AxyoAxyoDigital Identity Resolution Request a demo
Digital identity resolution

Turn strangers into customers, and stop impostors

Axyo tells customers, new visitors, bots and impostors apart, so each one gets the right offer or the right check.

Request a demoSee it work

How many of your returning customers does your site recognize?

Pick how your site recognizes people today.

Illustrative, 100 visits
Your site today
With Axyo
Not recognizedCustomerNew visitorBotImpostor

What she sees on your site

The value for your business

Telling a person from a bot, or one visitor type from another, uses no personal data. Axyo names someone only when they are already your customer or have identified themselves, and with their consent.

Recognize, decide, act on your site

You choose the actions and the limits. Axyo decides, visitor by visitor, when each one fires.

Recognize

Who is this, and how sure are we?

Decide

The next best action, weighed by value and risk.

Act on your site

Within the rules and limits you set, measured against a group that does not get it.

See the actionsHow it works
See it work

Follow one visitor from first visit to checkout

Website visitCheckoutLinkProtectResults
Play the demoRetail example
Why now

Recognition is getting harder, and fraud is getting faster

7 daysbefore Safari forgets visitors, for cookies set by scripts9
18%of shoppers who abandon a cart quit over creating an account6
4×more lost to false declines than to actual fraud4
+78%account takeover attacks, early 20263
AI agents are starting to shop, with their own payment tokens14
Get started

A working prototype for your business in about a week12

Pick your first actionsAbout 2 hours
Connect your data1 to 2 days
Prove it on your dataA few days
Founders built risk, data and AI platforms at a Fortune 100 payments company30+ data and AI patentsComputed, not generatedSee how to get started
Use cases

What your site can do, visitor by visitor

See it work · illustrative11One good customer, recognized and protectedRetail example
Identity graph
Exact linkFuzzy matchSupporting evidenceHouseholdNot consistent
The decision
Next: How it works
Decision-grade AI

Predictive AI and AI agents, with your team in control

Predictive AIFinds

Who each visitor is, and what needs attention.

AI agentsPrepare

The offer, the passkey check or the case.

Your teamControls

Accepts or overrides each action, or puts routine ones on autopilot.

Ready for AI shoppers14Computed, not generated
How it works

From your raw data to the next best action

Each layer builds on the one before it.

Next: Get started
Get started

A working prototype for your business in about a week12

Start small, prove it on your data, then grow.

About 2 hoursPick your first actions

Two or three to start, and the outcomes that matter most.

The steps
  • A discovery session with your ecommerce, marketing, fraud and IT leads
  • Before sign-in, at sign-in and checkout, or both; and Option 1, Option 2 or both
  • One success measure and your baseline, agreed
1 to 2 daysConnect your data

Read-only extracts. See the data list

The steps
  • A data walkthrough with your system owners
  • Secure, read-only access agreed with your security team
  • Your AI-ready Data, designed in your terms
  • A data quality report and sign-off
A few daysProve it on your data

The lift, measured against a group that does not get the actions.

The steps
  • Identity graph built on your history
  • Match accuracy checked on a labeled sample
  • Results measured against your baseline
  • An executive readout and a plan to go live
How success is measured

We agree the baseline first, then measure against it

MeasureWhat it tracksGoal
Recognition rateVisits and checkouts where the customer is recognizedUp
Conversion of recognized customersOrders and sign-ups from recognized visitors and customersUp
Time to paySeconds from checkout to paidDown
Welcome credits to returning customersCredits given to people who already buyDown
False decline rateGood orders declined as fraudDown
Account takeover lossesLosses and chargebacks from takeoversDown
Bad bots in offers and reportsOffers and visits that come from botsDown
Match accuracyLinks that are right, and true links foundUp

What recognition is worth in the market

+51%conversion for guest shoppers PayPal Fastlane recognizes7
+14%returning-shopper conversion with Stripe Link13
3xfaster checkout for Stripe Link customers13
Request a demo
Who benefits

Value for every team that touches the customer

Owner or general manager
  • More sales from customers you already have
  • Fraud losses down, every decision explained
Ecommerce and marketing
  • Personalize from the first visit
  • Offers by value, not blanket codes
Fraud and finance
  • Takeovers stopped before shipping
  • Fewer good orders declined
Customer service
  • Fewer “why was I declined” contacts
  • Accounts restored fast after a takeover
IT and data
  • Read-only, no rip-and-replace
  • One AI-ready data set, reused for the next use case

We’ve built this before, at enterprise scale

Built by people who did it at scale

Our founders built the risk, data and customer 360 platforms at a Fortune 100 payments company, and hold 30+ data and AI patents.

