THE AIVO METHOD
Four stages to a decision-grade read.
One method, named for the company. Every stage feeds a single read reported as a 95% confidence range and traceable to the prompts, responses, and pages behind it.
HOW WE OPERATE
We sit between the platform data and the work your teams do.
Monitoring tools produce data. Agencies and internal teams produce work. The layer in between decides which numbers are real and which work is worth doing, and it is the layer most brands do not have.
Below us: the data
Raw platform output, your monitoring tool, your analytics. Useful, noisy, and unable to tell you on its own whether a change means anything.
Us: the read
We sample the platforms ourselves, establish what is stable and what is drift, and decide which findings carry commercial weight. Everything we report carries a range and an evidence trail.
Above us: the work
Your content, PR, and technical teams, and the agencies you already use. We write the briefs. They execute. We can measure it afterward because we had no hand in it.
GOVERNING PRINCIPLES
The rules we do not break.
The discipline behind every range we publish.
Ranges, never ranks or points
95% confidence intervals are the default (calculated using the Wilson score interval).
Confidence over volume
The pitch is the range, not the run count.
Sample the default experience
Every read uses the current default model per platform, because that is what users receive.
Control first
The baseline is an unpersonalized, zero-history state, so segment effects are measured as departures from a known control.
Training vs retrieval view
Separate what the model knows from what it looks up.
Brand vs category lens
Direct brand queries and category queries measured separately.
Traceable to evidence
Every finding cites prompts, responses, and citations.
Independent. Never the fix.
AIVO measures and refers remediation out.
A
STAGE 01· before the clock
Architecture
Decide what matters to the business and size the sampling for a defensible read.
Yields
Prompt architecture · sampling design
I
STAGE 02
Interrogation
Sample the platforms as a default user sees them and capture everything the answer reveals.
Yields
Raw packet (client keeps) · enriched dataset
V
STAGE 03
Value-mapping
Turn observation into a short list of what is worth acting on, in priority order.
Yields
Executive read · 90-day roadmap
O
STAGE 04
Operationalize
Hand the team the briefs, plan, and measurement to act. Refer the fix out.
Yields
Workbook · briefs · PM plan · measurement
A
STAGE 01· before the clock
Architecture
Decide what matters to the business and size the sampling for a defensible read.
Yields
Prompt architecture · sampling design
I
STAGE 02
Interrogation
Sample the platforms as a default user sees them and capture everything the answer reveals.
Yields
Raw packet (client keeps) · enriched dataset
V
STAGE 03
Value-mapping
Turn observation into a short list of what is worth acting on, in priority order.
Yields
Executive read · 90-day roadmap
O
STAGE 04
Operationalize
Hand the team the briefs, plan, and measurement to act. Refer the fix out.
Yields
Workbook · briefs · PM plan · measurement
Every stage resolves to 95% confidenceObserved rate + Confidence interval
Every finding is a range at 95% confidence, and every range traces back to the prompts, responses, and pages behind it, so you can reproduce any number we report.
THE SUBJECT
Eight representation dimensions.
What the method measures across every stage and every read.
Presence
When a relevant prompt is asked, does the brand appear at all, and how often.
Comparison
When named against a competitive set, how does AI position the brand relative to others.
Recommendation
Beyond mention, how likely is AI to actually pick or endorse the brand.
Accuracy
Is what AI says true, and where is it wrong in a way that carries commercial or accountability weight.
Sentiment
Warm, neutral, or hostile, and toward which audience.
Sources
Where AI draws its picture of the brand, and how much of that the brand can influence.
Access
Can the platforms technically read and ingest the brand's own properties.
Audiences
How representation shifts across audience segments and personas.
DEEP DIVES
Explore the method.
Signal vs Noise
How much does a number have to move before the change is real?
Read moreWhich Metrics Hold Up
Which AI visibility metrics are stable and which are noise?
Read moreError Types
The seven ways AI gets a brand wrong, with examples.
Read moreSample Size
How many prompts and runs it takes to measure with confidence.
Read moreAudit vs Monitoring
The difference between monitoring and an independent representation audit.
Read moreConfidence Intervals
What a 95% confidence band actually means.
Read moreWhat It Costs
What an independent audit costs and what is included.
Read moreBriefing Leadership
How to brief a board on AI brand representation.
Read moreKnow what AI says about you.
And how sure you can be.
Independent measurement of how AI represents your brand. One market, one language, delivered in 7 to 14 business days.
30-minute scoping call · no commitment