About PerfectFinder

We built a better way to find the right thing—no guessing, no gaming, no regrets.

Our Mission

Every day, millions of people waste time, money, and trust trying to find the right product, service, or decision. They face:

3,000-item search results with tiny filters
Promoted listings and ads obscuring the best matches
No clear "why this is best" explanation
Returns, buyer's remorse, and choice paralysis

We created PerfectFinder to solve this: an AI-powered search agent that understands what you need in plain language and finds mathematically optimal matches—with complete transparency about why each result is perfect for you.

How we're different

Mathematically optimal

Not "pretty good" or "90% accurate." The best match for your preferences, guaranteed by our patent-pending preference-reasoning technology (US 2025/0298806 A1).

Millisecond performance

Near-linear time complexity means real-time results even on millions of candidates. Fast enough for live search.

Fully explainable

Every decision comes with level-by-level rationale. No black-box ML, no "trust us." Perfect for compliance and building user trust.

Manipulation-proof

Sellers can't game the system by adding irrelevant features or keyword-stuffing. Your priorities always stay on top.

The Inventor & Founder

Emanuele Di Rosa, PhD

Founder & CTO, AI Solutions

Dr. Di Rosa's vision: a world where people describe what they are looking for in plain language and get provably optimal search results with clear explanations. No black-box recommendation engines, no pollution because of goods returns, no speculative shopping, no buyer's remorse.

The Technology

At its core, PerfectFinder uses our patent-pending preference-reasoning technology that, between others, solves three fundamental problems:

1. Manipulation resistance: Sellers can't boost rankings by adding irrelevant features or keyword-stuffing. Only improvements at the right priority level matter.

2. Explainability: Every decision comes with a level-by-level audit trail. Users and regulators can see exactly why each option won or lost.

3. Performance: Polynomial-time complexity (near-linear in practice) enables real-time use even on massive candidate sets.

The system supports Boolean formula constraints, positive and negative preferences, co-optimal result sets, and personalized DAG configurations—all while maintaining provable optimality guarantees.

Let's build something together

Whether you're a marketplace, real estate listing platform, compliance-heavy industry, or any business where customers need to find the perfect match—we'd love to explore how hyper-personalized AI search can transform your users' experience.