Computed, not generated

The same inputs give the same answer, and every link traces back to its evidence.

An identity data partner

Resolves anonymous visitors to real people, even with only a first-party cookie.

See it in your business.

Share a sample of your website and customer data. We will show you who your returning visitors are, what they are worth, and where the risks are.

Request a demoSee it work
Sources and notes
  1. 1
    McKinsey & Company, “The value of getting personalization right, or wrong, is multiplying” (Next in Personalization 2021): 71% of consumers expect personalized interactions and 76% get frustrated when they do not get them; personalization most often drives a 10 to 15% revenue lift. Source
  2. 2
    Imperva (Thales), 2025 Bad Bot Report: automated traffic was 51% of all web traffic in 2024, and bad bots 37%. Source
  3. 3
    Signifyd 2026 State of Fraud Report, as summarized by eCommerce Fastlane: account takeover up 78% and card testing up 175% in January to April 2026. Source
  4. 4
    SEON, “False declines: the fraud tax you’re imposing on yourself”: false declines cost $213 billion globally in 2025; fraudulent ecommerce transactions totaled about $56 billion. Source
  5. 5
    ClearSale: 41% of consumers never return to a merchant after a false decline. Source
  6. 6
    Baymard Institute: 70.22% average cart abandonment rate across 50 studies; 18% of US shoppers who abandoned said the site wanted them to create an account. Source
  7. 7
    PayPal, Fastlane launch: businesses using Fastlane saw a 51% average increase in conversion among recognized shoppers versus traditional guest checkout. Source
  8. 8
    Amperity: the confidence needed that a record is the right person varies by the type of interaction. Source
  9. 9
    WebKit (Safari), “Full Third-Party Cookie Blocking and More”: storage that scripts can write, including first-party cookies set by scripts, is capped at 7 days. Source
  10. 10
    Industry standards the target data model draws on: schema.org (people, products and offers); OpenID Connect Core (standard identity attributes); NIST SP 800-63-4, Digital Identity Guidelines (July 2025); OWASP Automated Threats to Web Applications (bots, credential stuffing, fake account creation); and the Federal Reserve’s FraudClassifier and ScamClassifier models (fraud classification). schema.org · OpenID Connect · NIST · OWASP · Federal Reserve
  11. 11
    The demo, examples, sample cases and the example data model are illustrative. Maya Chen, the store, the devices, the dates and every number are fictional, with realistic patterns.
  12. 12
    Measured from the day your data is shared. The time depends on data availability (extracts shared and access approved), the number of sources, how quickly your team reviews matches, and your security reviews.
  13. 13
    Stripe, Link: returning-user conversion up an average of 14% for businesses with many repeat customers; Link customers check out 3x faster than non-Link customers. Source
  14. 14
    PYMNTS, “Visa, Mastercard, PayPal fuel agentic AI commerce boom”: Visa and Mastercard are rolling out payment tokens for AI agents, each tied to the consumer’s card. Source
  15. 15
    Standards the matching attributes follow: California’s definition of a unique identifier, Cal. Civ. Code 1798.140 (device identifiers, IP addresses, cookies, mobile ad identifiers, customer numbers, telephone numbers, and other persistent or probabilistic identifiers); the EMV 3-D Secure data elements card networks ask merchants to send at checkout (cardholder name, email, phone, billing and shipping address and whether they match, account age, browser IP address, screen size, time zone, and device information); the Unified ID 2.0 normalization rules for matching on email and phone (normalized, then SHA-256 hashed; phones in E.164 format); and IAB Europe’s guide to identifier types (people-based, device-based and probabilistic). Cal. Civ. Code 1798.140 · EMV 3-D Secure device data · Visa Secure data best practices · UID2 normalization · IAB Europe identifiers
  16. 16
    Signicat, The Battle to Onboard 2026, a survey of consumers across Europe: 60% abandoned an online financial application in the last year, down from 68% in 2022, citing excessive information requests and lengthy processes. Source
Illustrative examples11