, and what skills to train for.
Collaborating with AI Vendors & Partners
Understand technology ecosystem, providers, contract types, and compatibility with existing technology stack.
Data Strategy for AI
Make data AI ready; access and manipulate data safely with AI tools.
Responsible & Ethical AI
Spot bias, privacy, and accountability traps before AI solutions reach production.
Use-Case Identification & Prioritization
Approach to identify, evaluate, and sequence high-value AI opportunities in a structured way.
AI Application Development
Designing and building production-grade AI applications: API design, integration patterns, latency and cost optimization, fallback handling, user experience patterns for AI-powered applications.
AI Governance, Compliance & Regulation
SDAIA AI Ethics Principles, PDPL and data protection requirements, NCA controls, EU AI Act and NIST AI RMF, internal AI policy design, model risk management, and audit readiness for regulated AI deployments.
AI Infrastructure & Compute
GPU and accelerator architectures, inference optimization, hosted vs. self-hosted model deployment, and capacity planning at scale.
AI Security & Safety Engineering
Prompt injection defense, data leakage prevention, adversarial robustness, output filtering, and audit logging for AI systems.
Agentic AI Foundations & Architecture
Agent architectures with tools, memory, and planning; multi-agent orchestration patterns; human-in-the-loop design for safe execution; architecting agents for scale and continuous improvement.
Data Engineering for AI
Training data curation, labeling and annotation strategies, synthetic data generation, data quality for RAG pipelines, evaluation dataset construction, and handling unstructured and multimodal data at scale.
Generative AI & Large Language Models
Transformer architectures, model capabilities and limitations, fine-tuning approaches, and selection criteria for enterprise use cases.
MLOps & Model Deployment
Model versioning, CI/CD for ML, deployment strategies, drift and performance monitoring, and incident response for production AI systems.
Machine Learning Foundations
Core supervised, unsupervised, and deep learning concepts, with the criteria for selecting the right approach for a given problem.
Model Evaluation & Testing
Evaluation methodologies for ML and LLM systems: benchmark design, automated and human evaluation, regression testing, and quality gates.
Prompt Engineering & Optimization
Prompting patterns, structured output techniques, systematic prompt evaluation, version control for production prompt pipelines.
RAG & Knowledge Systems
RAG design: chunking strategies, embeddings, vector and hybrid search, reranking, and retrieval evaluation.
Industries
Sector-specific context, cases & regulation
Functions
Role-specific workflows, tools & use cases
Use-Cases
Focused curricula built around a single high-value AI application domain, regardless of industry or function.
Illustrative Examples of Use-Case Specific Training Programs:
Building Call-Center Agents
Design, build, and deploy AI agents that handle inbound customer queries end-to-end, covering intent detection, knowledge retrieval, escalation logic, voice and chat interfaces, and quality evaluation.
Ideal for
Customer service leaders, CX engineers, operations teams
Building Business Intelligence Agents
Build agents that query structured data, generate reports, surface anomalies, and answer business questions in natural language, integrating with data warehouses, dashboards, and internal knowledge bases.
Ideal for
Data teams, finance & strategy analysts, product managers
Building Media Monitoring Agents
Architect agents that continuously track news, social media, and publications, extracting signals, classifying sentiment, detecting emerging topics, and delivering structured briefings to stakeholders.
Ideal for
Communications teams, brand managers, research analysts
Let's design your program
We'll work with you to select the right modules, contextualize them to your industry and team, and sequence them around your specific AI maturity and goals.