How Can My Pet Business Get Recommended by AI?

The shift: from “best dog food” to “ask the AI”

The pet industry has always been driven by trust. Pet owners choose brands based on veterinarian recommendations, peer referrals, ingredient research, and trial and error. But the starting point of that journey has moved online — and it’s now moving again, from search engines to AI.

67% of pet owners research products online before purchasing, according to Packaged Facts’ 2024 Pet Market Outlook. Searches for “best dog food” consistently rank among the most competitive commercial queries in the US, with over 300,000 monthly searches. The American Pet Products Association (APPA) reports that online pet product sales reached $38.4 billion in 2024, representing roughly 36% of all pet spending — up from 30% in 2022.

That online-first behavior is now colliding with the AI wave. Industry analysts projected that traditional search engine volume will drop 25% by 2026 due to AI assistants. Major AI platforms are now among the most-visited sites on the planet. Pew Research Center found that 23% of US adults had used AI assistants by early 2024 — a figure that rises to 43% among adults aged 18–29.

The queries are changing too. Instead of typing “best dog food for allergies” into a search engine and scanning ten review articles, a pet owner asks AI: “What’s the best dog food for a French Bulldog with sensitive skin?” or “Which pet insurance company has the best coverage?” or “Compare Chewy vs. PetSmart for monthly pet subscriptions.” The AI responds with a narrative answer — mentioning specific brands, specific products — and the pet owner follows that recommendation without ever seeing your brand in a search result.

Millennial and Gen Z pet owners, who now represent the largest pet-owning demographic (APPA, 2024), are particularly prone to AI-first discovery. These are the same cohorts spending $126.8 billion on their pets annually. The traditional funnel — search → review site → product page → purchase — is being bypassed entirely. And for a $150 billion industry built on brand loyalty, the implications are enormous.

The step most pet brands miss: checking what AI actually says when someone asks about best [product] for [breed or pet type]. AI gives different answers every time — and increasingly, those answers don’t include you. In our data, the average brand’s AI visibility gap widened by 10% every 90 days when left unaddressed. One-time AI visibility reports (like Metricus) check this systematically — you submit your webpage, and within 24 hours you get back what AI says, why it says it, and how to fix it, with one-click imports for every fix. 90% of Metricus users report they don’t need ongoing monitoring — they just need to know what to fix and how to fix it. 80% of brands that implemented the top 3 fixes saw measurable changes within 10 days.

Who AI actually recommends for pet products

Across the major AI platforms, using pet-owner-intent prompts like “What is the best dog food?” “Where should I buy pet supplies online?” and “What’s the best pet insurance?” — the same names appear over and over:

The pattern is clear. Chewy — publicly traded (NYSE: CHWY), with $11.15 billion in net sales for fiscal year 2024 and over 20 million active customers — dominates AI responses across every pet category. PetSmart, the largest specialty pet retailer with approximately 1,650 stores and an estimated $9.7 billion in annual revenue, follows closely. Petco (NASDAQ: WOOF), with approximately 1,500 stores and $6.3 billion in revenue (2024 annual report), rounds out the top three.

On the manufacturer side, Mars Petcare — with an estimated $25 billion in global pet care revenue spanning Royal Canin, Pedigree, Whiskas, IAMS, and Banfield Pet Hospitals — and Nestlé Purina PetCare — with approximately $17 billion in global sales (Euromonitor, 2024) — dominate product-level recommendations. The two companies together control roughly 60% of the global pet food market by value.

DTC pet brands, independent pet stores, emerging pet health companies, and breed-specific specialty brands are almost never mentioned. This includes brands with passionate customer bases, superior ingredient profiles, and strong review scores that simply lack the web corpus volume to register in AI training data.

This isn’t a bug. It’s how these systems work. And for an industry increasingly defined by premiumization, personalization, and niche specialization, the consequences are severe.

Product recommendation positioning: what AI actually does with product queries

When a pet owner asks AI “What’s the best dog food for a Golden Retriever with joint issues?” the AI doesn’t search the internet in real time and evaluate every brand. It generates a response based on patterns in its training data — which brands appeared most often in the context of that query type, which sources carried the most authority weight, and which product claims were structured in ways the model could parse.

