I didn’t plan to start a full-blown experiment that afternoon. It began as a coffee-break curiosity — a “what if” moment while I had ChatGPT software open, the Claude language model in another tab, and Perplexity AI company ready to dig for data.
I’d been circling the same business challenge for weeks. Every brainstorming session with my team ended the same way — a pile of ideas, no clear winner. So I decided to see what would happen if I threw the exact same question at six different AI models.
I thought I’d get six similar answers. I couldn’t have been more wrong.
The question that started it all
I typed it once, carefully:
“If you had to grow my business revenue by 50% in 90 days, what’s the exact plan? Daily or weekly steps, no fluff.”
I gave them my business model in two lines. No backstory, no warm-up. I wanted to see how they’d think without me steering.
Round one – ChatGPT’s “consultant’s brief”
ChatGPT came back fast. It split the 90 days into three 30-day sprints:
- Sprint 1: Audience growth
- Sprint 2: Offer optimization
- Sprint 3: Sales conversion
Every task was clean, actionable, and realistic. This was the kind of plan you could hand to a mid-level manager and know they’d get moving. But as solid as it was, it felt… safe. Nothing in it made me stop and think, “I’d never have tried that.”
Round two – Claude’s psychological play
Claude took the same challenge and came at it sideways. Instead of just “get more leads,” it focused on why people buy.
It rewrote the plan so that retention was as important as acquisition.
It told me to:
- Redesign onboarding to create an emotional win in the first 48 hours.
- Build a referral loop that rewarded behavior, not just transactions.
- Send personalized follow-ups that referenced the customer’s first success.
It was the same revenue target — but framed in a way that made me realize we’d been ignoring half the game.
Round three – Perplexity brings receipts
Perplexity AI didn’t care about tone. It cared about proof.
It spit out competitor data like a machine gun: pricing breakdowns, ad spend estimates, most-used channels, average lifetime values. Then it overlaid those numbers on my own funnel.
The result? I could see exactly where I was undercharging, which channels were overpriced for my niche, and where two of my competitors were making easy money I was leaving on the table.
Round four and five – The curveballs
Two smaller models I rarely touch went in unexpected directions.
One focused entirely on automation — cutting my workload by 20 hours a week without losing output. It mapped tasks to outsource, automate, or drop entirely.
The other ignored marketing altogether and gave me 10 low-cost acquisition channels, most of which I’d never heard of. No content calendars, no ads — just unconventional, guerrilla-style tactics.
Round six – The underwhelmer
The sixth model… well, let’s just say it was filler. It gave me vague “increase awareness” and “optimize your brand story” lines with no meat. But even that was useful — I could immediately see what bad advice looked like next to the strong stuff.
Where it got messy
By the end, I had six documents open, each with different strengths. But if I tried to follow all of them? Total chaos. Some overlapped, some contradicted each other, and some were brilliant in one section but useless in another.
That’s when I pulled up Chatronix.
How Chatronix turned six voices into one plan
I dropped all six responses into Chatronix’s workspace. Here’s what happened:
- Six models, one desk: Each specialized in a part of the plan — structure, persuasion, proof, innovation.
- Turbo mode: Every tweak I made updated across the plan instantly.
- One Perfect Answer: Chatronix took the best bits from every model, stripped duplicates, and filled gaps.
The final plan was lean, aggressive, and felt like it had been written by a dream team of consultants who’d spent a month in a war room together.
| Step | From ChatGPT | From Claude | From Perplexity | From others | Final Plan |
| Acquisition channels | Solid basics | Loyalty focus | Competitor-based picks | Guerrilla ideas | Mix of all |
| Pricing | Conservative | Value framing | Benchmark-driven | — | Data-backed premium |
| Operations | — | — | — | Automation blueprint | Automation + delegation |
| Messaging | General | Emotional hooks | — | — | Emotion + proof blend |
Merge multiple AI answers into one clear plan with Chatronix — and let One Perfect Answer do the heavy lifting.
Bonus prompt kit for your own multi-model test
- Ask each AI: “If you had to hit [goal] in 90 days, what’s your exact plan?”
- Feed each model identical business context for fairness.
- Request one competitor analysis for market positioning.
- Ask for one unconventional idea from each model.
- Merge the best points in Chatronix for a unified strategy.
The moment it “clicked”
When I read the merged Chatronix plan, I realized something: no single model had given me the best answer. The “mind-blowing” part wasn’t one response — it was the combination.
From ChatGPT I got structure. From Claude I got psychology. From Perplexity I got proof. From the smaller models I got speed and originality. And from Chatronix, I got a way to make them all work together.
What happened when I ran the plan
I gave myself 90 days, as the prompt required.
By day 30, we’d already hit 20% growth.
By day 60, my sales team had cut follow-up times in half.
By day 85, we were at 48% growth — not quite 50%, but far enough to call it a win.
Why I’d do it again tomorrow
This experiment taught me that AI isn’t about picking “the best model” and sticking to it. It’s about creating a panel of voices, letting them argue, and then distilling the truth.
Now, whenever I face a big strategic decision, I start with six perspectives — and I always end with one Perfect Answer.