Search IntentLab.
Classify a query across four intent states, inspect the probability behind dominant and secondary signals, then test that distribution against the visible result surface and the intent mix of related searches.
One query.Four possible intents.
The profile records a dominant label with its probability and, where present, secondary intent labels with their own probabilities. The full distribution remains visible instead of being collapsed into one generic classification. Device, location, language and capture time remain attached to the observation because the same wording can produce a different intent mixture when the search context changes.
Classification is not always binary.Inspect the probability gap.
A query may have a highly dominant intent or a meaningful secondary intent. The matrix exposes the four intent probabilities and a TAO diagnostic describing how concentrated or mixed the returned distribution is.
The SERP is intent evidence too.Observe what Google actually surfaces.
A live result capture can expose organic listings, People Also Ask, images, videos, local packs, shopping, Knowledge Graph and AI features. Search Intent Lab records that composition next to the classifier output.
Count of organic items returned in the live capture.
Question-oriented SERP evidence associated with the query.
Visual result surfaces present in the captured SERP.
Transactional or location-oriented surfaces present in the result environment.
AI-related result types observed in the same query environment.
Structured entity or knowledge-oriented SERP surface.
Related-search elements returned in the live SERP capture.
Distinct SERP surface types observed for this query.
The seed query is one point.Map the intent around it.
Related queries expose adjacent search demand. Classifying those terms with the same declared rules reveals whether the seed belongs to a stable intent neighborhood, a mixed transition zone or a local intent collision.
| # | RELATED QUERY | VOLUME | MAIN INTENT | PROBABILITY | SECONDARY | KD | SERP FEATURES |
|---|---|---|---|---|---|---|---|
NO RELATED-QUERY CAPTURE | |||||||
Intent is classified.TAO diagnoses the shape of that classification.
TAO Intent State is a transparent diagnostic derived from the observed probability distribution. It describes concentration, mixture or collision within the capture and is not a Google ranking metric.
The primary probability is high and the secondary signals are materially weaker.
A secondary intent carries meaningful probability and should be considered in content architecture.
Two or more intent probabilities are sufficiently close that a single-format page may face an ambiguous search environment.
A later TAO diagnostic can flag when result composition appears materially different from the dominant classifier label.
Four evidence channels.One explainable intent record.
Classification, demand, visible result composition and related-query context remain distinct evidence channels. TAO combines them only after scope, capture time and provenance have been preserved.
QUERY → FOUR INTENT STATES → PROBABILITIESRecord the dominant classification and every meaningful secondary signal instead of reducing the query to a label alone.
QUERY → VOLUME + COMPETITION + DIFFICULTYAdd demand and competition indicators to the seed-query record while keeping them separate from intent probability.
QUERY → VISIBLE RESULT TYPES → SURFACE MIXCapture the concrete organic, question, visual, local, commerce, AI and knowledge surfaces surrounding the query.
SEED → ADJACENT QUERIES → INTENT MIXMap nearby demand and classify it under the same rules to expose local consistency, transition and collision.
Search intent probabilities are classifier outputs, not Google ranking factors.
Informational, navigational, commercial and transactional labels describe a modeled interpretation of query purpose. TAO compares that distribution with visible result composition and related-query evidence while keeping its Intent State diagnostic separate from observed search metrics.
Twelve laboratories.One research system.
SERP Lab
Capture live search environments, rankings, SERP features, AI Overviews, PAA and related searches.
Domain Lab
Analyze domain visibility, ranking distribution, top pages, competitors and observable organic footprint.
Keyword Lab
Investigate one seed query through demand, intent, suggestions and query expansion.
Competitor Lab
Compare domains through shared keywords, ranking intersections, competing pages and gap zones.
AI Visibility Lab
Observe AI mentions, sources, cited pages and query-level visibility.
Entity Lab
Observe Knowledge Graph evidence, citations, categories and related-search context.
Search Intent Lab
Compare intent probabilities, SERP composition and the intent mix of related queries.
Internal Linking Lab
Model internal link graphs, crawl depth, anchors and orphan patterns.
Topical Map Lab
Model topic nodes, semantic neighborhoods and coverage architecture.
Content Gap Lab
Identify missing queries, subjects and competitive coverage gaps.
Backlink Lab
Examine referring domains, citations and external link structure.
Retrieval Lab
Investigate retrieval, grounding, sources and RAG-oriented evidence.