The result is product recommendation positioning — a brand’s likelihood of appearing in AI-generated product recommendations for specific query types. Unlike search engine rankings, which change based on algorithm updates and can be influenced with paid ads, AI recommendation positioning is determined by the cumulative weight of a brand’s digital footprint across the entire training corpus.

This creates a compounding problem for pet brands. The brands AI recommends today accumulate more clicks, more reviews, more press coverage, and more forum mentions — which feeds back into AI training data, reinforcing their recommendation position. The brands AI ignores today fall further behind with every training cycle.

How product queries differ from informational queries

Pet-related AI queries fall into two distinct categories, and the competitive dynamics differ significantly between them.

Informational queries (“How often should I feed my puppy?” “What vaccines does my cat need?”) tend to pull from veterinary authority sites, pet health publications, and breed-specific resources. Brands can appear in these responses by publishing authoritative, data-rich health and care content that AI can cite as a source.

Product queries (“What is the best grain-free cat food?” “Best chew toys for aggressive chewers” “Compare fresh dog food delivery services”) are where the competitive gap is most severe. AI defaults to the brands with the highest corpus frequency and the most structured product data. A DTC brand might have a better product, better reviews, and a more specific answer to the query — but if AI never encountered it during training, it doesn’t exist in the response.

The distinction matters because many pet brands focus their content strategy on informational queries (blog posts about pet care, breed guides, health tips) while neglecting the structured product data, comparison content, and third-party authority signals that drive product recommendation positioning. You can rank well for informational queries and still be invisible when a buyer asks AI which product to actually purchase.

The breed-specific recommendation gap

Breed-specific product queries represent one of the largest untapped opportunities for DTC pet brands — and one of the areas where AI recommendations are most unreliable.

When someone asks “What’s the best food for a Cavalier King Charles Spaniel with heart issues?” AI typically recommends Royal Canin (which has a breed-specific line) or Hill’s Science Diet (which has veterinary formulas). These may or may not be the best options, but they appear because those brands have extensive breed-specific content indexed across veterinary sites, pet forums, and product databases.

A DTC brand that specializes in heart-healthy formulas — with veterinary endorsements, clinical feeding data, and targeted ingredient profiles — might be a better answer. But if that brand’s content doesn’t appear in the training data with sufficient frequency and authority, AI will never surface it.

The opportunity is in the structure. Brands that publish breed-specific product pages with detailed nutritional analysis, AAFCO compliance data, and condition-specific feeding guidelines — formatted in ways AI can parse — can earn recommendation positioning for queries the mass-market brands answer generically.

Why your pet brand is invisible to AI

AI generates recommendations based on patterns in its training data — billions of web pages, news articles, forum threads, review sites, and discussions. The brands that appear most frequently in that data are the ones AI recommends.

Consider the math:

• Chewy.com receives roughly 120–140 million monthly website visits (SimilarWeb, 2024), publishes thousands of product reviews, breed guides, and pet health articles, and is cited across financial news, pet industry publications, and forum threads constantly.
• PetSmart.com generates approximately 50–60 million monthly visits, with extensive product content, grooming and adoption service pages, and local store listings that generate massive third-party citation volume.
• Petco.com receives roughly 30–40 million monthly visits, plus extensive veterinary services content and pet wellness coverage.
• A typical DTC pet brand website receives 50,000–500,000 monthly visits, has limited third-party press coverage, and appears on perhaps 10–20 review sites and directories.

That’s a 200x–2,800x gap in web presence. And web presence is what AI systems learn from.

Three specific factors determine whether AI mentions your pet brand:

1. Corpus frequency: How often your brand appears across the web. Chewy has millions of mentions across financial coverage, pet forums, review sites, and product listings. A DTC pet food brand might have 500–2,000 total web mentions. When AI generates a response about “best dog food,” it surfaces the brands it encountered most frequently during training.
2. Source authority: AI weights authoritative sources more heavily. Royal Canin and Hill’s Science Diet are cited extensively in veterinary journal articles, PetMD, and AVMA resources. Chewy and PetSmart appear in financial analysis from major investment banks. A newer DTC brand may have excellent reviews on its own site but lacks authoritative third-party validation that AI trusts.
3. Content structure: Research has found that content with statistical citations and clear factual claims is significantly more likely to be cited by AI systems. Major pet brands publish structured product data, detailed nutritional analysis, AAFCO compliance statements, and feeding guides with specific quantities. Many DTC brands rely on lifestyle marketing (“real food for real dogs”) without the structured, citable data AI needs to extract and recommend.

