An AI Visibility Agency Built on Infrastructure

What an AI Visibility Agency Actually Does

An AI visibility agency works on the inputs generative systems use to construct answers: entity definitions, structured data, corroborating third-party sources, and retrievable content. Rankings are downstream of those inputs, not a substitute for them.

The practical scope covers four things: making your organization unambiguous to machines, publishing content that can be retrieved and quoted, building corroboration outside your own domain, and measuring what AI systems actually say about you over time.

Entity Clarity Comes First

If an AI system cannot resolve who you are, nothing else compounds. Conflicting names, thin structured data, and inconsistent descriptions across the web produce hedged answers or no mention at all.

BackTier engineers a single canonical definition of the organization and its people, expresses it in Schema.org markup connected by stable identifiers, and keeps that definition consistent across every surface a model may read.

Retrieval, Not Just Publication

Generative systems retrieve passages, not pages. Content has to answer a specific question in a self-contained block, with the claim stated before the elaboration.

We restructure existing content for passage-level retrieval and build definitional pages for the concepts your buyers ask about, so a model has something clean to quote.

Corroboration Outside Your Domain

Models weigh independent sources more heavily than self-description. Podcasts, interviews, directories, data sources, and reference entries all raise the confidence a system has in a claim about you.

This is slow work and it is the part most agencies skip. It is also the part that separates a brand that gets mentioned from a brand that gets cited.

Measurement You Can Audit

We baseline how AI systems currently answer a defined prompt set for your category, then re-run that prompt set on a fixed cadence and report the diff: inclusion, framing, competitor share, and factual accuracy.

No projected multiples, no borrowed industry statistics. The prompt set and the raw responses are yours, and they are what we report against.

Baseline

We record how ChatGPT, Perplexity, Gemini, Claude, and AI Overviews answer your category prompts today, and where your entity data conflicts across the web.

Entity build

We define the canonical entity, implement the connected schema graph, and resolve naming and description conflicts on and off your domain.

Retrieval layer

We restructure and publish the definitional, comparison, and answer content that gives models something specific to retrieve and quote.

Corroboration

We pursue independent sources, references, and placements that raise machine confidence in your claims.

Re-measure

We re-run the prompt set on a fixed cadence and report inclusion, framing, and accuracy changes against the baseline.