01Scan
Select a roster, confirm each person, and review a fixed sample of relevant AI questions and public sources.
Confirmed identityQuestion sampleLinked sources
SignalisedAI visibility and identity analytics for talent.
See how AI recommends and describes the people on your roster, against named peers, and what to change to improve their positioning.
Stated question sampleSource-backed descriptionsMeasured re-runs
Illustrative audit layout · no customer results shown
One roster. Clear evidence for each person.
RecommendationAppearances on a stated question sample, with provider and date; variation where comparable repeats exist
DescriptionSelected AI answer excerpt checked against a linked fact source
Next actionReviewer-approved change, owner and date for a comparable follow-up
Availability
Music · production beta
Results are reported only after the question sample, artist matches and evidence pass review.
How it Works
From observations to action.In four steps.
0102Benchmark
Measure how often AI recommends each person on that sample, alongside named peers and the way AI describes them.
Recommendation shareNamed peers
03Prioritise
Review source-backed positioning gaps and agree the bios, metadata, press or source changes to make next.
OpportunitiesEvidence-backed
04Monitor
Repeat the same question sample after agreed changes and compare results with normal run-to-run variation.
Comparable re-runsRecorded actions
Discuss a roster audit
Measured readouts include the exact question sample, provider and dates.
Core Capabilities
Recommendations. Descriptions. Actions.
One music roster workflow. Every reported result states the question sample, provider and evidence status.
Sampled share
SampledWho appears?
Sampled measurement
Recommendation share
The share of completed, relevant benchmark questions that recommend each person, with the sample size and provider stated.
Named peers
ReviewedHow described?
Positioning evidence
AI descriptions
Review sourced AI answer excerpts and the roles, niches and comparisons they attach to your talent against public and supplied material.
Source review
SampledWhat changed?
Measured follow-through
Actions and re-runs
Record agreed positioning changes and compare later results only when the benchmark sample and method remain compatible.
Audit history
Track How AI RecommendationChanges Over Time.
Record agreed positioning actions, then re-run the same question sample. Compare only compatible observations and show normal variation where repeated runs are available.
Recommendation HistoryRecorded ActionsEvidence RefreshesQuestion Coverage
Illustrative history
DatedBaselineSample frozen
ActionChange recorded
Re-runResult compared
FAQ
Frequently asked questions.
What does Signalised actually measure?+
How often AI recommends people on a stated sample of questions, how it describes them, and how those observations compare with named peers.
How should I interpret the benchmark?+
Results describe the stated questions, model and dates. They are not a measure of every AI model or every possible prompt, and different talent verticals are not directly comparable.
Where does the evidence come from?+
Reports link selected sources used in the review. For assisted pilots, we review source permissions, coverage and person matches before including evidence in a buyer readout.
Which talent verticals are supported?+
Music is the production beta. Existing assisted pilots remain available to authorised workspaces.
How current is the data?+
Measured reports state the benchmark provider, question sample and measurement dates; source-only assisted summaries mark measurement as pending. Later results are compared only when the sample and method are compatible.
How do I start a roster audit?+
Contact us about a 10–25 person roster audit. We agree the sample and evidence scope with your team before measurement.
Start with a scoped audit
See where AIrecommends your talent.
Signalised uses a controlled question sample to show when a person appears in AI recommendations and how those answers describe them.
A reviewed pilot can compare named peers on the same questions, then turn sourced gaps into practical positioning actions.