# Reviews and Reputation Management with AI > Small Business · AI Course · SB-7 > Source: https://ibl.ai/solutions/small-business/course/reviews-and-reputation-with-ai > Last updated: 2026-08-25 **Monitor every review platform, respond well and quickly, and stay on the right side of the FTC rules on testimonials and incentivized reviews.** ## The Short Answer **Review velocity and recency matter more than star count, and most small businesses monitor one platform. ibl.ai runs monitoring and drafts responses for your approval with no per-seat pricing, deployed where you own all the code and the data — so your customer feedback history stays your own operational asset.** On ibl.ai you own all the code and the data, run it model-agnostic across any LLM, and pay with no per-seat pricing — so you can deploy anywhere, from your own cloud to a fully air-gapped network. [Request Access](https://ibl.ai/contact) · [Explore Small Business](https://ibl.ai/solutions/small-business) ## Course facts - **Level:** Foundational - **Duration:** 3.5 hours across 8 modules - **Format:** Self-paced with a monitoring lab - **Modules:** 8 - **Catalog code:** SB-7 - **Frameworks covered:** FTC endorsement guides, Platform review policies ## What is this course about? Review velocity and recency matter more than raw star count, and most small businesses monitor one platform and respond inconsistently. This course builds monitoring without a paid subscription, a response template that de-escalates, legally compliant review solicitation, and the loop that turns review themes into operational fixes. ## Who is this course for? - Owner-operators managing their own reputation - Marketing and customer experience staff in small firms - Multi-location operators - Franchise managers ### What do I need before starting? - Claimed listings on your main review platforms - No technical background required ## What will I be able to do afterwards? - Explain why velocity and recency beat raw star average - Monitor every relevant platform without a paid subscription - Respond to a negative review in a way that de-escalates - Solicit reviews without violating FTC rules - Turn recurring review themes into operational changes ## What does each module cover? ### Module 1 — Why do velocity and recency beat star count? How review signals actually work, and why a 4.6 with recent reviews outperforms a stale 4.9. _(30 min)_ **Objectives** - Explain the signals that drive review-based ranking - Assess your current velocity and recency - Set realistic improvement targets **Topics:** Velocity · Recency · Volume versus average · Target setting **Activity:** Assess your review velocity and recency against two local competitors. ### Module 2 — How do you monitor everywhere without paying for it? Coverage across the platforms that matter for your business, assembled rather than subscribed. _(35 min)_ **Objectives** - Identify which platforms matter for your category - Assemble monitoring coverage - Set alerting that reaches you promptly **Topics:** Platform selection · Monitoring assembly · Alerting · Coverage gaps **Activity:** Set up monitoring across your top four platforms and verify alerts fire. ### Module 3 — How do you respond to a negative review? The response pattern that de-escalates, and the reflexes that make it worse. _(40 min)_ **Objectives** - Apply a response structure that de-escalates - Avoid the defensive reflexes that escalate - Move the conversation off the platform appropriately **Topics:** Response structure · De-escalation · Defensive reflexes · Moving offline **Activity:** Draft responses to five real negative reviews and have a colleague critique each. ### Module 4 — How do you ask for reviews legally? What the FTC prohibits around incentives, gating, and review solicitation. _(35 min)_ **Objectives** - State what the FTC prohibits in review solicitation - Design compliant solicitation - Avoid review gating **Topics:** FTC prohibitions · Incentive rules · Review gating · Compliant solicitation **Activity:** Audit your current solicitation process against the FTC rules. ### Module 5 — What do you do about fake negative reviews? Detecting reviews that are not from customers and pursuing removal realistically. _(30 min)_ **Objectives** - Detect likely fake reviews - Follow platform removal processes - Respond publicly when removal fails **Topics:** Fake detection · Removal processes · Realistic expectations · Public response **Activity:** Work a removal request through one platform's actual process. ### Module 6 — How do review themes become operational fixes? The loop most businesses skip — treating reviews as an operations signal rather than a marketing one. _(40 min)_ **Objectives** - Extract recurring themes across reviews - Prioritize themes by frequency and severity - Close the loop with an operational change **Topics:** Theme extraction · Prioritization · Operational change · Loop closure **Activity:** Extract themes from a year of reviews and identify the top operational fix. ### Module 7 — How do you show reviews on your own site correctly? Displaying reviews with structured data without misrepresenting them. _(30 min)_ **Objectives** - Implement review structured data correctly - Display honestly, including negatives - Avoid markup that misrepresents **Topics:** Review structured data · Honest display · Misrepresentation risk · Validation **Activity:** Implement and validate review structured data on your site. ### Module 8 — Building the monitor-and-respond workflow The lab module: monitoring, drafted responses, and human approval before anything posts. _(40 min)_ **Objectives** - Build monitoring with drafted responses - Require approval before any response posts - Measure response time and coverage **Topics:** Workflow build · Approval gate · Response time · Coverage measurement **Activity:** Build the workflow and run it for two weeks, approving every response. ## What is the capstone project? **Reputation workflow with an operational fix shipped.** Build monitoring across every relevant platform with drafted responses under human approval, audit your solicitation for FTC compliance, and extract review themes into at least one shipped operational change. _Deliverable:_ A live workflow, a compliance-audited solicitation process, and one operational fix traced to review themes. ## How are learners assessed? - Solicitation audit with every FTC compliance gap remediated - Response drafts critiqued for de-escalation - At least one operational change traced to a review theme ## What ships with the course? - **Facilitator guide.** Session-by-session running order, discussion prompts, and the questions that reliably derail a room. - **Learner workbook.