Why we built this
A potential customer visits a clinic website. She spends six minutes reading about a treatment, checks the pricing page twice, hovers over the booking button — and leaves. She comes back two days later. The site has no idea she was ever there. Same hero image, same generic copy, same starting point. She leaves again. That's not a user problem. That's a broken system.
The web was built to serve pages, not to understand the people reading them. Analytics can tell you someone visited. They can't make the next visit different. Cookies track sessions, not intent. There's nothing in the stack that lets a website adapt to what each person actually cares about — in real time, across visits, without manual rules or guesswork.
We started in aesthetic medicine because the cost of this gap is measurable. These are $200–$5,000 decisions. Visitors arrive with real intent — they've already searched, already compared. They spend 4–8 minutes on-site and leave without booking 96% of the time. Not because they lost interest, but because the site never responded to it.
Across three partner clinics, the adaptive system produced a 3.2x booking lift over static pages — same traffic, A/B tested, Jan–Mar 2026. It didn't work because of better copy or a smarter chatbot. It worked because the site finally remembered who was visiting and why.
Built to be verified, not believed
We make specific, measurable claims because we want partners who hold us accountable. Every metric below comes from live deployments, not projections.
Median adaptation latency
Time from visitor arrival to fully adapted page content. Measured across all three partner sites, Jan–Mar 2026. The system reads behavioral signals — scroll depth, hover patterns, referral source — and restructures content before the visitor consciously registers a delay. Below the 100ms threshold where humans perceive interface lag.
Conversion lift in early testing
Visitors who experienced the adaptive system booked appointments at 3.2x the rate of the static control group. Same traffic sources, same services, same pricing. The only variable was whether the site adapted to what each visitor was looking for. A/B tested across 3 clinics, statistically significant at p < 0.05.
Cross-business data exposure
Individual visitor data is architecturally isolated per business. No visitor profile, browsing history, or personal information ever moves between partner sites. What flows across the network are aggregate, anonymized patterns — which content formats convert, which objections appear most often, which engagement signals predict booking intent. The privacy boundary is enforced at the infrastructure level, not by policy.
Dedicated model per business
Each partner site gets its own adaptive model, trained exclusively on that business's data — services, pricing, availability, brand voice, and visitor behavior patterns. The shared network provides foundational intelligence, but the model that runs on your site is yours. It speaks in your tone, knows your calendar, and responds to your specific customer patterns.
US-based. Compliant by design.
US-based cloud infrastructure with data residency guarantees. All visitor data is processed and stored within US data centers.
Designed for GDPR, CCPA, and HIPAA from the architecture level. Consent management, data portability, and right-to-deletion are built into every deployment.
End-to-end encryption in transit and at rest. Role-based access controls. Audit logging on all data operations. No third-party data sharing.
Every partner gets access to a Trust Center where visitors can see what data exists, manage permissions, and request deletion. Nothing is hidden.
Non-negotiables
We earn when you earn
Our revenue model is commission-based. We only make money when the AI directly contributes to a booking. No setup fees, no monthly retainers, no contracts. If the system doesn't produce measurable results, the business pays nothing. This isn't generosity — it's alignment. Our incentive is to make the system work, not to sell licenses.
Privacy is structural, not legal
Most companies treat privacy as a compliance exercise — a document lawyers write before launch. We treat it as an engineering constraint. Data isolation between businesses is enforced at the infrastructure level. Consent is collected before data is processed, not retroactively. Deletion requests are executed immediately, not queued. The Trust Center exists because we believe visitors should see exactly what we know about them.
The network compounds
Every site that joins the Cerebrance network makes the AI more effective for every other site. Aggregate patterns — what content converts, what questions visitors ask, what objections come up — flow across the network while individual data stays isolated. This compounding effect is our core advantage, and it's one that can only be built over time through real-world deployment, not purchased or replicated.
Generic AI doesn't book customers
We've studied what happens when businesses deploy generic chatbot widgets. Visitors recognize them immediately, disengage, and leave with a worse impression than if there had been no AI at all. The reason Cerebrance works is specificity — each deployment knows the business's services, pricing, availability, brand voice, and customer patterns. It doesn't sound like AI. It sounds like the business.
Talk to the founder directly
We're pre-seed and working directly with every early partner. There's no sales team and no demo queue. If you're evaluating Cerebrance, you'll talk to the person who built it.