AI Visibility
Answer engine optimisationSee and improve how AI answer engines describe you.
At a glance
- Ownership
- Valtair Labs
- Category
- Answer engine optimisation (AEO)
- Status
- Beta
- Audience
- Marketing and content teams
- Retrieval
- Chunked content, embedded for vector search
- Evaluation
- Structured scoring against retrieved sources
- Measurement
- Scheduled runs, versioned for drift
- Model providers
- Multiple, provider-agnostic
- Deployment
- Cloud, scheduled pipelines
AI Visibility measures how a brand appears in AI-generated answers and shows what to change to improve it, the practice now settling under the name answer engine optimisation (AEO). We built it to study retrieval-augmented generation (RAG) from the outside in: what do answer engines actually surface, and why. It is a Valtair Labs experiment, and the name is a working description rather than a product brand.
Brands lose visibility as buyers move from search results to AI-generated answers they cannot measure or influence.
Marketing and content teams
Product capabilities
Answer visibility tracking
Track how a brand and its topics are represented across AI answer engines over time, so drift in how you are described is visible rather than anecdotal.
Grounding gap analysis
Source content is chunked and embedded, then compared against what answer engines return, exposing where material is missing, thin, or ambiguous for retrieval.
Actionable recommendations
Turn findings into specific, prioritised content changes.
Why we built it
Answer engines change how buyers discover brands, and most teams have no instrumentation for it. Building it let us study grounding and retrieval quality from the consumer side of the system, which is the side we usually do not get to see when we build retrieval for clients.
Product experience
A dashboard that shows current visibility, tracks change over time, and points to the specific content that needs work.
System architecture
AI and data components
- Structured answer evaluation against retrieved sources
- Embeddings and vector search over chunked source content
- Grounding and citation gap detection
- Recommendation generation with rationale
- Provider-agnostic model orchestration
Backend and integrations
- Scheduled measurement pipelines
- Versioned result history for drift comparison
- Content ingestion, chunking, and vector indexing
- Analytics integration
Evaluation and reliability
Measurements are versioned so drift in how answer engines describe a brand is comparable over time, and recommendations are tied to observed retrieval gaps rather than generic advice.
Product status
Status reflects where AI Visibility is today. We publish product stages honestly and do not present prototypes as production systems.
Key lessons
- Visibility is only useful tracked as drift over time, not as a snapshot.
- Most answer-quality problems trace back to thin or ambiguous source content, not to how it is chunked or embedded.
- Grounding gaps are easier to fix once they are made specific.
An experiment, not a product
AI Visibility is a Valtair Labs project. We build and run it to sharpen our own engineering, and we publish what we learn. It has no separate website, pricing, or support, and the name is a working description rather than a brand.
RAG and Knowledge Systems
For products that must retrieve and reason over private or domain-specific information with citations.
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