Turn AI Search into Your Top Discovery Channel

Most DTC brands focus on traditional SEO and paid search. They invest heavily in tools like Ahrefs or SEMrush for keyword research and Google Ads for traffic. These tools are optimized for Google's ranked results, not for conversational AI. They target keywords like "best running shoes for flat feet" but miss the natural language query an AI receives: "I have flat feet and I run 20 miles a week on pavement. What are 3 shoe models I should consider and why?"

Consider a Shopify-based DTC brand selling specialized hiking gear. They have detailed product pages and a Google Merchant Center feed. A shopper asks Perplexity, "What's the best waterproof, lightweight daypack under $150 that fits a 13-inch laptop?" PerplexityBot scans the web. It finds the brand’s page, but key specs like waterproofing level, weight in lbs, and laptop sleeve dimensions are buried in images or unstructured paragraphs. The crawler cannot extract this data reliably, so it cites a competitor's page that uses a clean HTML table with the exact specs. The sale is lost before the brand knew it was a contender.

The structural problem is that most ecommerce platforms are built for human readers and keyword crawlers, not for modern AI crawlers. Systems like Shopify or BigCommerce use themes that prioritize visual layout over semantic structure. Product data is often stored in ways that are difficult for an AI to parse and compare against other products. Without machine-readable, structured data, a brand is invisible to this new, rapidly growing discovery channel.

The first step is an AEO audit. Syntora analyzes your 10-20 most critical product pages and blog posts to identify how AI crawlers see your content. The audit uses Syntora’s own 9-engine Share of Voice monitor which tracks ChatGPT, Claude, and Gemini to see if and how you are currently being cited. You receive a report showing specific content and technical gaps, from missing JSON-LD schemas to unstructured product data.

Based on the audit, Syntora implements a system to structure your content for AI extraction. This involves generating `Product` and `FAQPage` JSON-LD schemas automatically from your product database. For blog content, a Python script using the Claude API rewrites introductions to be citation-ready, directly answering the target query in the first two sentences. Content is reformatted to use semantic HTML, like `<table>` for specifications, which AI crawlers prioritize.

The delivered system is a set of scripts and templates integrated into your existing CMS or ecommerce platform. You also get a custom Share of Voice dashboard, built with Python and Supabase, that tracks your brand’s citations across 9 major AI engines weekly. This dashboard provides direct proof of what content is driving AI-based discovery, allowing you to refine your strategy based on real data.