Perceptivity Labs | AI perception systems
Shape how AI perceives your brand.
Perceptivity Labs builds AI perception systems for brands operating in model-mediated markets, where discovery, evaluation, and recommendation increasingly happen inside AI interfaces.
AEO and Studio form the measurement and activation layers of a broader platform for machine-readable brand presence.
01/Research thesis
AI is becoming an interface between markets and brands.
The internet was built for human navigation.
AI systems now interpret it on behalf of buyers.
Discovery, evaluation, and recommendation are becoming model-mediated.
Brands require governed systems for machine perception.
02/Products
A perception platform for answer-led markets. AEO measures. Studio operationalizes.
Measure
AEO
Measures how answer engines understand, cite, recommend, or omit a brand across engines, intents, languages, and regions.
Create
Studio
Creates source-backed, brand-safe assets from measured perception gaps, with governance built into the workflow.
Product surfaces under development
03/Method
Measure representation. Diagnose sources. Govern intervention.
- I
Sense
Ask the buyer questions across ChatGPT, Gemini, Perplexity, Google AI, retail AI and regional-language surfaces.
Query universe | intent universe | engine coverage
- II
Diagnose
Separate visibility from understanding. Find the perception gaps, citation gaps and confidence breaks shaping the answer.
Brand graph | source graph | confidence score
- III
Act
Create and publish faithful knowledge assets, then measure predicted lift against observed answer movement.
Perception report | recommendations | creation layer
04/Perception Report
One operating view. Evidence, sources, and recommended action.
Perception Audit
Perception Report
Illustrative category sample
Confidence
74
Recommendation share
14%
Perception gap
3
Source gap
7
Model representation
The brand is recognized for product innovation, but ownership proof is under-explained in high-intent recommendations.
Recommended intervention
Publish service proof and ownership-cost evidence where third-party citations are shaping the answer.
05/Product philosophy
Hide the complexity. Show the recommendation.
The machinery is complex by design. The product resolves that complexity into a clear, governed recommendation.
Abstracted infrastructure
- LLMs
- Agents
- Vector databases
- Knowledge graphs
Operational question
What is the recommended intervention?
06/Operating metrics
Measures built for operational decisions.
Answer Share
How often AI names, cites or recommends the brand.
Gap Map
Where the answer layer misses, mistrusts, or omits the brand.
Source Control
Which pages, partners, and assets are teaching the answer.
Coverage depth varies by engine.
07/Share of Model
A metric for model-mediated markets.
Share of Model is the share of category answers that name, cite or trust your brand, by engine, intent, language and region. It matters only when paired with confidence and correctness.
- Category leader58%
- Rival31%
- You14%
- Not named22%
Illustrative · model outputs, not a live query. Directional, by engine and language.
08/Operating questions
Questions for model-mediated markets.
Where are model-mediated surfaces entering discovery and evaluation?
How is the brand represented across answer engines and AI interfaces?
Which sources, assets, and claims are shaping that representation?
What intervention is most likely to improve the next answer?
09/Machine Readable
Field notes from model-mediated markets.
A short brief for marketing leaders on how AI interfaces are representing categories, sources, and brand positions.
Receive the brief. Unsubscribe anytime.
Measure machine perception. Govern the response.
Start with a focused read of the category, the relevant answer surfaces, and the perception gaps worth addressing.