Bildur researches thousands of real reviews, builds a psychological buyer profile, then tests formats and headlines against a 10-person AI panel modeled on your actual customers.
Built on analysis of 500+ high-converting DTC advertorials
Every other AI writing tool starts the same way: paste your product description, pick a tone, click generate. The result? Generic copy that could be for any product.
Real advertorials that convert aren't written from product specs. They're written from deep understanding of the customer — their fears, frustrations, the exact language they use to describe their problems, what finally pushed them to buy.
That's what Bildur does differently. Before we write a single word, we research your customer — then we test every decision against a synthetic audience modeled on them.
Before generating your advertorial, Bildur creates 10 AI personas modeled on your actual customers and polls them on format preference and headline effectiveness.
From your customer research, we create 10 psychologically varied sub-personas — from skeptical researchers to impulse buyers, scanners to deep readers.
Each persona votes on which advertorial format (listicle, editorial, story, how-to, comparison) they'd click, read, and buy from. The winner is selected with confidence scoring.
6 headline variations are tested against the panel. The winning headline is selected based on click intent, trust, and purchase signals.
| Format | Click | Read | Buy |
|---|---|---|---|
| Listicle (Selected) | 9/10 | 8/10 | 7/10 |
| News/Editorial | 3/10 | 5/10 | 2/10 |
| Personal Story | 2/10 | 4/10 | 1/10 |
“7 Reasons Dermatologists Are Recommending This New Moisturizer”
Bildur analyzes thousands of real reviews from Amazon, Reddit, and competitor sites — extracting the exact language, fears, and desires your customers express.
From that research, Bildur builds a behavioral buyer profile: motivations, cognitive biases, decision triggers, objection patterns, and voice-of-customer quotes.
10 AI personas modeled on your customers vote on format and headlines. No more guessing — know which version will convert before you publish.
Using the winning format and headline, Bildur generates a complete, publish-ready advertorial that speaks your customer's language.
Bildur takes your product page, Amazon listing, or competitor URLs as starting points.
Thousands of real reviews scraped and analyzed across Amazon, Reddit, and competitor sites. Not surveys. Not assumptions. Real customer voice.
Bildur builds a comprehensive buyer profile — motivations, fears, cognitive biases, purchase triggers, and the exact language your customers use.
10 AI personas modeled on your customers vote on 5 advertorial formats. The winning format is selected with confidence scoring. Then 6 headlines are tested against the panel.
Using the panel-selected format and winning headline, a complete, publish-ready advertorial is generated — fully designed, properly structured, with conversion elements placed based on what actually works.
The Synthetic Audience Panel tests all five formats and tells you which one your customers prefer — with confidence scoring.
"7 Reasons This Product Is Taking Over"
Cold traffic from social ads. Scannable, curiosity-driven.
"The Science Behind the Trend"
Authority-building. Product doesn't appear until 60% through.
"How I Finally Fixed My [Problem]"
Warm audiences. Emotional, transformation-driven.
"The Expert Guide to [Topic]"
SEO traffic. Value-first, product-second.
"We Tested [Brand] vs [Competitors]"
High-intent buyers actively shopping. Data-driven.
“Bildur doesn't guess what your customers want. It asks them.”
1,500–2,500 words, professionally structured
The format your audience actually prefers, with confidence scoring
Tested against 10 personas for click intent, trust, and purchase signals
CTAs, social proof, and trust elements placed based on proven patterns
Written in your buyer's language, addressing their specific objections
See exactly how each persona voted and why — use insights across all your marketing
Customer reviews analyzed during development
AI personas per synthetic audience panel
High-converting advertorials studied