By Industry
One foundational layer that serves as an AI model factory, customized for each industry and vertical.
About 86% of sessions never produce an identity, and less than 1% are ever truly understood. Kahoona generates context from Digital Body Language, before identity, for every actor, human or agent, so each industry gets a model tuned to how it actually buys.
Fintech
Analytics
+12%
Revenue
$12.4M
Quote Abandoner
Travel
Aviation
Oct 12
JFK
→
LAX
Book Now
Retail
E-commerce
2
Wireless Headphones
$49.99
Add to Cart
High-Intent Buyer
Last-Minute Booker
PERFORMANCE
+124%
Conversion Lift
MODEL V.2.4
Intent Segmentation
Real-time predictive scoring
AI
ML
The Blind Spot, In Every Industry
More Investment. More infrastructure. But the intelligence gap is widening.
The web ran on persistent identifiers: cookies, device IDs, logins. Privacy pulled that foundation out, and nothing replaced it. Sessions are under 60 seconds and shrinking, and AI agents multiply anonymous traffic at machine speed. More money and more tools, but the gap between the content you need and the context you have keeps widening.
Contextless before Kahoona
Context-rich with Kahoona
Brand & Retail
Travel
Financial
Telecom
Financial Services & Insurance
Predict who will complete a quote and buy, and what they're worth, before they identify.
High-value financial and insurance journeys happen online and anonymously, and decide clearly. Kahoona predicts, from behavior alone, which visitors will complete a quote and buy, and how much they're worth.
No engage on the real objection, not a generic reminder - running in production for global FSI groups.
Before and After
Each persona, side by side: what your stack sees without Kahoona, and what it can pin on with it.
Without Kahoona
The Coverage Comparer
A consumer exploring different options, comparing coverage and frequently toggling between providers.
Without:
A comparer weighing deductibles and add-ons, high predicted value, likely to convert.
Prior hits in the quote flow, but no media focus, poor value signal to date.
With Kahoona:
A high-intent applicant who started a quote and dropped - not seen.
First hits in the quote flow, follow up by media, pass the value signal to sales.
Without Kahoona
The Quote Abandoner
A high-intent applicant who started a quote and dropped. No coverage on the last step, left it pending.
Without:
A high-intent applicant who started a quote and dropped - not seen.
With Kahoona:
No engage on the real objection, not a generic reminder.
What Kahoona Generates For Every Actor
Six dimensions, built from behavior alone, before identity.
Identity
Who is this?
Anonymous, predicted, or known; seller vs Rx.
Affinities
What do they want?
Price sensitivity, coverage amnesty, or brand loyal.
Behavioral
How do they act?
Coverage quote abandonment, or revenue loyalist.
Intent
What are they doing?
Researching, quoting, or ready to buy.
Journey
Where in the funnel?
Early research, mid-quote, or at-the-decision.
Value
What is this actor worth?
Predicted policy value and conversion likelihood, so spend follows the actors worth the most.
The Value
01
Spend on the quotes that will close
Predict Conversions and intent from behavior, so paid media and the quote flow follows the actors worth the close.
02
Grow add-on and coverage value
Read who is likely to add complementary coverage, and guide them to it before they leave.
03
Compliant by design
No PII, no fingerprinting, generated from behavior on your own site, so business value and privacy never trade off.
Fits what you already run.
Fits your quote flow, paid media, and CRM, and your compliance run by FSI, so cross-site learning, generated on your own site.
The missing primitive.Built.
Explore the Platform
The Context Generation Layer of the Agentic Web
© 2026 Kahoona Inc.
New York
By Industry
One foundational layer that serves as an AI model factory, customized for each industry and vertical.
About 86% of sessions never produce an identity, and less than 1% are ever truly understood. Kahoona generates context from Digital Body Language, before identity, for every actor, human or agent, so each industry gets a model tuned to how it actually buys.
Fintech
Analytics
+12%
Revenue
$12.4M
Quote Abandoner
Travel
Aviation
Oct 12
JFK
→
LAX
Book Now
Retail
E-commerce
2
Wireless Headphones
$49.99
Add to Cart
High-Intent Buyer
Last-Minute Booker
PERFORMANCE
+124%
Conversion Lift
MODEL V.2.4
Intent Segmentation
Real-time predictive scoring
AI
ML
The Blind Spot, In Every Industry
More Investment. More infrastructure. But the intelligence gap is widening.
The web ran on persistent identifiers: cookies, device IDs, logins. Privacy pulled that foundation out, and nothing replaced it. Sessions are under 60 seconds and shrinking, and AI agents multiply anonymous traffic at machine speed. More money and more tools, but the gap between the content you need and the context you have keeps widening.
Contextless before Kahoona
Context-rich with Kahoona
Financial Services
Financial Services & Insurance
Predict who will complete a quote and buy, and what they're worth, before they identify.
High-value financial and insurance journeys happen online and anonymously, and decide clearly. Kahoona predicts, from behavior alone, which visitors will complete a quote and buy, and how much they're worth, so your quote flow and paid media focus on the actors that matter. It also fills the compliance tool these teams live by.
No engage on the real objection, not a generic reminder - running in production for global FSI groups.
Before and After
Each persona, side by side: what your stack sees without Kahoona, and what it can pin on with it.
Without Kahoona
The Coverage Comparer
A consumer exploring different options, comparing coverage and frequently toggling between providers.
Without:
A comparer weighing deductibles and add-ons, high predicted value, likely to convert.
Prior hits in the quote flow, but no media focus, poor value signal to date.
With Kahoona:
First hits in the quote flow, follow up by media, pass the value signal to sales.
Without Kahoona
The Quote Abandoner
A high-intent applicant who started a quote and dropped. No coverage on the last step, left it pending.
Without:
A high-intent applicant who started a quote and dropped - not seen.
With Kahoona:
No engage on the real objection, not a generic reminder.
What Kahoona Generates For Every Actor
Six dimensions, built from behavior alone, before identity.
Identity
Who is this?
Anonymous, predicted, or known; seller vs Rx.
Intent
What are they doing?
Researching, quoting, or ready to buy.
Affinities
What do they want?
Price sensitivity, coverage amnesty, or brand loyal.
Journey
Where in the funnel?
Early research, mid-quote, or at-the-decision.
Behavioral
How do they act?
Coverage quote abandonment, or revenue loyalist.
Value
What is this actor worth?
Predicted policy value and conversion likelihood, so spend follows the actors worth the most.
The Value
01
Spend on the quotes that will close
Predict Conversions and intent from behavior, so paid media and the quote flow follows the actors worth the close.
02
Grow add-on and coverage value
Read who is likely to add complementary coverage, and guide them to it before they leave.
03
Compliant by design
No PII, no fingerprinting, generated from behavior on your own site, so business value and privacy never trade off.
Fits what you already run.
Fits your quote flow, paid media, and CRM, and your compliance run by FSI, so cross-site learning, generated on your own site.
The missing primitive.Built.
Explore the Platform
The Context Generation Layer of the Agentic Web
© 2026 Kahoona Inc.
New York