In the previous posts, we covered the Strategy of Synthetic User Research and the Architecture of building High-Fidelity Personas.
You now have a “Virtual CISO” or a “Synthetic Procurement Manager” sitting in your digital office. They are grounded in reality, trained on your competitors’ reviews, and programmed to be skeptical.
Now comes the hard part: What do you actually say to them?
If you treat your synthetic panel like a casual chatbot, you will get casual answers. To get actionable Roadmap data, you need to treat this like an interrogation. You need structured “Simulations” that force the AI to choose between trade-offs.
A real customer won’t tell you the truth unless you push them. Neither will a synthetic one.
Here are the 3 “Interrogation Workflows” we use at Development Corporate to stress-test early-stage SaaS products.
Workflow 1: The “Landing Page Roast” (Solving Conversion Friction)
Founders often stare at their own landing pages for so long they become blind to the jargon. You think “Orchestrated Data Silos” sounds smart. Your customer thinks it sounds expensive and confusing.
The goal of the Roast is to identify “Cognitive Friction”—the exact moment a user gets confused and closes the tab.
The Setup
Don’t just ask, “Is this page good?” Instead, assign your “Enterprise Eddie” persona a specific task: “Find 3 reasons to leave this website immediately.”
The Prompt Structure
Markdown
### CONTEXT
You are “Enterprise Eddie” (as defined previously). You are busy. You have 30 seconds before your next meeting.
### TASK
Review the Landing Page copy below.
Your goal is to find “Red Flags” that would make you close the tab.
Look for:
1. Vague Promises (“We optimize synergy”)
2. Lack of Social Proof (No logos, no case studies)
3. Confusing Pricing (Enterprise = “Contact Us”)
### OUTPUT FORMAT
Provide a “Brutal Honesty” score (1-10) and a bulleted list of the exact sentences that annoyed you.
Why This Works
When we run this for clients, the AI frequently highlights issues like:
- “You didn’t list your integrations above the fold. I use Salesforce. If I don’t see the logo, I assume you don’t support it. Goodbye.”
- “You used the word ‘Revolutionary’ three times. I am a CISO; I don’t want a revolution, I want stability.”
This is high-velocity CRO (Conversion Rate Optimization) before you even launch the site.
Workflow 2: The Feature “RICE” Simulator (Prioritizing the Roadmap)
Every early-stage Product Manager battles with the Roadmap. Should you build SSO (Single Sign-On) to please enterprise clients, or a Mobile App to please end-users?
Usually, you guess. Or you use the RICE Framework (Reach, Impact, Confidence, Effort) based on gut feeling.
With a synthetic panel, you can simulate the “Impact” score.
The Setup
Take your top 3 potential features. Present them to 3 different synthetic personas (e.g., The Admin, The End-User, The Buyer). Ask them to rank the features based on their specific selfish needs.
The Prompt Structure
Markdown
### SCENARIO
We are deciding which feature to build next for Q3. You are the “VP of Engineering.”
### OPTION A: Audit Logs
Detailed history of who changed what in the system.
### OPTION B: Slack Integration
Real-time alerts in your team channel.
### OPTION C: Dark Mode
Visual theme update.
### TASK
Rank these 1-3 based strictly on your priority of “Security and Compliance.”
Explain WHY you ranked them that way.
The Insight
You will likely find a conflict:
- The User wants Slack Integration (convenience).
- The Buyer (VP) wants Audit Logs (compliance).
- Nobody cares about Dark Mode right now.
This highlights the Buyer vs. User Disconnect, a classic trap in B2B SaaS. If you only listen to users, you build a fun product that no one buys. If you only listen to buyers, you build shelfware. The simulation reveals the trade-off.
Workflow 3: The “Objection Battle” (The Sales Dojo)
Founder-Led Sales is terrifying because you practice on live leads. You burn your best prospects learning how to handle basic objections.
The Objection Battle turns your synthetic persona into a “Gatekeeper Simulator.”
The Setup
You are going to script a sales conversation. You will paste your “Opening Pitch,” and the AI is instructed to interrupt you.
The Prompt Structure
Markdown
### MODE: “The Wall”
You are a prospect who does NOT want to buy. You are happy with your current solution (Excel).
I will speak first.
Whatever I say, you must find a logical objection.
– If I talk about “Time Savings,” you argue about “Implementation Time.”
– If I talk about “Cost,” you argue about “Budget Freeze.”
– If I talk about “Features,” you say “We don’t need that.”
Only concede if I give a truly compelling, quantified answer.
The “Dojo” Effect
Run this loop. Try to convince “Synthetic Sarah” to book a demo.
- Attempt 1: You try to sell features. She shuts you down.
- Attempt 2: You try to sell fear. She calls you manipulative.
- Attempt 3: You pivot to specific ROI/Case Studies. She agrees to a 15-minute call.
Copy the script from Attempt 3. That is your new sales script for real humans. You just saved yourself 20 rejected cold calls.
Scaling Up: From Chat to Automated Batching
Running these one by one in ChatGPT is fine for testing. But to get “Directional Data,” you need volume.
Sophisticated teams use tools like LangChain orZapier to automate this.
The “Batching” Workflow:
- Create a spreadsheet with 50 variations of your Value Proposition.
- Use an API (like OpenAI’s) to feed each row to 5 different Synthetic Personas.
- Ask the AI to output a “Score” (0-10) for each.
- Sort the spreadsheet by the highest average score.
Now you aren’t just writing copy; you are optimizing copy mathematically against a simulated market. This is the future of AI-Accelerated Strategy.
Conclusion: Data-Driven Empathy
The goal of these simulations is not to turn product management into a robot’s job. It is to clear the noise.
By using the Landing Page Roast, the RICE Simulator, and the Sales Dojo, you filter out the bad ideas, the confusing copy, and the weak arguments before they ever reach a human being.
This respects your customers’ time. When you finally get that real CISO on the phone, you aren’t guessing. You are validating a refined, battle-tested hypothesis.
But… is the data real? How do you know “Enterprise Eddie” isn’t just hallucinating a preference? How do you distinguish between a simulated insight and a random error? In the next post, we tackle the Elephant in the Room: Hallucinations. I will teach you the “Validation Sandwich”—the methodology to cross-reference AI insights with human reality.
