Capstone — An End-to-End SAP AI Analytics Solution

What you will learn

• Assemble five previously learned SAP AI capabilities (semantic grounding source, vector retrieval, orchestration, tabular prediction, narrow agent) into one governed, end-to-end architecture
• Explain and apply the generative AI hub orchestration pipeline's fixed execution order for a solution combining grounding, masking, filtering and a model call
• Combine a tabular prediction (SAP-RPT or HANA PAL) with an LLM explanation step that never invents an uncomputed cause
• Design a narrow, single-tool agent action with a named executing identity, exact input schema and a human-in-the-loop confirmation step
• Produce a governance and cost close-out that keeps generative-AI-hub metering (tokens/GenAI tokens/capacity units) and AI Units separate, with no invented totals
• State precisely what changes between a trial-tenant build and a production deployment of the same solution

Module overview

Who this is for. The final module of the SAP AI path (“AI & LLM Fundamentals for SAP Consultants — Transformers, Tokens, Embeddings, Context” through “SAP AI by Industry — Verified Cases in Manufacturing, Retail and Financial Services”). It assumes you have built or specified the three portfolio demos, can position the work honestly, and can cite verified industry evidence. It assembles five capabilities you have already met — Datasphere or Business Data Cloud as a grounding source, HANA Cloud's Vector Engine, the generative AI hub's orchestration pipeline, a tabular prediction (SAP-RPT or PAL), and a narrow Joule/MCP agent action — into one coherent, governed, end-to-end solution, and names exactly what separates a trial-tenant build from a production one.

Prerequisites

• Completion of “Your SAP AI Portfolio — Three Demos an Analytics Consultant Can Show”, “Positioning Yourself as a Freelance SAP AI Consultant” and “SAP AI by Industry — Verified Cases in Manufacturing, Retail and Financial Services”, or equivalent portfolio, positioning and evidence-discipline experience
• Working knowledge of the generative AI hub orchestration service, HANA Cloud Vector Engine, SAP-RPT/PAL (“SAP HANA PAL Hands-On — Classification, Clustering and Association with UnifiedClassification” to “hana-ml Hands-On — DataFrame, ConnectionContext and ModelStorage in SAP HANA Cloud”), and Joule Studio/MCP agents
• Access to a generative AI hub trial tenant, a HANA Cloud instance with the NLP feature enabled, and a Joule Studio sandbox is recommended but not required for the written exercises

Outcomes

• Draw and defend a five-layer SAP AI architecture from memory, naming the exact SAP capability and its GA status at each layer.
• Produce a complete, correctly ordered orchestration configuration combining grounding, masking and filtering for a real use case.
• Deliver a governance and cost close-out that a client's security and finance teams could both sign off on.
• State the precise, concrete gap between a trial-tenant build and a production deployment of the same solution.