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Sales, Marketing & Ad Tech

AI Answer Visibility API

A growing share of the answers your clients care about are generated rather than listed. Nobody can tell them whether they are in those answers, or why.

SEO and content platforms
Digital agencies
Brand marketing teams
PR measurement vendors

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What your customers would see

example interface

MockupIllustrative mockup of the interface we would build. Figures shown are examples, not results from a live deployment.

The problem this solves

The question arrives from clients constantly now: when someone asks an AI assistant about our category, do we come up? It is a reasonable question and the current answer is usually a screenshot from somebody's phone. The measurement layer that exists for search positions does not exist for generated answers, and the naive approaches produce numbers that fall apart under scrutiny.

Several things make this hard in ways rank tracking is not. The same question asked twice can produce different answers, so a single observation is a sample from a distribution rather than a fact. Answers vary by whatever context the surface has about the user. Citation behaviour differs between surfaces, and some cite nothing at all. Phrasing matters enormously — three natural ways of asking the same question can produce three different brand sets.

What can be measured properly is presence and citation rate across a defined query set, sampled repeatedly, reported with uncertainty. That is genuinely useful: it shows whether a brand is mentioned, which sources the answer leans on, how the answer characterises the brand, and how that changes over time. What cannot be measured is total share of all AI answers everywhere. The first is a product. The second is a claim we would not make.

Who buys this

SEO and content platforms, Digital agencies, Brand marketing teams, PR measurement vendors

Category: Sales, Marketing & Ad Tech

What the API returns

What comes back

Presence rate per query: how often a brand appears across repeated samples
Citation extraction: which sources an answer drew on and which were attributed
Answer characterisation — how the brand is described, and whether the description is accurate
Competitor presence on the same query set, from the same samples
Confidence intervals on every rate, derived from the sample size actually collected
Query phrasing variants, so sensitivity to wording is measured rather than ignored
Surface and model-version metadata on every observation
Change detection distinguishing a real shift from sampling noise and from a surface update

Endpoints

Method	Path	Returns
POST	/v1/queries/track	Register a query set with phrasing variants and sampling plan
GET	/v1/visibility/summary	Presence and citation rates with intervals
GET	/v1/queries/{id}/observations	Individual sampled answers with metadata
GET	/v1/citations	Sources cited across the tracked query set
POST	/v1/competitors/compare	Relative presence across a competitor set
GET	/v1/changes	Detected shifts separated from sampling variation

Request and response

illustrative

Request

{
  "brand": "Example Industries",
  "queries": [
    { "intent": "category_recommendation",
      "variants": ["best industrial flow meter suppliers",
                   "who makes reliable industrial flow meters",
                   "recommend a flow meter manufacturer"] }
  ],
  "surfaces": ["surface_a", "surface_b"],
  "samples_per_variant": 12,
  "competitors": ["Northbridge Mfg", "Calder Group"]
}

Response

{
  "brand": "Example Industries",
  "window": "2026-10-15 to 2026-10-22",
  "observations_collected": 72,
  "summary": {
    "presence_rate": 0.42,
    "presence_ci_95": [0.31, 0.54],
    "cited_rate": 0.19,
    "cited_ci_95": [0.11, 0.30]
  },
  "by_variant": [
    { "variant": "best industrial flow meter suppliers", "presence_rate": 0.58, "n": 24 },
    { "variant": "who makes reliable industrial flow meters", "presence_rate": 0.42, "n": 24 },
    { "variant": "recommend a flow meter manufacturer", "presence_rate": 0.25, "n": 24 }
  ],
  "characterisation": [
    { "theme": "described as mid-market", "frequency": 0.31 },
    { "theme": "calibration service mentioned", "frequency": 0.22 },
    { "theme": "incorrect product range stated", "frequency": 0.08, "flag": "accuracy" }
  ],
  "top_cited_sources": [
    { "domain": "trade-publication.example", "share": 0.28 },
    { "domain": "example-industries.com", "share": 0.17 }
  ],
  "competitors": [
    { "brand": "Northbridge Mfg", "presence_rate": 0.67, "ci_95": [0.55, 0.77] }
  ],
  "method_note": "Sampled measurement over a defined query set. Not a census of AI answers."
}

Where the data comes from

Permitted access to AI answer surfaces	Official APIs and interfaces where terms allow programmatic querying for measurement; some surfaces do not, and those are excluded rather than worked around
Your client's query set	The questions that actually matter commercially, defined with the client — a generic keyword list measures the wrong thing here
Brand and product reference data	Canonical names, product ranges and spellings, so presence detection is not confused by near-matches
Cited source resolution	Domain and publication identity for attributed citations, which is where the practical optimisation insight sits
Your own site and content inventory	To relate citation patterns to what the brand has actually published
Observation archive	Generated answers are not retrievable later; every sample has to be captured and stored when collected

On data licensing. Being allowed to read a dataset does not automatically mean you may redistribute it, resell it through an API, or train a model on it. Before any build starts we check the licence, terms and derived-data rules of every source, and we design around the ones that do not permit your intended use. Where a commercial licence is required, that cost is identified during discovery rather than discovered later.

