Built to think like
your actual buyers.
Buyer Clone doesn't run a generic AI audit. Each agent is constructed around a specific buyer — their role, their personality, and the way they read a page before they decide whether to trust you. Here's what that means in practice.
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Most AI personas are the same person.
Left uncalibrated, language models default to a very specific buyer: roughly 25–35, tech-comfortable, culturally Western, moderately optimistic. That's not a buyer archetype. That's a mode collapse.
- One persona type re-used across every test
- No cultural calibration — every buyer behaves like an American user
- Reads every section with the same level of attention
- Describes what it thinks, but doesn't behave like someone who'd bounce
- Produces the same feedback regardless of your actual market
- Each agent built for a specific role, market, and buying context
- Role and context calibration — a cautious CFO reads your pricing differently than a high-trust growth lead
- Attention depletes under friction and refills under clarity — like a real reader
- Actually bounces, stalls, or converts — doesn't just rate your page out of 10
- Different markets surface different friction — because they have different trust baselines
Four layers. One buyer.
Every Buyer Clone Agent is powered by the Cortex Engine — Buyer Clone's multi-layer inference architecture. It builds each agent from four distinct layers before it ever reads a word of your page. The order matters: each layer shapes how the next one is interpreted.
Identity
Role, seniority, sector, company size, and buying authority. A CFO and a developer reading the same page are looking for completely different things — and neither of them should look like a generic "decision-maker."
Culture
Every market has a different trust baseline. Buyers with high ambiguity aversion need more proof before they trust your claims. Pricing opacity that a high-trust buyer overlooks can be a hard exit for a cautious one. Culture is calibrated to the buyer's context and market — not assumed to be neutral.
Personality
Personality governs how a buyer reads — not what they read. A highly conscientious buyer scans every feature card methodically. A high-openness buyer tolerates novel framing that a more conventional reader would dismiss. An anxious buyer burns through their attention budget faster when friction appears. These aren't archetypes — they're behavioural rules mapped from validated personality research.
Occupational lens
Role type determines which sections of your page activate attention — and which sections get skimmed. A buyer with an analytical orientation refills their attention budget when they encounter data and specifics. A commercially-oriented buyer needs ROI signals and peer proof. When a buyer lands on a section that matches their professional orientation, attention recovers. When they don't find it, it doesn't.
Every buyer arrives with a limited amount of patience.
Real buyers don't read every word. They scan with a budget — and that budget depletes under friction and refills under clarity. When it hits zero, they leave.
Every Buyer Clone agent starts a session with a budget shaped by their personality and culture. From there, specific events move the needle in either direction.
Your page doesn't convert everyone the same way.
Run a panel of agents across your buyer types and you'll see the gap between who your page is written for and who it actually needs to serve. Three buyers, same URL, three different outcomes.
Scanned for case studies and quantified outcomes. Found testimonials with no numbers and no names he recognised from his world. Attention depleted at the social proof section. Left before reaching the CTA.
Went straight to social proof, found a result that matched his use case, and skipped features entirely. Budget was near-full when he hit the CTA. Signed up without hesitation — the fastest path to value was obvious.
High trust in the product concept. But she needed integration specifics — API docs, stack compatibility — before she could recommend it internally. The integrations section was too vague. Tab left open, waiting for more.
The brief from this panel surfaces three distinct fixes — a pricing anchor for budget owners, quantified proof points for sceptical evaluators, and integration detail for technical buyers — each mapped to the exact buyer profile it would unblock. Not a list of suggestions. A ranked action plan.
A brief, not a data dump.
The session ends with a prioritised conversion brief — not a wall of agent logs. Each finding is ranked by how many buyers it affected, which profiles triggered it, and what the fix is.
Finance and operations buyers cannot evaluate a tool without a price anchor. "Contact sales" with no starting range is a hard exit for anyone who owns a budget. Adding a starting price or tier structure is the highest-impact single change in this brief.
Testimonials from Marketing and Sales leads read as irrelevant to operations and technical buyers. Adding one peer quote per buyer type — with a specific result, not a vague endorsement — is predicted to recover 3–4 stalls.
A secondary "See a demo" or "Explore the product" CTA would capture the stall pool — buyers who had enough trust to stay, but not enough to commit to a free trial without a softer entry point.
Know your page converts before you stake budget on it.
Paste a URL. Define your buyers. Get a conversion brief in under 10 minutes — before a single real visitor has the chance to leave.
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