We Value Your Expert Opinion.

This page is for those who have been provided early access to the AI Client Consulting Simulator prototype. This is a high-fidelity training ground designed to bridge the gap between theory and execution. We are now inviting you to break the mold—dive in and evaluate how this environment pressure-tests your framing techniques, discovery tactics, and information fidelity.

Use the S.P.O.T.© Framework along with the quick-start scenario below to guide your testing of the simulator.

Quick Start Scenario using the S.P.O.T.© Framework: The OptiCore Logistics Churn Crisis

Important Testing Note: Because this is a prototype phase, you are not expected to complete a full consulting cycle. Your objective is to perform a "Mini-S.P.O.T. Check." We ask that you select a few personas and ask 2–3 targeted questions per SPOT stage to evaluate how the AI handles the transition from high-level business goals to granular data realities.

The Context

You are a consultant for OptiCore Solutions, a B2B SaaS company. Their flagship product, "OptiFlow," is facing a critical threat: rising churn in the Logistics vertical. With accounts averaging $120,000 ARR, this trend endangers a $7M revenue retention goal essential for a future private equity exit.

The Current Situation

  • Logistics Churn: 14% (Target: 8%).

  • Onboarding NPS: 52 (Target: 70).

  • Support Lag: 38-hour resolution time; 60% of tickets are "Integration" related.

Your Mission: Rapid Discovery Testing

Select from the following personas to test the AI’s response depth across the S.P.O.T.© framework:

  1. Alex Jensen (CEO): Best for Scope (S). Ask about the $7M revenue gap and the strategic importance of the Logistics vertical.

  2. Brenda Markham (Head of Sales): Best for Scope (S) & Process (P). Ask about "Logistics Discounting" and why the sales team feels the product is losing its edge.

  3. Charles Ito (VP of Product): Best for Process (P). Ask about the 22% analytics adoption rate and what the product usage data reveals about user friction.

  4. Evan Galloway (Director of Data Platforms): Best for Observation (O). Ask about the AWS/Redshift infrastructure and the 20% missing timestamp error rate.

  5. Diana Rostov (Lead Data Scientist): Best for Observation (O). Ask about the "fact_product_usage_granular" tables and the hurdles in her current churn models.

  6. Fiona Chen (Customer Support Manager): Best for Process (P) & Transmit (T). Ask about the surge in "Integration" tickets and why support resolution times are lagging.

How to Begin

To start the session:

  • Select a persona.

  • Copy and paste this into the chat:
    "I am here to help OptiCore address the rising churn in the Logistics vertical. From your perspective, what are the primary issues to be addressed in order to hit the 8% churn target this year?"

Then drill down with the other personas.

Tester’s Evaluation Guide

Keep this open as a reference to help you fill out the feedback form once you finish your "Mini-SPOT Check."

1. Information Fidelity

  • Level 4 (High-Fidelity): The persona feels "real" (e.g., Alex focuses on PE-valuation while Evan focuses on server logs). The data is nuanced and scenario-specific.

  • Level 3 (Authentic/Surface): The character is correct, but the answers feel a bit "safe" or lack the gritty complexity of a real client.

  • Level 2 (Generic Chatbot): The response is helpful but sounds like a standard AI assistant rather than a stakeholder with a specific job to do.

  • Level 1 (Misaligned): The AI "hallucinates" facts or the CEO suddenly starts discussing low-level Python code (breaking character).

2. Trigger Accuracy

Did the AI successfully "gate" information? It should withhold the "smoking gun" (like specific technical error codes or internal team conflicts) until you ask a question that demonstrates the correct depth of framing.

3. S.P.O.T.© Execution Matrix

On a scale of 1–5, how effectively did the AI allow you to:

  • S: Define the business problem and stakeholder goals?

  • P: Map the workflow mechanics and find the bottleneck?

  • O: Audit the data health and availability?

  • T: Confirm your understanding of the solution context?

Technical Reminder: This is a prototype. There is no save or export function. Please copy text and paste into a document or take screenshots of any significant breakthroughs or errors to include with your feedback.

Testing the Simulator: The Feedback Loop

We’re in a rapid iteration phase, and your feedback is our most critical data point. As we refine this experience, we need to know: Does this simulator add value to the Business Problem Framing Workshop?

Submit your feedback using this form. Your insights on the form below are vital. We are specifically looking for your take on how this tool enhances the "upstream" problem-framing process. Your input is what transforms this prototype into a world-class training asset.

  • The "v1.0" Roadmap: Please note that this is a functional prototype. While the core AI interaction is live, the ability to save and export your chat history is currently in development for the next release. For now, we recommend copying questions/responses and capturing screenshots of any key strategic breakthroughs you’d like to keep. You’ll the opportunity to upload those in the survey below.

Testers please complete the UX survey.

Registered Users Go to Simulator

Want a closer look or a chance to test the prototype? Fill out the form below and we’ll be in touch!