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Platform · Sfere ID

One consented
customer profile

Sfere ID is a customer data platform and real-time identity graph that resolves fragmented, anonymous touchpoints into one golden customer profile, consented and yours to own

  • Real-time graph
  • Multi-identifier
  • Consent built in
  • Yours to own

One week, one customer

The story of Maya,
as the graph sees it

Six ordinary moments from one week. No configuration and no rules to learn. Scroll and watch a blank card become a person. The card on the right is the only thing to follow

fig. 01 · monday

A stranger browses

Someone reads two pages on the website and leaves. No login, no name - the only thing anyone saw is a cookie: c_9f2. Your analytics counts a "visitor." That's the whole relationship so far

→ the card gets its first mark: one dashed chip, and an UNSIGNED stamp

fig. 02 · wednesday

A receipt buys a name

In store, someone buys a jacket and asks for the receipt by email: maya@…. Now the card has a holder: Maya. The order number o_8841 is stapled on with it

→ the HOLDER field writes itself in. Stamp: IDENTIFIED

fig. 03 · thursday

The cookie gives her away

Evening: a sign-in to the online store from a laptop, carrying the same cookie as Monday's stranger. So Monday was never a stranger. It was Maya, before we knew her. History rewrites itself onto her card

→ the cookie chip pulses: Monday's visit back-fills into Maya's story

fig. 04 · friday

The phone joins too

The app opens on a phone, then logs in with the same email. A loyalty scan follows at the store register. Phone, laptop, inbox, receipt, register: five ways she shows up, one person behind all of them

→ gold foil slides on. Stamp: GOLDEN PROFILE. Customer #48211

fig. 05 · saturday

The tablet tempts the graph

A family tablet: kids' shows all afternoon, then Daniel logs in, then Ava. One device, two different people. A greedy graph would fuse them into one "customer" and call it complete. This one refuses

→ the shared device tries to bridge two cards. The guardrail blocks it: HELD APART

fig. 06 · the count

One week, nine records, three people

Your tools counted nine separate records this week. The graph holds three customers, plus one honest "we don't know yet." Nobody invented, nobody merged by mistake, every join can show its evidence

0records your tools counted
0resolved customers
0held apart, on purpose
Sfere ID · customer cardfig. 00
?
-
-
identifiers - none yet cookie c_9f2 email maya@… order o_8841 device d_311 device d_900 loyalty l_2210

waiting for the first touch…

ids: 0 · status: blank

Same records, different rules

What if?

The story you just watched is the graph under Sfere ID's defaults. Flip each scenario to see the same week resolve a different way, because the rules are yours to define

If email weren't evidence…

Customer #48211 · Mayaemail · loyalty · order · 2 devices · cookie⚡ golden profile

with email as evidence: one customer

web + online storecookie c_9f2nameless
order o_8841no linkable evidencenameless
app + registerdevice d_900 · loyalty l_2210nameless

same week, three "strangers"

Take one identifier away and one customer lands as three separate records. This is why identity can't hang on a single hard key

If the guardrail were off…

Danieldaniel@…held apart
Avaava@…held apart

guardrail on: two people, kept two

daniel@… + ava@…fused by shared device d_tab⚠ over-merged

two real people, one wrong profile

Without the guardrail, the family tablet collapses a married couple into one "customer," and every message downstream is wrong for one of them

If a customer is a household…

Danieldaniel@…held apart
Avaava@…held apart

definition: one person

🏠 Household H-07daniel@… · ava@… · shared tabletone shared-account household

definition: household, merged on purpose

Decide what a customer means for your business (one person, a family sharing one account, a company with several buyers) and resolution follows your model

How it works

Identity that holds up under audit

Sfere ID keeps resolution accurate, explainable, and current at every stage, starting with the first anonymous visit

  1. 01

    Recognize more customers in real time

    The same customer shows up as a loyalty scan in store, a laptop in your online store, and an anonymous browser in between. Sfere ID joins those appearances into one profile, so recognition survives every device switch