** Exercises, checklists, and the templates each module's activity produces. - **Hands-on lab environment.** A sandboxed ibl.ai deployment so exercises run against real agents, not screenshots. - **Assessment bank.** Scenario questions and rubric criteria mapped to each stated learning outcome. - **Source bibliography.** Every primary regulation and standard cited on this page, linked and dated. ## Which AI agents does this course use? - [Reviews Reputation Agent](https://ibl.ai/solutions/small-business/agent/reviews-reputation-agent) - [Customer Support Agent](https://ibl.ai/solutions/small-business/agent/customer-support-agent) - [Marketing Agent](https://ibl.ai/solutions/small-business/agent/marketing-agent) - [Business Insights Agent](https://ibl.ai/solutions/small-business/agent/business-insights-agent) ## Where does the course material come from? Every module is grounded in primary sources — the regulation, standard, or research itself, not a summary of it. Each was resolved at authoring time. - [Business guidance](https://www.ftc.gov/business-guidance/blog) — Federal Trade Commission. The endorsement and testimonial rules governing review solicitation. - [U.S. Small Business Administration](https://www.sba.gov/) — SBA. Customer experience and reputation guidance. - [Course structured data](https://developers.google.com/search/docs/appearance/structured-data/course) — Google Search Central. Structured data implementation reference for Module 7. - [California Consumer Privacy Act](https://oag.ca.gov/privacy/ccpa) — California Attorney General. Privacy obligations when handling customer contact for review requests. ## Delivery notes Binding guidance for anyone preparing and delivering this course: - Module 4 has real enforcement risk. The FTC has acted against review gating and incentivized reviews, and the course must be specific about what is prohibited rather than gesturing at best practice. - The approval gate in Module 8 is non-negotiable. An auto-posted response to a negative review is a public risk no small business should take. - Module 6 is where this course earns its place. Reputation courses stop at responding; the operational loop is what actually moves the rating. - Be realistic in Module 5 about removal success rates, which are low. Overpromising here produces frustration and undermines the rest. - Coordinate with SB-2 — support quality drives reviews, and the two courses should share the theme extraction. ## Why run AI training on a platform you own? - **You own the course, not a licence to it.** Course content, learner data, and the platform run inside your perimeter — you own all the code and the data. - **Model-agnostic delivery.** Run the course's AI components on any LLM — Claude, GPT, Llama, Gemini, Command — and switch anytime. - **No per-seat training licences.** Usage-based or self-hosted, so cost tracks actual use rather than headcount. - **Deploy anywhere.** Cloud, private VPC, on-premise, or fully air-gapped — including for cohorts that cannot use public AI tools. ## Frequently asked questions ### What does the Reviews and Reputation Management with AI course cover? Review velocity and recency matter more than raw star count, and most small businesses monitor one platform and respond inconsistently. This course builds monitoring without a paid subscription, a response template that de-escalates, legally compliant review solicitation, and the loop that turns review themes into operational fixes. It runs 3.5 hours across 8 modules across 8 modules, at foundational level, and closes with a capstone: Reputation workflow with an operational fix shipped. ### Who should take Reviews and Reputation Management with AI? It is written for Owner-operators managing their own reputation, Marketing and customer experience staff in small firms, Multi-location operators, Franchise managers. Prerequisites: Claimed listings on your main review platforms; No technical background required. ### Can we run this course on our own infrastructure? Yes. ibl.ai is model-agnostic and deploy-anywhere — cloud, private VPC, on-premise, or fully air-gapped — and you own all the code and the data. Cohort data, submissions, and any material learners upload stay inside your perimeter, which matters for small business teams that cannot send work to a public AI tool. ### How do we get access to Reviews and Reputation Management with AI? Request access and we will set it up for your cohort — hosted by ibl.ai, or running against your own deployment. Tell us the group size and timing you need, and whether it should run inside your own perimeter. ### How much does AI training for small business cost on ibl.ai? There is no per-seat pricing — you pay for usage or self-host and pay only for the infrastructure, so a 5,000-person rollout does not cost 5,000 licences. 1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University. ## More Small Business courses - [Your First Five AI Agents in Thirty Days](https://ibl.ai/solutions/small-business/course/first-five-ai-agents-in-30-days): A practical sequence for an owner-operator with no IT department — which five agents to deploy first, in what order, and how to tell within a month if each earns its keep. - [AI Customer Support That Does Not Sound Like a Robot](https://ibl.ai/solutions/small-business/course/ai-customer-support-not-a-robot): Automate the first response without losing the relationship — tone, escalation thresholds, and the questions a small business should never let an agent answer. - [AI Bookkeeping and Cash-Flow Forecasting for Owner-Operators](https://ibl.ai/solutions/small-business/course/ai-bookkeeping-cash-flow): Use AI for categorization, reconciliation, and a cash-flow forecast you can trust — plus a clear line for where your accountant still has to sign. - [Lead Follow-Up Agents: Responding in Minutes, Not Days](https://ibl.ai/solutions/small-business/course/lead-follow-up-agents): Speed-to-lead is the highest-leverage automation a small business can make — answer every inquiry immediately without sounding automated or breaking consent law. - [AI Marketing for Local Business: Content, Search, and Ads](https://ibl.ai/solutions/small-business/course/ai-marketing-for-local-business): Produce marketing that ranks locally and does not read as generated — local search fundamentals, review-driven content, and ad testing on a small budget. - [Quoting and Estimating with AI for Trades and Services](https://ibl.ai/solutions/small-business/course/quoting-estimating-with-ai): Get accurate quotes out the same day — pulling from your historical jobs, current material pricing, and margin rules, with your approval before anything goes out.