Avenq.aiAvenq.ai visibility scorecards
Measure what AI systems say before you fix the site.
Avenq.ai tests how AI systems describe your business, then maps the schema, metadata, FAQ, source, and entity fixes that help AI read the website more accurately.
Schema
Metadata
FAQ data
Proof signals
Operating principle
No fake numbers. No made-up proof. No promises that an AI tool will cite you.
Only clearer, better-supported information.AI question
“What does this business offer?” Avenq.ai turns scattered details into structured answer signals.Website signal
Facts → Schema → Pages A cleaner foundation for AI parsing and answer eligibility.
AI-readable services
Schema fields
Source-backed facts
Entity clarity
Visibility score
AI-readable services
Schema fields
Source-backed facts
Entity clarity
Visibility score
What is AI readability?
AI readability is the layer that helps answer engines understand what your business is eligible to be recommended for.
Search is moving from link lists into generated answers. In that environment, a website has to do more than look good: it needs clean entity signals, structured schema, direct service definitions, answer-ready FAQs, and source-backed proof that AI systems can parse without guessing.
The AI answer landscape is still early. Businesses that organize their machine-readable layer now can build a stronger foundation before their competitors treat AI visibility as a standard part of marketing. Avenq.ai does not guarantee placement, but clearer signals can improve the odds that AI systems understand, describe, and cite the business correctly over time.
Optimization components
Schema and JSON-LD
Organization, LocalBusiness, Service, Product, FAQPage, WebPage, Breadcrumb, sameAs, review, and source fields where they are accurate and verifiable.
Direct answer structure
Clear first-sentence definitions, question-led sections, service descriptions, product facts, pricing context, and next-step signals that can be extracted cleanly.
Entity and source clarity
Consistent names, categories, locations, social profiles, references, proof links, reviews, and claims that AI systems can connect to the right business.
Crawlable page data
Readable page titles, metadata, headings, alt text, internal links, sitemap and robots basics, and important content that is not hidden from automated readers.
Meet Avenq.ai
Avenq.ai is founded by Robert Hillis and Koby Cerra.
We are two college students building a focused AI-readability studio for businesses that want their websites to be understood more clearly by AI search and answer systems.
We are early, direct, and hands-on. Our work is not about pretending to guarantee rankings or citations. It is about checking the parts of a website AI systems rely on: schema, metadata, FAQs, source signals, proof, and clear business definitions.
Because Avenq.ai is founder-led, every audit is reviewed directly by the people building the process. No inflated claims. No fake proof. Just clearer AI-readable structure.
Robert Hillis
Focuses on client intake, audit strategy, scorecard direction, and implementation planning.
LinkedIn ↗Koby Cerra
Focuses on research, schema review, quality control, and client recommendations.
LinkedIn ↗Measured, not guessed
Every audit is built around what AI systems can read, what they may misunderstand, and what proof the site can honestly support.
Scorecard method
We measure AI readability before recommending fixes, then use the scorecard to show what improved.
The scorecard turns AI-readability into something a client can review: prompts, results, accuracy notes, citation checks, and page-level fixes. It is not a ranking guarantee. It is a controlled way to see whether AI systems can understand the business more clearly after the website signals are improved.
Prompt set
We build buyer-style prompts around the business category, service area, offer, proof, comparisons, and decision questions.
Baseline read
We record whether AI systems mention the business, describe it correctly, cite it, confuse it, or miss key eligibility signals.
Signal fixes
We improve the readable inputs: schema, metadata, FAQ data, source fields, sameAs links, service definitions, and proof-backed claims.
Re-test
After implementation, we re-run the same prompts and compare visibility, accuracy, citation support, and answer usefulness.
How the score increases
- AI systems can identify the business category and offer without guessing.
- Important pages include accurate schema and answer-ready metadata.
- FAQs answer real buyer questions in extractable language.
- Claims are supported by reviews, examples, credentials, sources, or approved proof.
- The same entity details appear consistently across the site and trusted profiles.
Pricing and scope
Start with the right level of work, not automatically the biggest package.
Every engagement starts with the audit, so we're measuring before we recommend anything. From there it can scale into full implementation, ongoing monitoring, or both. We confirm fit before recommending paid work.
01 · $500-$1,000
Full AI Visibility Audit
A measured scorecard plus a page-by-page roadmap with schema gaps, entity issues, missing source signals, FAQ opportunities, and implementation priorities.
- Visibility scorecard and prompt set
- Schema, FAQ, proof, and source gaps
- Prioritized roadmap for your team
02 · $1,000-$1,750
Complete Implementation
Approved changes placed on the site: JSON-LD schema, FAQ data, metadata, service definitions, source fields, or working directly with your web person.
- Approved schema placed on-site
- Metadata and FAQ field updates
- Design-preserving implementation
03 · $175-$300/mo
Monthly Visibility Monitoring
Available only after a completed audit. A recurring re-measure on the same frozen prompt set, with a month-over-month change report and optional implementation of approved changes each month.
- Monthly re-measure on frozen prompts
- Change report vs. the baseline
- Optional implementation add-on
Implementation paths
Avenq.ai can work with your web person or implement the approved fixes directly.
If you already have a web person
We deliver the audit as a clean implementation brief: exact pages, fields, schema types, wording notes, and validation checks your developer can follow.
- Developer-ready schema and metadata notes
- Page-by-page priority order
- Clear “do not change design” boundaries
If you need Avenq.ai to implement
We confirm platform access, page count, and approval boundaries before placing schema, updating metadata, adding FAQ data, and tightening approved answer fields.
- Design-preserving website updates
- Schema and FAQ validation after changes
- Final summary of exactly what changed
Who this is for
Avenq.ai is for businesses with real offers that need cleaner AI-readable structure.
Good fit
- Your site has real services, products, locations, proof, or FAQs that AI systems should understand.
- You want schema, metadata, FAQ data, and entity signals reviewed professionally.
- You need a roadmap your developer, web person, or Avenq.ai can implement without redesigning the site.
Not the right fit
- You want guaranteed AI citations, rankings, or AI Overview placement.
- You need fake proof, inflated claims, or unsupported authority signals.
- You want a full website redesign instead of targeted AI visibility, schema, and source-signal work.
Proof in progress
Early pilot work is shaping the Avenq.ai system.
Pilot work
See the early projects, internal tests, and client-style work helping shape the Avenq.ai audit system.
View proof page →Professional standard
Avenq.ai does not guarantee placement in AI answers, search results, AI Overviews, or third-party tools. The work improves the clarity, structure, and machine-readability of approved website information so AI systems have better data to interpret.
Start with what’s true
Choose the AI-readability path that fits the site.
Pick a paid tier directly, or request scope confirmation before work begins. We confirm the page count, platform, and implementation need before collecting payment.
View pricing →