The intelligence layer

The difference between a demo and a service is mostly in this section. These are the parts that keep the answer trustworthy once real traffic is hitting it.

Repeated sampling with a documented plan, treating each observation as a draw rather than a measurement
Confidence intervals on every reported rate, sized from the actual sample count, so a thin sample presents as thin
Phrasing variant design as a first-class part of the method, because wording sensitivity is one of the largest effects in this data
Brand presence detection with entity resolution, handling abbreviations, misspellings and parent-company mentions distinctly
Characterisation extraction into recurring themes, with an accuracy flag where the answer states something factually wrong about the brand
Surface and version metadata captured per observation, so a step change can be attributed to a surface update rather than a brand shift
Change detection against sampling variation, requiring a shift to exceed the interval before it is reported
Continuous method validation, since any change to a surface's behaviour can invalidate comparisons across the boundary

What it changes for the business

An answer to the question clients ask

Presence and citation rates with intervals replace screenshots and anecdote in the AI visibility conversation.

Content priorities with evidence

Knowing which sources answers lean on points content and PR effort at the publications that actually get cited.

Misstatements caught

Accuracy flags surface cases where answers describe a brand's products wrongly, which is a correctable problem once visible.

Defensible measurement

Stated sample sizes and intervals mean the numbers survive a client's scrutiny rather than inviting it.

How it fits into your product

How it embeds

REST summary and observation endpoints for your own dashboards and client reports
Sampling plans configured per client and per query group through the API
Change webhooks into agency alerting, with sampling-noise suppression applied
Observation archive accessible so a reported rate can be traced to the answers behind it
White-label delivery, with the method note carried into client-facing output rather than dropped
Per-client entitlements on query volume, surfaces and sampling frequency

Included in every build

Authentication, scoped API keys and per-customer rate limiting
Input validation with predictable, documented error responses
Versioned endpoints so your integration never breaks without notice
Request and response logging for audit and dispute resolution
Latency, error-rate and usage monitoring with alerting
OpenAPI specification, sandbox environment and client examples
Backups, failover design and a documented recovery procedure
An agreed support arrangement once the service is live

How we would build it

about 20 weeks

Discovery and feasibility

Weeks 1–3

This one depends heavily on data access, so we start by proving the sources exist, can be licensed for your use, and actually carry the signal the model needs. If they do not, you find out here.

Architecture and baseline model

Weeks 4–6

A written architecture and a baseline model measured against a holdout period, so you know what accuracy you are buying before the full build starts.

Production build

Weeks 7–16

Data pipelines, model training and serving, historical archive, validation harness, API, security, monitoring and documentation.

Validation and integration

Weeks 17–20

Backtesting against held-out history, accuracy reporting your team can audit, integration support and an agreed retraining cadence.

Commercial models

There is no fixed price list, because the cost is driven by data complexity, integration count, model requirements, request volume and the support level you need. Scope is agreed in writing before development starts.

Fixed-scope prototype

A defined slice of the service, built and priced as one piece of work. The usual first step when the business case needs proving internally.

Fixed-price build

An agreed scope against a written architecture, delivered for an agreed price. Best when the requirements are already clear.

Build then retainer

We build the service, then operate and improve it under a monthly agreement covering monitoring, updates and model maintenance.

White-label product

The service runs under your brand, your domain and your pricing, with MLPivot invisible to your customers.

Questions buyers ask

Is this a complete picture of AI answer visibility?

No, and that distinction is the most important thing to understand about it. This is sampled measurement over a query set you define, on surfaces that permit programmatic access. It is not a census, it cannot see answers given to real users in their own context, and it says nothing about surfaces we cannot query. We put the method note in the response payload so it travels with the number into whatever report it ends up in.

Why do the numbers move when nothing changed?

Because generated answers vary between identical queries. That variation is intrinsic, which is why the service samples repeatedly and reports intervals rather than single observations. A presence rate moving from 0.42 to 0.46 with overlapping intervals has not changed in any meaningful sense, and the change detection is built to say so rather than generate an alert.

How stable is this over time?

Not very, and that is a property of the surfaces rather than the measurement. Answer behaviour, citation patterns and interface layouts change frequently and without notice, sometimes enough to break comparability across the change. We capture surface and version metadata per observation so a step change can be attributed, and we would flag a discontinuity rather than drawing a smooth line through it.

Can you optimise a brand into AI answers?

We would not promise that, and we would be sceptical of anyone who does. What the data supports is an observation: answers lean on identifiable sources, and brands well covered in those sources appear more often. That makes citation analysis a reasonable input to content and PR priorities. It is not a lever with a predictable response, and presenting it as one would be dishonest.

Which surfaces can you actually measure?

Those whose terms permit programmatic querying for this purpose, which is a smaller set than the list of surfaces that exist, and it changes. We establish the permitted set during discovery, design the product so unavailable surfaces are absent rather than estimated, and would tell you if the remaining coverage is too thin to be worth building on.

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