Edgy Labs LLC
Edgy Labs LLC (part of Compasso UOL) · Marketing
Edgy Labs LLC is a Houston-founded enterprise SEO agency and generative engine optimization practice, part of Compasso UOL, that restructures product data so AI search engines can cite it.
About Edgy Labs LLC
What Is Edgy Labs LLC
Edgy Labs LLC built its name on one narrow promise: make very large, very messy corporate websites legible to search engines. Alexander De Ridder, Michael Umansky and Gary Haymann founded the company in Houston in 2016. It began as a research lab bolted onto a tech publication, where the team tested ranking theories on its own pages before selling the results as a service.
What it sells is work, not software. Most buyers arrive asking for AI SEO services and leave with a data project. Developers, SEO strategists and content writers audit a client's catalog, rewrite product and service data into one consistent semantic structure, then connect it to internal knowledge graph work and schema markup. Compasso UOL, a Brazilian digital services group, bought the company on November 4, 2021 and folded it into a larger enterprise portfolio.
The limits matter as much as the strengths. There's no self-serve tier, no public price list, and a shop with two hundred product pages gets little from an engagement built around catalogs with tens of thousands of records. The edgylabs.com domain now redirects to AI/R, Compasso UOL's generative engine optimization practice, so the brand lives on inside a broader agentic AI consulting offer.
Getting Started
- Send a request through the AI/R contact form and describe your catalog size, page count and the markets you sell in.
- Take the maturity assessment, which scores how well generative engines can currently parse your content and product data.
- Agree on a starting scope, usually one category, region or business unit rather than the whole estate.
- Let the team rebuild that slice into structured data with schema markup and knowledge graph links, then measure citation and traffic shifts.
- Hand the rules to your own content and data owners, with the agency retained for governance and monitoring.
Product Information
A quick look at Edgy Labs LLC's pricing, supported platforms, and performance.
Best for
The users, tasks, and scenarios where this tool fits best.
Users
- Enterprise SEO leads
- Product data and PIM owners
- Marketing teams bracing for AI answers
Tasks
- Rewriting a sprawling product catalog into one semantic structure that engines can parse the same way every time.
- Adding knowledge graph links and schema markup to service pages that currently describe offers in prose only.
- Diagnosing why a page ranks well in classic search but never shows up in AI generated answers.
- Writing data governance rules so a hundred content editors stop inventing their own naming conventions.
Scenarios
- A retailer with tens of thousands of SKUs and inconsistent attributes across regions.
- A manufacturer whose technical specifications sit in PDFs that no crawler reads properly.
- A brand that watched organic clicks slide after AI Overviews started answering its head terms.
Key features
Semantic Content Structuring
The core service turns raw catalog and service descriptions into an explicit, interconnected information model. Every product attribute, category and use case gets a defined place, which cuts the ambiguity that makes AI systems guess. Clients keep the model after an engagement ends, so the structure survives staff changes.
Knowledge Graph and Schema Markup
Edgy Labs connects pages to a knowledge graph and marks them up with Schema.org vocabulary. That gives generative engines named entities and relationships instead of loose text to interpret. The practical payoff is fewer wrong facts in AI answers and better odds of being picked as the cited source.
API-First Delivery
Content and product data leave the engagement in a form other systems can pull, not as a stack of documents. That suits enterprises already running a PIM, a CMS and a feed tool side by side. It also means updates land once and propagate, rather than being retyped per channel.
GEO Maturity Assessment
Before any rebuild, the team scores how visible a brand is to AI engines and where the data breaks down. The assessment covers content completeness, structural consistency and how often competitors get quoted instead. It gives a buyer a defensible starting point and a way to measure the spend later.
Continuous Data Governance
Structure decays. New SKUs arrive with missing fields, regional teams rename things, and the model quietly drifts. The governance layer sets naming rules, review cycles and quality checks so the semantic structure still holds a year after launch. This is what separates an ongoing retainer from a one-off cleanup.
Pros and cons
Pros
- Deep specialization in enterprise scale SEO and generative engine readiness, with a track record naming Fortune 500 clients.
- One team covers strategy, content, schema markup and data governance, so you're not stitching four vendors together.
- Methodology documented as a repeatable lifecycle, from maturity assessment through monitoring.
- Backed by Compasso UOL, which brings global delivery capacity and offices beyond the US.
- The work targets a real shift in buying behavior, since a growing share of research now ends inside an AI answer.
Cons
- No published pricing and no self-serve entry point, so evaluating cost takes a sales conversation.
- Built for large catalogs; small and mid-sized sites will find the engagement oversized for their needs.
- Brand continuity is confusing, because the original edgylabs.com domain now redirects to AI/R and older case studies live in two places.
Frequently asked questions
It runs enterprise SEO programs and generative engine optimization projects. Practically, that means auditing large content estates, restructuring product and service data, and adding schema markup so search engines and AI assistants read the same facts.
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