Most emerging pet brands fail on all three. They have low corpus frequency compared to the giants, limited authoritative mentions outside their own marketing channels, and lifestyle-oriented content that AI can’t easily parse into recommendations.

What AI gets wrong about pet brands

Even when AI does mention a pet brand, there’s a significant chance it gets the facts wrong. AI gives incorrect or outdated information in approximately 30–40% of pet-specific queries. In an industry where incorrect product recommendations can affect animal health, accuracy isn’t optional.

Pricing and availability

Pet food prices fluctuate frequently based on ingredient costs, supply chain disruptions, and promotional cycles. The Bureau of Labor Statistics reported that pet food prices increased 8.4% year-over-year in 2023, one of the highest inflation categories in the CPI. AI frequently cites pricing that is months or years old. A pet owner asking “How much does a 30-lb bag of Royal Canin cost?” might receive a figure that is $10–$20 off the current price. Worse, AI sometimes recommends specific product formulas that have been discontinued or reformulated — a common occurrence in the pet food industry where brands rotate SKUs regularly.

Breed-specific nutrition recommendations

One of AI’s most consequential errors in the pet space involves breed-specific dietary advice. Different breeds have vastly different nutritional needs — a Great Dane puppy requires carefully controlled calcium-to-phosphorus ratios to prevent developmental orthopedic disease, while a Dalmatian needs low-purine diets to prevent urate stones. AI frequently provides generic nutritional advice tagged to specific breeds, conflates puppy and adult nutritional requirements, or recommends brands for breed-specific conditions without verifying that the product actually meets those specific nutritional parameters.

Veterinary diet claims

Prescription and veterinary diets (Hill’s Prescription Diet, Royal Canin Veterinary Diet, Purina Pro Plan Veterinary Diets) require veterinary authorization. AI sometimes recommends these products without noting the prescription requirement, or conflates veterinary-exclusive products with over-the-counter versions of the same brand. The FDA’s ongoing investigation into the potential link between grain-free diets and dilated cardiomyopathy (DCM) in dogs — which has generated over 1,100 reports to the FDA since 2018 — is another area where AI provides inconsistent and sometimes dangerously outdated guidance.

Pet insurance coverage details

The pet insurance market has grown to approximately $4.5 billion in the US (NAPHIA, 2024), with over 5.36 million pets insured. AI frequently provides incorrect coverage details, outdated premium estimates, and wrong waiting period information. Premium pricing varies significantly by breed, age, location, and coverage level — a pit bull in New York City pays 2–3x what a mixed breed in Iowa pays. AI typically cites national averages that are meaningless for individual decisions.

Product ingredient lists and sourcing

Pet food ingredient lists are regulated by AAFCO and the FDA, and formulations change frequently. AI sometimes cites ingredient lists from old formulations, attributes ingredients from one product line to another within the same brand, or makes sourcing claims (e.g., “made in the USA”) that don’t reflect current manufacturing practices. For premium and DTC brands where ingredient quality is the core value proposition, AI getting the ingredients wrong undermines the brand’s primary differentiator.

The $150 billion market AI is reshaping

The US pet industry is massive — and still growing:

• Total US pet industry spending exceeded $150 billion in 2024 (APPA National Pet Owners Survey, 2024–2025), up from $136.8 billion in 2022 and $123.6 billion in 2021.
• Pet food and treats represent the largest category at $64.4 billion (APPA, 2024), followed by veterinary care and products at $38.3 billion, pet supplies and live animals at $31.5 billion, and other services (grooming, boarding, insurance, training) at $16.5 billion.
• Chewy reported $11.15 billion in net sales for fiscal year 2024 (annual report), with 20 million+ active customers and Autoship subscription revenue representing approximately 78% of net sales.
• Mars Petcare generates an estimated $25 billion in global pet care revenue across pet food (Royal Canin, Pedigree, Whiskas, IAMS, Nutro) and veterinary services (Banfield, VCA, BluePearl) — making it the largest pet care company in the world.
• Nestlé Purina PetCare generated approximately $17 billion in global sales in 2024 (Euromonitor, 2024), making it the second-largest pet food company globally.
• The pet insurance segment reached $4.5 billion in the US (NAPHIA, 2024), growing at 21.7% CAGR since 2019, with projections the market could reach $12–$15 billion by 2030.
• The global pet care market is projected to reach $350 billion by 2027 (Euromonitor International), growing at 6–7% annually.

