Live SAP Simulation Before Consultants Finish Discovery
Stand up a buyer-specific SAP simulation and automate solution delivery, with proof through reliability runs, failure cases, automated repair, and audit evidence.
Ground the simulation in the buyer's real processes
Connect the reference and data sources the simulation leans on: AWS Knowledge for architecture and best-practice SOPs, and Snowflake or Databricks for the buyer-shaped data the scenario runs against. The simulation reasons from these, not from a generic template.
Capture the build in a playbook, a reusable, named set of steps: which of the buyer's processes to model, the scenarios to prove, and the evidence to produce. Reused across prospects, it turns each new pitch into a repeatable run.
Playbook: !sap-sim Given a buyer's processes: 1. Model the target process (order-to-cash, procure-to-pay) as a runnable simulation against buyer-shaped data. 2. Define the reliability scenarios: normal load, peak load, and the failure cases that scare them. 3. Run each scenario; capture performance and where it breaks. 4. Repair automatically what fails; rerun to prove the fix. 5. Produce audit evidence: what ran, what broke, what was fixed.
Ask the buyer for one real process document or data extract. A simulation grounded in their own order flow lands far harder than a polished demo built on invented data.
Kick off the simulation build per prospect
This is an on-demand run you start when a deal warrants proof: paste the kickoff message with the prospect and their processes filled in, and Opulent stands up the scenario before discovery wraps.
For a repeatable motion across many prospects, wrap the playbook so each new buyer brief spins up its own simulation from the same shape.
The sharp edge: a simulation that only proves the happy path convinces no one who has been burned by an implementation. Insist on modeling the failure cases the buyer actually fears, not just the demo-friendly flow.
Watch the simulation prove reliability
Take a run building an order-to-cash simulation for a mid-market manufacturer:
The run does not just show it working, it breaks it on purpose, repairs it, and proves the repair.
Simulation: order-to-cash for "Kessler Industrial" Run actions: - Modeled their order-to-cash flow against a Snowflake dataset shaped like their volumes. - Normal load: 10k orders/hr clean. Peak: held at 35k/hr. - Failure case: injected a pricing-service outage, 8% of orders stalled at invoicing. - Auto-repair: added a retry-and-queue path; reran peak with the outage, 0% stalled. - Produced an evidence pack: run logs, before/after metrics, and the repair diff.
What you can put in front of the buyer
When the run finishes, you have proof, not slides, and each claim is verifiable:
A live, buyer-specific simulation of their process. Reliability results across normal load, peak load, and named failure cases. An automated-repair record showing what broke and how it was fixed. An audit-evidence pack tying every claim to a run log.
The evidence pack is the proof-of-work: the buyer can trace each performance and reliability claim back to the run that produced it, which is what earns trust before consultants have even scoped the project.
Sharpen the proof
When a prospect pushes on a scenario you did not model, add it to !sap-sim, or write the pattern into memory (the notes a run recalls next time), like "manufacturing buyers always ask about EDI failure handling".
Keep a library of the failure cases that resonated; the strongest proofs come from scenarios other buyers in the same segment feared.
The natural chain: once a buyer signs, hand the validated scenarios to Data Analysis as an Agent-Run Workflow so the same data and reasoning support their live reporting.