Design scalable enterprise RAG and knowledge platforms with architecture, governance, security, and operating models built for production adoption.

This advanced 2-day program helps architects and enterprise AI leaders make informed decisions when designing RAG platforms across organizational boundaries. Participants evaluate architecture patterns, access controls, retrieval services, agent integration, observability, governance, and cost before producing a practical enterprise RAG architecture blueprint.

  • Why get trained: Learn how to design enterprise RAG architectures, permission-aware retrieval, multi-tenant platforms, evaluation systems, governance controls, and phased implementation strategies.
  • Why it matters: Enable secure, scalable, and cost-effective enterprise AI adoption while balancing quality, performance, resilience, compliance, and long-term ownership.
  • Who should attend: Enterprise, Solution, Data, Security, Integration, and Cloud Architects; Senior AI, Platform, Search, and Knowledge Engineers; Engineering Managers; Platform Owners; AI Governance Leads; and Technical Product Leaders.

Participants develop an enterprise RAG architecture and operating model through practical scenarios, architecture exercises, and practitioner-led workshops. HRD Corp Claimable.

Overview

This two-day advanced program prepares architects and enterprise AI leaders to design RAG and knowledge platforms that operate across organizational boundaries. Participants evaluate architecture patterns, source systems, access controls, retrieval services, agent integration, evaluation, observability, cost, governance, and operating models, then produce an enterprise architecture blueprint.

Program boundary. This is an architecture and decision-making program rather than a coding bootcamp. It complements RAG Application Engineering by addressing enterprise scale, multi-tenancy, identity, lifecycle ownership, platform capabilities, governance, and the trade-offs required for production adoption.

Skills Covered

  • Translate business and regulatory needs into enterprise RAG architecture requirements
  • Select centralized, federated, domain-oriented, or managed knowledge patterns
  • Design ingestion, retrieval, authorization, generation, and evidence services
  • Implement permission-aware and tenant-aware retrieval boundaries
  • Design evaluation, observability, auditability, and knowledge-quality controls
  • Integrate RAG with Claude applications, tools, MCP, and agentic workflows
  • Balance quality, security, latency, resilience, and total cost of ownership
  • Produce a phased enterprise RAG architecture and operating model

Prerequisites

Before joining the program, participants should have:

  • Working knowledge of cloud applications, APIs, identity, data platforms, or enterprise architecture
  • Understanding of basic RAG concepts, retrieval pipelines, and generative AI risks
  • Experience making or supporting architecture and governance decisions
  • Coding is not required, although technical implementation experience is useful

Target Audience

  • Enterprise, solution, data, security, integration, and cloud architects
  • Senior AI, platform, search, and knowledge engineers
  • Engineering managers, platform owners, and AI governance leads
  • Technical product leaders responsible for enterprise AI platforms

Course Curriculum

Module 1: Strategy and Use-Case Portfolio

Learn how to connect enterprise AI initiatives with business goals. This includes evaluating business value, risk levels, organizational readiness, build-versus-buy decisions, platform scope, and measurable outcomes.

Module 2: Enterprise Knowledge Landscape

Understand how to assess the information available across an organization. Explore source systems, different types of content, ownership, data lineage, classification, freshness, deletion, and retention so that knowledge can be managed effectively.

Module 3: RAG Architecture Patterns

Explore different ways to structure enterprise RAG systems, including centralized, federated, domain-based, managed, multimodal, graph-enhanced, and agentic approaches. Learn how to compare these patterns and select an approach based on business and technical needs.

Module 4: Identity, Security and Isolation

Learn how to make sure users can access only the information they are authorized to see. This includes user context, access-aware retrieval, tenant boundaries, secrets, encryption, and controls for protecting sensitive information.

Module 5: Evaluation and Observability

Understand how to measure and monitor the quality of enterprise RAG systems. Topics include retrieval and answer evaluation, traces, evidence, audit logs, quality controls, drift monitoring, and incident response.

Module 6: Scale, Reliability and Economics

Learn how to design RAG systems that can operate reliably at enterprise scale. Explore indexing, caching, response time, resilience, throughput, model routing, capacity planning, and cost controls.

Module 7: Governance and Operating Model

Explore how organizations can manage enterprise knowledge and AI platforms over time. This includes ownership, standards, platform responsibilities, domain stewardship, approvals, and change management.

Module 8: Architecture Capstone

Put the concepts into practice by designing an enterprise RAG platform for a realistic multi-domain organization. The capstone covers source integration, retrieval, identity, Claude integration, citations, evaluation, observability, governance, ownership, cost controls, and a phased implementation roadmap.

 

Dates & Locations

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Exam & Certification

Complete the Enterprise RAG Architecture & Knowledge Platforms Training and receive your Enterprise RAG Architecture & Knowledge Platforms Training Certificate of Completion after successfully completing the 2-day program. This validates your skills in designing enterprise-scale RAG and knowledge platforms, including architecture patterns, security and access controls, retrieval, evaluation, observability, governance, cost management, and operating models.

Training & Certification Guide

Why train with Trainocate

No. Coding is not required. However, technical implementation experience can be useful because the program deals with enterprise architecture, integrations, security, and AI platforms.

You will work on practical architecture activities such as mapping enterprise knowledge sources, comparing RAG patterns, designing access-aware retrieval, creating control strategies, and developing an enterprise RAG architecture blueprint.

The capstone involves designing an enterprise RAG platform for a realistic multi-domain organization. You will create a blueprint covering source integration, retrieval, identity, Claude integration, citations, evaluation, observability, governance, ownership, cost controls, and implementation planning.

The program includes practical architecture deliverables such as a knowledge-domain map, target architecture, security boundaries, governance and operating model, phased roadmap, risk register, and executive architecture brief.

The program is not designed as a coding bootcamp. Its focus is enterprise architecture and decision-making, including how to select architecture patterns, manage security and access, evaluate systems, and plan for enterprise-scale adoption.

Participants are assessed through hands-on lab evidence, the capstone solution, and their ability to explain architecture decisions, controls, limitations, and next steps.

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