Yet despite its size, the pet industry’s AI visibility landscape is heavily concentrated. The top 5 retailers and top 5 manufacturers capture the overwhelming majority of AI recommendations. The hundreds of DTC brands, independent pet stores, emerging pet health companies, and specialized service providers that collectively represent billions in annual revenue are largely invisible to AI.

Packaged Facts reports that premium and super-premium pet food now accounts for over 40% of total pet food sales in the US, up from 30% a decade ago. This premiumization trend is driven by brands whose entire value proposition is being different from mass-market options. Yet AI consistently recommends the mass-market giants first, undermining the premiumization trend that is actually driving industry growth.

You can’t buy your way into an AI recommendation. There are no ad slots. You have to earn it through web presence, authoritative content, and structured data. And right now, only a handful of companies are earning it.

Where pet brands get discovered — and what’s changing

Understanding the discovery landscape reveals how AI is shifting the competitive dynamics. A pet brand visible on search engines may be completely invisible in AI — and the migration from one to the other is accelerating.

The gap between search engines and AI recommendations for pet products is significant. On search engines, a well-optimized DTC pet brand can compete for long-tail queries like “best fresh dog food delivery” or “grain-free cat food for sensitive stomach.” Paid search gives smaller brands access to the same visibility real estate as Chewy. In AI responses, there is no paid option. The same 3–4 giant retailers and 3–4 legacy manufacturers appear whether the query is generic or highly specific.

Particularly concerning: AI collapses the research funnel. On search engines, a pet owner might click through 5–8 review sites, visit 3–4 brand websites, and compare options across multiple sessions before purchasing. In AI, they get a single curated recommendation in one response. If your brand isn’t in that response, you don’t get a second chance. The “discovery window” has shrunk from multiple touchpoints over days to a single AI-generated paragraph in seconds.

When AI recommends your competitor: the dynamics behind product queries

The most damaging scenario for a pet brand isn’t being absent from AI entirely — it’s being absent from the specific product queries where your potential customers are making purchase decisions.

Consider how this plays out in practice. A pet owner with a new Labrador puppy asks AI: “What’s the best large breed puppy food?” AI responds with Royal Canin Large Breed Puppy, Purina Pro Plan Large Breed Puppy, and Hill’s Science Diet Large Breed Puppy. The pet owner selects one and purchases. They never see the DTC brand that sources human-grade ingredients, conducts AAFCO feeding trials, and has a 4.9-star rating from 8,000 customers — because AI never mentioned it.

Now multiply that by every product query in every pet category. Every “best cat litter for odor control,” every “best dog harness for pulling,” every “best pet insurance for puppies.” Each of these queries is a purchase decision being made inside an AI response. And each one routes to the same small set of dominant brands.

The subscription compound effect

Pet products have unusually high repeat purchase rates. Chewy reports that Autoship (subscription) revenue represents 78% of its net sales. A pet owner who starts buying a food brand based on an AI recommendation typically continues for months or years. The lifetime value of a single AI-influenced acquisition is not one bag of dog food — it’s 12–36 months of recurring orders.

For a DTC pet food brand, average customer lifetime value ranges from $1,200 to $3,600 (12–36 months at $100/month average order value). Every product query where AI recommends a competitor instead of you represents that full lifetime value lost — not a single transaction.

This is what makes AI recommendation positioning in the pet industry particularly high-stakes. Unlike categories where purchase decisions are one-time (furniture, electronics), pet product decisions create long-term revenue streams. Losing the initial AI recommendation doesn’t cost you one sale. It costs you a customer.

