Schema App

Schema App is an enterprise platform for building and governing structured data at scale. Founded in 2016 and based in Guelph, Ontario, the company (operating as Hunch Manifest Inc) positions its product as the tool for creating a “Content Knowledge Graph”: a machine-readable model of your brand, products, and expertise that both Google and AI engines can trust and act on.

The platform sits in a narrow but important category: it does not just generate schema snippets. It manages structured data as a continuous, governed data layer across large sites where manual markup is impractical. Enterprise teams at regulated industries, healthcare providers, and large media companies use it because schema consistency at scale is genuinely hard to maintain with in-house engineering alone.

What Schema App does

Schema App gives enterprise SEO teams a way to deploy, manage, and validate schema.org markup across a large site without routing every change through a developer. The core workflow involves designing your entity structure (products, people, places, articles, FAQs), deploying markup via tag manager or CMS integration, and monitoring what search and AI engines pick up. The company also sells an “Entity Hub” add-on that gives teams direct control over how entities and relationships in their knowledge graph are defined, so you can explicitly tell AI systems what your business is, who your team is, and how your products relate to each other.

The homepage explicitly frames this as AI defense: “We didn’t know how to fix the AI Overview hallucination problem, but Schema App helped us solve it.” That quote is from Wells Fargo, and it points to a real use case that has grown fast: brands seeing incorrect information surface in AI Overviews and needing a structured data solution to correct it.

Why it matters for AI visibility

Structured data is one of the clearest signals AI engines use to understand factual claims about a brand. When ChatGPT, Perplexity, or Google’s AI Overviews generate a response that includes your product, your pricing, or your company facts, those facts are disproportionately drawn from machine-readable sources: schema markup, Wikipedia, and brand knowledge panels. A well-deployed schema layer is not a Google-only play. It directly affects whether AI engines hallucinate about you or cite you accurately.

Schema App’s framing as a “Content Knowledge Graph” provider is accurate to this shift. An FAQ schema block tells Google what questions a page answers. A Product schema block with accurate pricing and reviews reduces the chance that an AI overview manufactures wrong numbers. Organization markup with verified sameAs links to Wikidata and LinkedIn anchors your entity in the knowledge graph, which is exactly what LLMs draw from when describing companies. If you want to understand how to implement these signals yourself, schema markup and FAQ schema are good starting points.

For brands tracking whether these efforts are paying off in AI results, the question becomes: after you deploy structured data correctly, are AI engines actually citing you? That requires a different tool. AI visibility tracking tells you whether the work is showing up in ChatGPT and Perplexity responses. Fokal’s AI visibility tools let you monitor that directly.

Ratings and customer evidence

Schema App’s pricing page shows a 4.75/5 rating on G2 (18 reviews) and 4.9/5 on Capterra (18 reviews). Both verified from their live site. Customer case studies on the homepage name Henry Ford Health (impressions up 119% with entity linking), Wells Fargo (replaced outdated AI results in weeks), and InSinkErator (structured data turned into reusable AI advantage). These are enterprise accounts, consistent with the positioning.

Pricing

Custom enterprise pricing. No public tiers. Schema App’s pricing page states: “We offer customized pricing based on the scale and complexity of your enterprise needs.” They quote based on site size, content complexity, level of support, and whether you add the Entity Hub. You submit via a form to get a proposal.

This means Schema App is not a self-serve tool. It suits teams with a significant SEO budget and either a large site or a specific AI visibility problem (hallucinations, incorrect AI Overviews) that justifies managed services. For teams at the other end of the market, the directory has a range of structured data and AI SEO tools at different price points.

Who it is for

Large enterprises where schema markup is a compliance, accuracy, or scale problem. Healthcare, financial services, and regulated industries where an AI engine citing the wrong fact is a real liability. SEO teams that own the brief but rely on developers to ship (Schema App removes that bottleneck). And any brand that has already appeared incorrectly in an AI Overview and needs to fix it at the root rather than petition Google.

For smaller sites, the cost-to-benefit ratio will not stack up. The product assumes you already have a structured data strategy and need tooling to execute it across thousands of pages, not a way to add a few schema blocks to a blog.