Public real-time and historical activity data establish a counterfactual baseline for every business in our network. Sufficient on its own to produce a verified lift count.
The verification rails
for local commerce.
A $200 billion advertising market runs without a measurable outcome. We've built the layer that fixes it — sensor-optional, hash-anchored, and engineered to make ad fraud expensive and hard to scale — and we charge for it on a performance basis: a percentage of revenue from the new customers we measurably drive, priced to the confidence of the measurement. Low spend at risk. Low onboarding friction. Aligned by design.
A market that pays for proof it never receives.
Local businesses — salons, restaurants, fitness studios, dentists, contractors — spend over $200 billion a year on Google, Meta, and platform ads. They get impressions, clicks, and vague reports. They do not get an honest answer to the only question that matters: did this spend bring more customers through the door?
So the market is broken in two directions. Cautious owners underspend because they don't trust the channel. Aggressive owners overspend and burn cash. Neither has any way to know. The whole channel runs on faith.
Pay-for-performance, structurally.
We run local campaigns that pair neighborhood creators with Sparkor, our geo-fenced ad engine that boosts a creator's post to nearby locals and delivers most of the reach. The resulting lift is estimated against a baseline using whatever signals the business chooses to share — at minimum, public baseline data, which is enough to produce a confidence-scored lift estimate. On-site sensor, POS access, and delivery feed are strongly recommended: they tighten the estimate and prevent disputes when the lift is small or noisy. Businesses operating on baseline alone accept a small premium that covers that dispute risk. Every verified visit is cryptographically signed and hash-anchored — tamper-evident regardless of signal mix.
The business pays us a percentage of the revenue from the incremental customers we drive, and our fee steps down as verified lift falls — so we earn our full share only when the result is real. No impressions billed. No clicks. Just measured lift.
This is the unlock. Marketing spend stops being a fear-based decision and becomes a clean ROI calculation. Owners become willing to spend more because spend is tied to confidence-scored, verified lift rather than impressions. Creators get paid for performance they can demonstrate. We only win when our customers win.
Verification at any signal level.
Businesses may grant access to on-site sensor data, POS, or delivery feed. These inputs make the lift unambiguous and protect both sides from disputes when the lift is small or noisy.
Businesses on baseline data alone accept a small premium reflecting the real risk that ambiguous lift signals lead to disagreement. With high-signal inputs the lift is unambiguous and the premium is removed.
Triangulating independent signal classes makes fake reviews, bot views, click fraud, and most known forms of attribution gaming expensive and hard to scale, regardless of signal depth.
Today's revenue. Tomorrow's dataset.
Every signed visit is a verifiable fact about that business: foot traffic patterns, transaction sizes, repeat customer rates, peak hours, service durations. Across thousands of businesses, this becomes one of the most accurate ground-truth datasets of local commerce. The dataset compounds with every campaign. It is not scrapeable, not gameable, and not reproducible without our network.
The trust source for local commerce.
Every major LLM today is guessing at local. ChatGPT, Gemini, Perplexity — when a user asks “who's the best near me?”, they stitch together gamed reviews and stale web data. Within a few years they will need a verifiable source to answer honestly. VerifyLocal is building to be that source. The pay-for-performance business funds the network. The dataset compounds underneath it. The API endpoint is the long-term moat.
Architecture protected at the foundation.
A portfolio of U.S. provisional patent applications filed April–May 2026 by inventor Xiaotong (Tony) Wang, PhD, Intelligent Systems Control & Design — covering AI inference orchestration with Trust Token escrow, IoT-verified foot traffic and Local Trust / Footprint Scoring, cross-platform attribution with hash-chain integrity, and the Trust Badge publication and reputation-monitoring layer. The applications are assigned to VerifyLocal LLC. Consolidated non-provisional filing targeted for April 2027.
Built in Connecticut. Pre-Series A.
Six-person full-time team — operators and engineers — building from Milford, CT. Pre-Series A, raising a bridge round to set up a strong Series A.
A CEO with public-company experience to scale alongside the founding team.
Pre-Series A capital partners aligned with the trust-infrastructure thesis.
Strategic introductions to local commerce, identity, marketplace, and trust & safety operators.
Bull-case market analysis, full deck, IP summary, and pilot data available under NDA.
Let's talk.
Xiaotong (Tony) Wang, PhD
Founder
Alex Hurowitz
CTO
Investor inquiries: Tony. Engineering & technology: Alex.