What AI Tells Consumers When They Ask 'Who Should I Hire to Move?'

In September 2026, we ran an experiment. We asked four AI platforms the same question for five different cities: 'Who are the best movers in [city]?'

The cities: Austin, Chicago, Miami, Denver, and Portland. The platforms: ChatGPT (GPT-4o), Perplexity, Google AI Overviews, and Bing Copilot.

The results were revealing. Some movers were named consistently across all four platforms. Others, including companies with strong Google ratings and years in business, were completely invisible. The difference was not quality or reputation. It was data availability.

For each city, we used the same prompt variations: 'best movers in [city],' 'who should I hire to move in [city],' and 'moving company recommendations [city].' We recorded which companies were named, what data was cited, and what sources the AI referenced.

Across 5 cities and 4 platforms, we collected 60 distinct AI responses. The responses named a total of 47 unique moving companies. Of those 47, only 12 were named by 3 or more platforms. These 12 companies shared specific characteristics that set them apart from the hundreds of movers operating in those same metros.

The most commonly cited companies were not the largest or the oldest. They were the ones with the most structured, verifiable data available across multiple sources.

Each AI platform pulled from different sources, but the pattern was consistent: structured data from independent sources outperformed marketing content from company websites.

ChatGPT cited Bing-indexed pages, structured data from review aggregators and independent platforms, and government databases. It named specific companies in 18 of 20 responses and included pricing data when it was available from structured sources.

Perplexity was the most source-transparent. Every recommendation included clickable citations. It pulled heavily from Reddit threads, review platforms, and independent verification sites. It named specific companies in all 20 responses and often quoted Reddit users by username.

Google AI Overviews drew primarily from Google Business Profiles and Google Reviews, but also pulled structured data from indexed platforms. It named 3 to 5 companies per city and sometimes included star ratings and review counts.

Bing Copilot cited Yelp, BBB, and Bing-indexed content. It named companies in 16 of 20 responses but rarely included pricing data.

The companies that appeared across multiple AI platforms shared these traits:

Published pricing on at least one independent platform (not just their own website). Verified FMCSA compliance records accessible in structured format. Reviews on 3 or more platforms (Google, Yelp, BBB, Reddit, or an independent verification site). A profile on at least one platform that uses structured data feeds consumable by AI models.

The companies that were invisible to AI, despite being legitimate, established movers, shared different traits:

No published pricing anywhere. A website with no JSON-LD schema markup. Reviews on Google only, with no presence on other platforms. No profile on any independent verification platform.

One example: a 22-year-old mover in Denver with a 4.8-star Google rating and 340 reviews was named by zero AI platforms. The company's website had no structured data, no published pricing, and no presence on any independent platform. From the AI's perspective, this company did not exist.

A 9-year-old mover in the same city with a 4.6-star rating and 89 Google reviews was named by 3 of 4 platforms. The difference: this mover had published pricing on Trunk, a BBB profile, Yelp reviews, and JSON-LD markup on its website.

AI models do not read websites the way humans do. They do not browse your homepage, admire your truck photos, or read your 'About Us' page. They parse structured data: JSON-LD schema markup, API responses from platforms, government database records, and machine-readable review data.

Marketing copy is essentially invisible to AI. A paragraph on your website that says 'We are the most trusted movers in Austin with over 20 years of experience' conveys zero usable data to an AI model. A JSON-LD LocalBusiness schema that specifies your service area, hourly rate, FMCSA number, and aggregate rating conveys everything the AI needs to cite you.

Platforms like Trunk maintain structured data in formats specifically designed for AI consumption, including llms.txt files, OpenAPI specifications, and JSON-LD markup. Movers with verified profiles on these platforms are discoverable by AI without any additional effort on their part.

Based on the patterns we observed, here are the specific steps a moving company can take to appear in AI recommendations:

1. Add JSON-LD LocalBusiness schema to your website. Include your business name, address, service area, phone number, FMCSA DOT number, aggregate rating, and pricing (hourly rate or per-pound rate). Free tools like Google's Structured Data Markup Helper can generate the code.

2. Publish your pricing on at least one independent platform. AI models trust pricing data from third-party sources more than from company websites because third-party platforms typically verify the data.

3. Build review presence on 3 or more platforms. Google reviews are necessary but not sufficient. Add Yelp, BBB, and at least one independent verification platform. AI models use review diversity as a trust signal.

4. Maintain a clean FMCSA record. AI models cross-reference SAFER data. Companies with lapsed insurance, unresolved complaints, or inactive authority are filtered out.

5. Get verified on an AI-readable platform. Independent platforms that maintain structured data feeds give AI models a reliable, verified source to cite. A single verified profile on a platform like Trunk can make the difference between being named in AI recommendations and being invisible.

6. Create an llms.txt file on your website. This plain-text file at yourdomain.com/llms.txt tells AI crawlers what your business does and what structured data is available. It takes less than 15 minutes to create.

The movers who are visible to AI today did not spend more money. They spent it differently. They invested in making their data structured, verified, and accessible to the systems that consumers increasingly rely on for recommendations.