ReviewShield is the complete build-to-launch blueprint for an AI reputation-protection SaaS — a risk-scoring engine that flags suspicious and extortion-pattern reviews, an evidence vault that documents everything, an AI response assistant, and a guided reporting workflow grounded in current FTC guidance and real platform policy.
Consumers now read reviews before choosing almost any local business — and a small but real number of bad actors have learned to exploit exactly that dependence.
Figures verified against BrightLocal's Local Consumer Review Survey (2026) and the FTC's published rule — full citations with URLs and dates are inside the book's Sources & Further Reading chapter. An older, commonly-cited "82% of consumers read reviews" figure is now dated; this book explains why and uses the current number throughout.
Everything you need to answer: if I wanted to build a real reputation-protection SaaS, what exactly would I build first, and how would I sell it?
A 0-100 scoring model combining extortion language, timing anomalies, and content-similarity signals — never presented as proof.
Direct and indirect indicators of payment demands, coordinated attacks, and review-bombing patterns, combined rather than keyword-matched.
Timestamped, exportable case files for screenshots, messages, invoices, and job records — built for a real reporting workflow.
Seven response modes, from a warm thank-you to an extremely careful potential-extortion reply — with a strict "never accuse" rule.
Grounded in the 2024 FTC rule and Google/Yelp/Trustpilot/Meta policy — with a monthly Compliance Health Check.
API-only ingestion, a risk engine, a 17-entity database schema, and a full system diagram.
From a solo plumber to a multi-location HVAC company, a beauty studio, a dental practice, and a pest-control team.
A 3-tier SaaS ladder ($39/$69/$99), unit economics, and revenue projections from 100 to 10,000 customers.
A month-by-month plan from validation to scale, plus a tactical first-10-customers playbook.
A 3-person shop that lives on referrals and reviews.
20 employees, several locations to monitor at once.
Two locations, highly visual and review-sensitive.
4 chairs, patient trust tightly tied to online reputation.
A small dispatch team covering a wide service area.
The book includes the full scoring formula, the five risk bands, and an example dashboard, screen by screen.
Reviews are ingested through official platform APIs and analyzed for language patterns, timing anomalies, and extortion indicators.
A 0-100 risk score is generated and, for anything above Watch, the Evidence Vault starts building a timestamped case file automatically.
The AI drafts a response matched to the review type — never accusatory, never a guess dressed up as fact — for the owner to approve or edit.
For policy-violating content, ReviewShield assembles a case summary and the correct reporting pathway, then tracks the platform's response.
Secure checkout · 7-day money-back guarantee
Detection, response-drafting, marketing, sales, and operations prompts — organized by job to be done.
Risk-score calibration, evidence-capture, monthly audit, and MVP-planning worksheets.
Buildable UI/UX specs for every core screen, from sign-up to billing.
Eight named risks — false accusation to data breach — each with severity, probability, and mitigation.
An honest comparison against Birdeye, Podium, Reputation, NiceJob, Grade.us, GatherUp & GoHighLevel.
Including the direct, honest answer to "does this guarantee a review gets removed?"
Plus 4 funding scenarios, from bootstrapped to a $500K launch.
Glossary for beginners, a tool/platform cheat sheet, and a day-by-day First 30 Days plan.
This is a newly published title — reviews are on their way.
Awaiting first reader review.
Awaiting first reader review.
Awaiting first reader review.
This is a complete written blueprint — product spec, technical architecture, business model, and launch plan. It is not source code or a working application.
No — and the book is explicit about this. No platform guarantees removal of any review, and this book never claims otherwise. ReviewShield's role, as designed, is to help identify potentially fraudulent or policy-violating content, document evidence, and streamline reporting — the platform makes the final call.
No. The 2024 FTC Consumer Reviews and Testimonials Rule bans specific deceptive review practices and is enforced through FTC actions against businesses — it does not create a private right to sue, and the FTC does not operate an individual review-takedown service. The book explains this clearly in Chapter 15.
No. The book is written for a first-time, non-technical founder and includes a no-code/low-code build path alongside the custom-code option.
Every financial figure, formula, and projection is clearly labeled as an "Assumption" or "Illustrative estimate" — the book is explicit about the difference between verified facts and worked examples.
An instant-download PDF and DOCX, 188 pages, delivered immediately after checkout.
No. The book explicitly recommends engaging qualified legal, financial, and regulatory counsel before launching a real product.
BrightLocal's Local Consumer Review Survey, the FTC's published rule and Federal Register notice, and official platform policy pages for Google, Yelp, Trustpilot, and Meta — every figure is cited with a URL in the Sources & Further Reading chapter, and unverifiable claims are labeled as such rather than repeated as fact.
If ReviewShield: The AI Reputation Protection System for Small Businesses isn't useful to you, email sales@viralbydesign.co within 7 days of purchase for a full refund — no questions asked.
Get the complete ReviewShield product, technology, and business blueprint today.
Get Instant Access — $12.99 →