Category-specific blind spots

Different pet product categories show different patterns of AI recommendation concentration:

• Pet food: The most concentrated. Royal Canin, Purina, Hill’s, and Blue Buffalo appear in the vast majority of AI food recommendations. DTC fresh food brands (The Farmer’s Dog, Ollie, NomNomNow) appear occasionally for “fresh dog food” queries but are absent from general “best dog food” queries.
• Pet insurance: Moderately concentrated. Lemonade, Trupanion, and Healthy Paws dominate, but newer entrants like Pumpkin and Figo appear in some responses. The gap is smaller here because the category is newer and no single brand has overwhelming corpus frequency.
• Pet supplies and accessories: Highly concentrated at the retailer level (Chewy, PetSmart, Petco, Amazon) but less concentrated at the brand level. AI tends to recommend where to buy rather than what to buy in accessories categories.
• Pet services: Rover dominates boarding and sitting queries. Veterinary service queries route to VCA (Mars Petcare) and Banfield (also Mars Petcare). Independent groomers, trainers, and boarding facilities are almost entirely invisible.

Understanding which category dynamics apply to your brand determines where the AI visibility gap is costing you the most and where product recommendation positioning improvements will have the largest revenue impact.

What drives AI recommendation positioning for pet brands

The brands that appear in AI pet product recommendations share specific patterns. These patterns are observable across major AI platforms and consistent regardless of query phrasing:

• High-volume presence on review aggregators. Chewy’s product review database, Amazon ratings, and dedicated pet review sites create the citation density AI draws from. Brands with thousands of indexed reviews across multiple platforms appear more frequently than brands with reviews only on their own site.
• Veterinary or pet-authority endorsements. Royal Canin and Hill’s appear in veterinary contexts because veterinary journals, PetMD, and AVMA resources cite them. Third-party authority from sources AI considers credible carries more weight than first-party marketing claims.
• Active discussion footprints on forums. Reddit communities like r/dogs, r/cats, and breed-specific subreddits are heavily represented in AI training data. Brands that are frequently discussed — positively or negatively — in these communities appear in AI responses. Brands never mentioned in organic discussions don’t.
• Structured product data. Brands that publish detailed nutritional analysis, AAFCO compliance data, ingredient sourcing information, and feeding guidelines in structured formats give AI parseable data to cite. Lifestyle-first branding without structured product data leaves AI with nothing to extract.
• Financial and industry press coverage. Chewy, Petco, and the major manufacturers appear in financial analysis, industry reports, and business news. This coverage creates authority signals that AI weights when determining which brands to recommend for commercial queries.

DTC pet brands and specialty manufacturers are structurally disadvantaged on most of these dimensions. Even brands with superior ingredient sourcing, higher customer satisfaction, or veterinary endorsements are invisible to AI if they lack the raw citation volume that major retailers and legacy brands accumulate. AI recommendations in the pet space are driven by web footprint density, not product quality.

A Metricus AI visibility report maps your pet brand’s position across every major AI platform, identifies ingredient misinformation or pricing errors, and traces the exact sources feeding competitor recommendations to your potential customers.

The case for understanding your AI visibility now

The pet industry is at an inflection point. The APPA reports that 66% of US households — approximately 86.9 million homes — own a pet, the highest rate ever recorded. Spending continues to climb. The humanization of pets — treating them as family members, not just animals — drives premiumization across food, health, and services. And the discovery mechanism for all of these purchases is shifting from search engines to AI.

The pet brands that understand their AI visibility now — while competitors are still focused exclusively on Amazon ads, paid search, and social media influencers — will have a structural advantage that compounds over time. Every piece of authoritative, data-rich content you publish today enters the training data that shapes AI recommendations tomorrow.

The cost of waiting is real. Consider the math for a DTC pet food brand:

• Average customer lifetime value for a DTC pet food subscription: $1,200–$3,600 (12–36 months at $100/month average order value)
• If 10% of potential customers now start their purchase journey with AI (a conservative estimate given adoption trends), and AI never mentions your brand, the lost customer math is significant
• For a DTC brand acquiring 5,000 new customers per year, 10% AI-driven discovery loss = 500 lost customers = $600,000–$1.8 million in lost lifetime revenue
• For a pet retailer with higher transaction volumes, the numbers scale accordingly

E-commerce penetration in pet products continues to accelerate, with online channels expected to capture over 40% of pet product sales by 2027. As that online channel increasingly includes AI-mediated recommendations, the brands that are visible in AI responses will capture a disproportionate share of growth.