  2. 02

    Real-time stitching, not overnight batches

    The moment a customer acts is the moment to respond. Profiles update in real time as signals arrive, with no waiting on a nightly batch to learn what already happened

  3. 03

    Customer definitions that fit your model

    Decide what a customer means for your business: one person, a family sharing one account, or a company with several buyers. Resolution follows your definition instead of forcing one shape

  4. 04

    One journey, read as one story

    Anonymous behavior is tracked from the first touch and joined to the profile once a customer identifies, so the pre-signup history and everything after it read as one relationship

  5. 05

    Stitching rules you define

    Resolve on any combination of identifiers rather than one hard key, and apply your own data-quality rules so identity stays accurate as profiles merge

Consent & residency

Governed at the data layer,
not bolted on

The hardest part of a customer graph isn't collecting data; it's owning it responsibly. Consent, residency, and deployment are built into Sfere ID from the profile up, so the same record that powers activation is also the one that keeps you compliant

  • Consent is captured and enforced at the profile itself: governance built in, not patched on top
  • Compliance is built into the data layer, CCPA/CPRA included
  • Deploy in your own cloud, on-premise, or fully managed. The choice is yours
  • Your customer data never leaves your control. Activation decisions run inside your environment
See data residency

Connect your data

Connect every source, keep it yours

Sfere ID sits on an open connector framework and a warehouse you own. Bring the touchpoints you already have, with no rip-and-replace and no vendor holding your customer data hostage

  • 01

    An open connector framework ingests events from online stores, point of sale, app, web, streaming, ticketing, and social, with no parallel database to maintain

  • 02

    Map your existing user IDs with custom identifiers and attributes, so the profile fits the data you already keep

  • 03

    Stream to a columnar analytical warehouse you own, so the platform stays composable with your stack

  • 04

    A clean REST API and client SDKs cover anything custom, so every capability is programmable

See what developers get

How resolution works

Merge what matches,
keep people distinct

Resolution matches signals on any identifier, merges them into one golden record, and applies guardrails so distinct people stay distinct

  1. 01

    Identifiers, not one hard key

    Email, device, app ID, order or ticket number, loyalty ID, custom IDs: each is a first-class identifier with its own merge rules. Resolution connects events on any of them, so you are not forced to hang identity on a single field that is often missing

  2. 02

    A live graph that merges and splits

    New signals either match an existing profile, create one, or merge several into a golden record as the graph learns the relationships. Contested matches follow rules you set, rather than silently collapsing two real people into one

  3. 03

    Guardrails against false merges

    Per-identifier limits stop a shared device or family login from over-merging distinct people. When a limit would be crossed, the system holds the records apart instead of guessing. Correct identity beats complete-but-wrong identity

Put the profile to work

One resolved profile drives segmentation, journeys, and paid audiences in Sfere Activate

See Sfere Activate

Questions

Identity questions engineers ask

Is resolution real-time or a nightly batch?
Real-time. Profiles update as signals arrive, so the customer you recognize reflects what just happened rather than what a batch job will report tomorrow. Historical and warehouse data map into the same graph so batch context is not lost
Deterministic or probabilistic matching?
The model is graph-based and rule-driven: it connects events on shared identifiers you define, with per-identifier merge limits and priorities you control. That keeps resolution explainable and auditable rather than a black-box score
How does consent fit in?
Consent is captured and enforced at the profile itself, not patched on downstream. The same governed record that powers activation is the one that keeps you compliant
Do we have to move our data to you?
No. Sfere ID sits on an open connector framework and a warehouse you own, in your cloud account, on-premise, or fully managed. Bring the sources you already have and stay composable with the rest of your stack
What keeps the graph from getting polluted by dirty data?
Resolution runs alongside schema validation as events land, so graph hygiene is enforced on the way in rather than repaired by a nightly cleanup job. You apply your own data-quality rules (normalizing formats, catching typos, filtering internal test traffic), and per-identifier limits hold contested records apart instead of guessing a merge

Know who's in front of you while they're still there

Bring your messiest export to a walkthrough. We'll run it through Sfere ID live and show you the golden profiles, the merge evidence behind every one of them, and the records the guardrail refused to guess on