Be the business ChatGPT recommends.

Background

Cascade Comfort Heating & Air is a family-owned HVAC contractor founded in 2009, serving Bend, Redmond, and Sisters across Central Oregon with a team of 14 technicians. Roughly 80% of its revenue came from paid search and repeat customers, and when homeowners asked an AI assistant for a recommendation, the company simply never came up.

The challenge

Testing how AI assistants answered local questions — "best HVAC contractor in Bend," "who should I call for emergency furnace repair Bend," "most reputable heat pump installer Central Oregon" — national franchises were recommended every time. Cascade was absent: it had few recent reviews, inconsistent listings across directories, and service pages that did not answer the questions homeowners actually asked.

How RankSimply did it

1. 1

   Baseline AI recommendation scan

   RankSimply scanned 48 high-intent local prompts across ChatGPT, Gemini, Claude, Perplexity, and Google AI Overviews, established a 0/48 baseline, and reverse-engineered why the franchises were recommended — review volume, consistent listings, and answer-shaped pages.

2. 2

   Reviews and citations rebuilt

   Automated review requests went out after every completed job to lift recent review velocity; NAP details were corrected across 60+ directories AI engines use to verify a business; and the Google Business Profile was filled out with services, hours, photos, and Q&A.

3. 3

   Answer-shaped pages and schema

   Service and cost pages were rewritten to answer how Central Oregon homeowners actually search — rebates, mini-split costs, why a furnace short-cycles — and marked up with LocalBusiness and FAQ schema so assistants could read and cite them cleanly.

Timeline

• Month 1

  48-prompt baseline scan returns 0 recommendations; review and citation gaps mapped.

• Month 2–3

  Review velocity climbs; listings corrected across 60+ directories; Google Business Profile rebuilt.

• Month 4

  First AI recommendations appear in ChatGPT and Google AI Overviews for furnace and heat-pump questions.

• Month 6

  Recommended in 34 of 48 prompts; AI and organic become the #1 source of booked calls and paid budget is cut 60%.

Background

Riverstone Family Dental operates two offices in Chattanooga, Tennessee with six dentists, offering family dentistry, cosmetic work, and dental implants. New patients had historically arrived through word-of-mouth and Yelp, but referral volume had been flat for two years while a growing share of prospective patients said they had "asked an AI" before booking.

The challenge

When the practice tested how AI assistants answered local questions, the results were stark: across 40 high-intent prompts such as "best dental implants in Chattanooga," "how much do veneers cost in Chattanooga," and "emergency dentist near downtown Chattanooga," Riverstone was named zero times. Competitors with thinner clinical credentials were the ones recommended, because their pages answered the questions directly and were structured for machine reading.

How RankSimply did it

1. 1

   Baseline citation tracking

   RankSimply scanned all 40 prompts across ChatGPT, Perplexity, Claude, and Gemini, establishing a 0/40 baseline and identifying exactly which competitors the AI models defaulted to and why.

2. 2

   Rewrites with attributed claims

   Service pages were rewritten to lead with a direct answer, then back it with attributable facts: board certifications, the 3,000+ implants placed since 2014, transparent cost ranges ($1,800–$3,200 per implant in the Chattanooga market), and recovery timelines. Each page added FAQPage structured data so models could extract clean question–answer pairs.

3. 3

   Re-scan until recommended

   Rewritten pages were re-scanned weekly. Pages still below the readiness threshold were revised again — tightening the answer, adding comparison tables, and citing sources — until the assistants began recommending the practice by name.

Timeline

• Month 1

  40-prompt baseline scan returns 0 recommendations; competitor answer patterns reverse-engineered.

• Month 2

  Implant, veneer, and emergency pages rewritten with direct answers + FAQ schema.

• Month 3

  First recommendations appear in Perplexity and ChatGPT for implant and cost queries.

• Month 4

  Recommended in 27 of 40 prompts; 19 new patients report finding the practice via an AI assistant.

Background

Móraga & Bell Injury Law is a four-attorney personal injury firm in Tucson, Arizona, founded in 2017. It competes against several high-spend "billboard firms" that publish aggressively across every emerging practice area. With no in-house content team, the firm kept losing AI and search recommendations to competitors before it even knew they had moved.

The challenge

When prospective clients asked ChatGPT and Gemini "best lawyer for an e-bike accident in Tucson" or "who handles rideshare crash claims in Arizona," the billboard firms were recommended — because they had published answer-shaped pages first. Móraga & Bell typically discovered a competitor's new page 11 days later, long after the AI models had already learned to name someone else.

How RankSimply did it

1. 1

   Continuous competitor and AI monitoring

   RankSimply watched six competitor websites, the Tucson SERP, and how AI assistants answered 30 local injury prompts on a continuous loop — flagging the moment a rival began getting recommended for a new accident type.

2. 2

   Same-day, statute-cited counter-pages

   When a competitor published an "e-bike accident" page, the agent alerted the firm within hours and auto-drafted a more comprehensive, machine-readable counter-page — citing the relevant Arizona statutes (A.R.S. § 28-819 on e-bike classification), local settlement ranges, and FAQ schema — queued for attorney review the same day.

3. 3

   Reviews plus attorney-reviewed publishing

   Client review growth ran alongside, and every counter-page passed an attorney's sign-off before going live — so the pages AI learned from were both trusted and legally accurate.

Timeline

• Week 1

  Agent begins monitoring 6 competitors, the Tucson SERP, and 30 AI injury prompts; baseline 0 recommendations.

• Week 3

  Detects a competitor's new rideshare-accident page within hours; counter-page reviewed and live the same day.

• Week 6

  AI assistants begin recommending the firm for "Arizona e-bike accident law."

• Month 5

  Recommended in 22 of 30 prompts; qualified leads up 127% and two billboard firms out-recommended.