The pet insurance segment illustrates this perfectly. NAPHIA data shows that pet insurance grew 26% year-over-year in 2023, with total premiums reaching $4.5 billion. When a pet owner asks AI “What is the best pet insurance?” — an increasingly common query — the brands that appear in the response capture the consideration set. A few insurers dominate these responses, while dozens of competitive insurers with strong coverage and competitive pricing are invisible.

Related reading

• How to Turn AI Visibility Data Into an Action Plan — The framework for turning audit findings into specific, prioritized actions.
• AI Is Getting Facts Wrong About Your Brand — Why 30–40% of AI responses about pet brands contain factual errors and what drives the inaccuracy.
• What Is AI Visibility and Why Does It Matter? — How brands appear in AI responses and what determines who gets recommended.

Frequently asked questions

Why does AI always recommend Chewy when I ask about pet food?

AI generates recommendations based on patterns in training data. Chewy generates over 20 million monthly website visits, has extensive product review content, investor coverage, and dominates pet industry discussions across forums and news sites. This creates what researchers call corpus frequency — the more a brand appears across the training data, the more likely AI is to recommend it. Smaller DTC pet brands may have superior products but lack the web presence volume needed to surface in AI recommendations.

How accurate are AI recommendations for pet food and pet health products?

AI responses about pet products contain factual errors in approximately 30–40% of queries. Common errors include outdated pricing (pet food prices change frequently due to ingredient cost fluctuations), incorrect product availability (recommending discontinued formulas), wrong ingredient lists, and inaccurate breed-specific feeding recommendations. AI also struggles with veterinary nutrition claims — sometimes recommending grain-free diets without noting the FDA’s ongoing investigation into a potential link to dilated cardiomyopathy (DCM) in dogs.

Can DTC pet brands compete with Chewy and PetSmart in AI visibility?

Yes, but not by matching their volume — by matching their structure and authority in specific niches. DTC pet brands can improve AI visibility by publishing data-rich content with specific nutritional claims, ingredient sourcing details, and feeding trial results that AI can extract and cite. Building citations on authoritative veterinary and pet industry sources, implementing Product and FAQPage structured data, and creating breed-specific or condition-specific content all help. Niche authority can outperform generic volume.

How much revenue are pet brands losing from AI invisibility?

The revenue impact depends on brand size and category, but the math is significant. The US pet industry exceeds $150 billion annually, with pet food alone at $64 billion. If even 8–12% of product discovery shifts to AI, that represents $12–18 billion in AI-influenced pet spending annually. For a mid-size DTC pet food brand doing $20 million in annual revenue, 10% of potential customers starting with AI and never seeing their brand represents $2 million in at-risk revenue.

What happens to my pet brand if I ignore AI visibility?

The gap widens. In our data, the average brand’s AI visibility gap grew by 10% every 90 days when left unaddressed. As more pet owners shift from search engines to AI for product discovery, brands that are invisible in AI lose compounding share of new customer acquisition. The brands that AI recommends today accumulate more web mentions, more reviews, and more authority signals — which makes AI recommend them even more in the future. Waiting does not keep things stable; it accelerates the gap.

How do I find out what AI recommends instead of my pet brand?

The step most pet brands miss: checking what AI actually says when someone asks about best products in your category. AI gives different answers depending on the query, and those answers increasingly exclude smaller brands. One-time AI visibility reports (like Metricus) check this systematically — you submit your webpage, and within 24 hours you get back what AI says, why it says it, and how to fix it, with one-click imports for every fix. 90% of Metricus users report they don’t need ongoing monitoring — they just need to know what to fix and how to fix it. 80% of brands that implemented the top 3 fixes saw measurable changes within 10 days.

Sources: American Pet Products Association (APPA) National Pet Owners Survey (2024–2025); Packaged Facts Pet Market Outlook (2024); Euromonitor International global pet care data (2024); pet industry analyst projections (2024); North American Pet Health Insurance Association (NAPHIA, 2024); Bureau of Labor Statistics CPI pet food data (2023); FDA DCM investigation updates (2018–2024); Chewy Inc. fiscal year 2024 annual report; Petco Health and Wellness 2024 annual report; Mars Petcare corporate data; Nestlé Purina PetCare segment reporting (2024); SimilarWeb traffic estimates (2024); search volume forecast data (2024); Pew Research Center AI adoption survey (2024). AI mention rates based on Metricus internal testing across the major AI platforms (2026).