Search across Architecture IntelligenceTry engineering explorations, SAP Clean Core, evidence, integration or AI governance.

Workspace

Saved on this device only — nothing here is synced or uploaded.

ARCHITECTED BY PRASAD™

Can AI reproduce how an enterprise architect actually decides?

This is a living computational Digital Twin of architecture decision-making. It models public evidence, SAP authority, policy limits, uncertainty and decision behaviour, then compares observable outcomes with human-authored architecture decisions.

Prasad Digital Twin

Observable decision behaviour.

Current public state
Twin state
Current

Public snapshot, not fake activity

Evidence
11

Approved public evidence records

Observed agreement
2 / 2

N = 2 human-gold cases

Authority
Read-only

0 public write tools

Scenario
Governed SAP AI architecture
Decision
Capella vs ABAP4C Architecture 1 vs side-by-side BTP
State
Partial
LIVE / SIMULATE / EVIDENCE / VERIFY

One Twin, one operating model.

Legacy ASK and DECIDE routes still work. Public presentation now leads with the mental model people understand first.

One real decision demo

Watch a recommendation move when BTP becomes unavailable.

The preview below is rendered by the same deterministic R5 decision and counterfactual runtime used on the Digital Twin page.

Open the full trace
Counterfactual

Promotion flagship: governed SAP AI architecture pattern

Current research experiment

Governed SAP AI agents

Open research theme
Question

AI-generated explanations can appear credible without sufficient SAP evidence or control.

Position

Use deterministic evidence collection first, LLM reasoning second, and explicit human approval for consequential actions.

Next

Publish a reusable trust and execution reference model.

Public corpus

12 engineering explorations, 14 publications and 10 research themes.

Prasad Deshpande in a professional portrait
Prasad Deshpande
The human behind the architecture

The human behind the Twin.

I am Prasad Deshpande, an SAP and enterprise-architecture practitioner focused on making complex transformation and governed AI practical, explainable and operationally safe.