How businesses got named by AI in detail.

Background

An established HVAC company with steady demand from search and referrals, but no read on how AI assistants represented it when prospective customers asked for a recommendation.

The challenge

Its presence in AI answers was unstable: named in one run, gone the next, across ChatGPT, Claude, Perplexity, and Google AI. It sat just outside the top group, and two competitors were steadily pulling ahead. Without a stable, repeatable measurement, there was no way to tell whether any change was actually working.

What we did

We treated AI visibility as a measurable system, not a one-off check:

• Locked down consistent business facts, services, service areas, and hours everywhere the models read them
• Added structured service and service-area data so each offering was machine-readable
• Built content answering the exact “best [service] near me” and comparison questions buyers ask AI
• Tracked a fixed prompt set every week to separate real movement from run-to-run noise
• Benchmarked share of voice against the specific competitors AI named instead

Results

Over four months the company's AI visibility rose about five points and settled into the top three across all four models, while the average competitor's visibility fell about five points. The gain held week to week — a defendable position rather than a one-run spike.

Background

A construction company on a brand-new domain, with no search history and no domain authority — a capable business with real projects, but effectively invisible to the systems customers increasingly ask for recommendations.

The challenge

At the start it scored 0% AI visibility: no assistant mentioned it for any core service, and nothing on the wider web pointed to it from the sources the models trust. With a zero-authority domain, there was no foundation for AI to draw on.

What we did

We built the foundations from the ground up, in sequence:

• Published clean, structured business and service information the models could parse
• Created authority content around its services, projects, and service areas
• Earned citations on trusted, relevant sources AI reads when recommending contractors
• Established entity data so the business is recognized as a distinct, real entity
• Tracked domain authority and citation growth alongside AI visibility for a measurable trail

Results

Within four months the company moved from 0% to about 5% AI visibility, began earning citations across AI chats, and built real domain authority from a standing start — a base that keeps compounding with every new project and mention.

Background

A second HVAC business that was already lightly present in AI — around 4% visibility — but carrying a hidden liability beneath that number.

The challenge

The models were stating inaccurate facts about it — wrong service details and outdated specifics — at the exact moment a customer was deciding who to call. Low presence was the smaller problem; being described incorrectly was the costly one.

What we did

We fixed accuracy and presence together:

• Traced where each incorrect claim originated and corrected it at the source
• Rebuilt the service and company information into clear, current, structured facts
• Earned fresh citations across ChatGPT, Perplexity, and Google AI so models had accurate, attributed data to cite
• Re-tested the same factual prompts over time to confirm the corrections held

Results

Over four months AI visibility more than tripled, from 4% to 14%, and the share of answers stating correct facts rose from roughly a third to almost all. Presence and accuracy moved together, so more visibility meant more correct visibility.

Background

An upholstery service with a healthy AI presence built up over time — until a domain migration and a restructuring of its service lineup changed everything overnight.

The challenge

The migration and restructure knocked it out of AI answers: visibility collapsed to about 2% and the models lost track of what the business now did, mapping it to the wrong services or none at all. Hard-won presence was at risk of being lost for good.

What we did

We ran it as a controlled recovery, not a rebuild:

• Mapped old-to-new URLs and implemented clean 301 redirects to preserve equity
• Rebuilt the service architecture so each restructured offering was distinct and machine-readable
• Re-established the entity and service signals the models use to match a business to a query
• Prompted re-indexing and monitored how each model re-learned the business
• Verified the models described the new service lineup correctly

Results

Within three months visibility recovered to about 10%, with AI again correctly representing the restructured services. A migration that could have been a lasting setback became a clean reset — on a clearer structure than before.

Background

A destination restaurant — well known locally and busy on weekends, but reliant on word of mouth and reviews rather than search or AI.

The challenge

When diners asked AI for recommendations in its cuisine niche, the restaurant was invisible. Competitors with stronger structured data and review profiles owned the AI shortlist — some with a weaker in-person reputation.

What we did

We focused on the queries diners actually ask AI:

• Built cuisine- and occasion-specific content matched to real AI prompts
• Aligned listings, menus, and structured data across platforms
• Strengthened and surfaced the review signals the models weigh
• Created comparison and “best [cuisine] near me” pages
• Tracked share of voice against named competitors weekly

Results

Share of voice in the niche climbed from about 6% to 17% over four months, moving the restaurant into the set of places AI suggests first, while the leading competitors' share edged down.

Background

A 24/7 locksmith whose business depended on urgent, high-intent demand — the lockout at midnight, the lost keys before work.

The challenge

For exactly those emergency queries it barely registered in AI answers — around 3% visibility — so at the moment a customer needed a name fastest, AI rarely gave them this one.

What we did

We targeted high-intent, emergency-led visibility:

• Built emergency-intent content matched to urgent AI prompts
• Corrected and unified local listings across platforms
• Earned citations on the sources AI cites for service recommendations
• Strengthened review and availability signals — 24/7 and response time
• Tracked appearance across all four models for emergency queries

Results

AI visibility rose from about 3% to 12% in three months. The business began appearing for emergency queries across all four major AI tools, and monthly citations grew from a single mention to eight.