Across conversations with buyers, a recurring theme emerges: distinguishing purpose-built AI from repackaged ChatGPT is increasingly difficult.
We get it. In an AI-saturated market, it’s easy to assume everything runs on the same engine. But Evolv AI isn’t another ChatGPT wrapper—and it wasn’t designed to be.
Instead of relying on a single large language model, Evolv AI combines multiple AI systems—each tailored for experience optimization. Yes, LLMs are part of our stack, but they’re used intentionally: to support content generation and idea synthesis within a broader, structured process. Not to run the process.
Can ChatGPT Replicate Evolv AI’s Automated Recommendations?
We tested it.
In a recent internal experiment, we attempted to manually replicate an Evolv AI-style workflow for UX recommendations using ChatGPT.
Step 1: Gathering Knowledge
We fed ChatGPT structured inputs, including:
- A competitive analysis
- Persona insights
- Experimentation data
- UX screenshots of a pricing page
We asked ChatGPT to identify key performance signals from the pricing page, such as:
- "Expanded pricing details improved desktop checkout performance by +26.7% but reduced mobile by -5.45%."
- "An FAQ sidebar decreased mobile checkout performance by -22.8%."
- "Removing premium upsells increased desktop checkout performance by +13.2%."
Step 3: Generating Insights
From those facts, we prompted for behavioral insights:
- Desktop users prefer persistent, detailed content
- Mobile users need streamlined layouts and fast access to CTAs
- Option overload hurts engagement; too few options reduce perceived value
Step 4: Identifying Opportunities
We asked for CRO opportunities based on those insights, for example:
- Segment Layout by Device – Expand plan details on desktop; collapse on mobile
- Differentiate Bundles Visually – Use badges, icons, or color cues
- Tailor Premium Offers by Device – De-emphasize on desktop; highlight on mobile
Step 5: Hypotheses and Ideas
Finally, we prompted for testable UX ideas:
Hypothesis: Collapsing Plan Cards on Mobile Increases Engagement
- Idea 1: Shrink cards and add a “View Details” toggle
- Idea 2: Hide feature lists behind a click
- Result: Reduces scroll depth, keeps CTAs in view
Hypothesis: Highlighting Premium Features Improves Conversion
- Idea 1: Add a “High Performance” badge
- Idea 2: Apply bold color and shadow to draw attention
- Result: Boosts visibility and engagement among mobile users
The Result?
We got an output that can be used for experimentation.
But it took hours to assess a single page.
Every step required a new prompt. Every insight had to be reasoned through. Every idea had to be aligned to performance data manually.
For an enterprise running:
- Dozens of experiments in parallel
- Across segmented audiences, devices, and traffic patterns
- Requiring constant ideation, prioritization, and iteration…
…it’s just not sustainable.
Why This Matters: From Insight to Impact, Without the Overhead
Solving experimentation challenges at scale is why Evolv AI exists.
We don’t just generate ideas—we automate the lifecycle behind them, helping teams build, test, and optimize faster with less friction.
Where general-purpose LLMs operate in isolation, Evolv AI is anchored in live performance data. It learns continuously from real experiments—identifying what works, where, and for whom—and adapts in real time.
That closed feedback loop is what sets us apart.
What Evolv AI Does Differently
- Synthesizes live and historical performance data: We don’t guess what might work—we know what has worked, and apply that to what could.
- Structures that knowledge into opportunity maps: We surface friction by persona, journey step, and device—then align ideas to real conversion gaps.
- Prioritizes intelligently: We rank opportunities by projected uplift, context, and traffic potential—so the best ideas go live first.
- Closes the loop: Each test feeds back into the system, refining what gets recommended next. This turns experimentation into a compounding advantage.
- Applies agent-based orchestration: Validates ideas against performance logic (not just surface reasoning), ensures outputs are experiment-ready, not just theoretically interesting, and detect misleading suggestions that may sound right but underperform.
LLMs like ChatGPT are valuable—but they lack:
- Persistent context — They don’t remember your historical results, audience segments, or business goals between sessions.
- Performance grounding — They generate ideas, but don’t learn from what actually worked or drove impact.
- Lifecycle orchestration — There’s no built-in system to launch, evaluate, and iterate on ideas over time.
- Reliability of outputs — They can hallucinate facts or invent logic, especially when lacking structured input or guardrails.
So yes—technically, you can ideate UX experiments with ChatGPT. We did.
But only with hours of prompting, structure, and manual effort.
At scale, that’s not just inefficient—it’s a missed opportunity.
Evolv AI bridges that gap. We turn your real-world data into high-impact, testable ideas that evolve alongside your users. It’s not about generating more ideas—it’s about generating the right ideas, automatically, and putting them to work.