Platform Overview

One Loop. Three Phases.
Compounding Outcomes.

The audience intelligence platform that compounds. Built on your first-party audience and interest graph. Composable across every channel you spend on.

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Identity
What You Have TodayCookies and CRM duplicates; ten records per person
What iCustomer BringsOneSource immutable IDs you own, resolved across 50+ pre-unified sources
Audience building
What You Have TodayWeekly CSV exports, stale on arrival
What iCustomer BringsAlways-on cohorts from any signal, updating in place
Activation
What You Have TodayPer-channel uploads at 20-40% match
What iCustomer BringsOne API to 200+ connections at 70-90% match
Measurement
What You Have TodayLast-touch attribution guesses
What iCustomer BringsCausal measurement with holdouts, across every channel
Governance
What You Have TodayUngoverned automations, leaking prompts
What iCustomer BringsBuilt-in guardrails (Harness) and an audit log (a Decision Trace) on every action
Phase 1 · Foundation

Your audience and interest graph.

Identify, match, enrich, and monitor your audience without moving data (Snowflake, Databricks, BigQuery). The Signals Hub reveals website and brand visitors and layers in intent signals. A signals waterfall keeps FIRE scores, Fit, Intent, Recency, Engagement, dynamic and your audiences always on.

Audience

Immutable IDs anchored in your data cloud.

  • Immutable hashed IDs (HMAC-SHA-256), never expire, never rented
  • Identity resolution across people, accounts, devices, emails
  • Activity schema, canonical, time-aware event model
  • AI lookalikes generated from your best buyers
Unified identity graph across people, accounts, devices, anchored in the warehouse

Interest Graph

Signals Hub: 1P + 2P + 3P signals unified, time-aware.

  • Signals Hub: website and brand visitor reveal, in real time
  • Signals waterfall drives a dynamic, always-on FIRE score
  • Match and enrich with consent-based 2P/3P data
  • Time-aware: interest decays, recency matters, freshness compounds
Time-aware interest graph compounding signals from owned and partner sources
Phase 2 · Activation

Audience activation to every channel. Match-rate boosting built in.

One API to every tool you already run: MCP Server, CLI, and iConnect, our meta orchestrator (one router for all your tools), activate audiences and next best actions without tool bloat. 200+ integrations, bidirectional sync, match rates from 20-40% to 70-90%. PII and leak protection built in, your channels stay your channels.

Activations

Every channel, matched + synced.

  • 200+ integrations: Meta, Google, LinkedIn, The Trade Desk, and more
  • One API: MCP Server, CLI, iConnect
  • Bidirectional sync, outcomes flow back to Optimization
  • PII and leak protection on every payload
Decision layer routing audiences to paid, owned, retail media, and agent surfaces
Phase 3 · Optimization

Every cycle smarter. This is your moat.

The Decision Fabric decides who, when, and what: causal AI + the Decisioning Waterfall (ranked rules-then-models) + FIRE scoring. iWorkers, role-based AI teammates, execute. Efficient, domain-adapted AI models (SLMs) run inside your data cloud: no raw data leaves, and compute costs stay low. Every decision is logged as a Decision Trace, auditable, explainable, defensible. Outcomes feed back. Segments sharpen. Agents improve. The platform compounds.

Decision Fabric

A trusted decision layer for humans and agents alike.

  • Decision Engine: Decisioning Waterfall + Causal AI + propensity models
  • iWorkers: CMO orchestrator plus 26 specialized agents (15 D2C plus 11 B2B), 100+ tools
  • Harness: guardrails, evals, and controls for every agent action
  • Four context pillars: audience, brand, domain, outcome ground decisions
  • Decision Traces: every decision logged, signal to iWorker to FIRE to action to outcome
Decision engine selecting next-best-action and routing to channels

Measurement & Learning

Causal proof across every channel, written back into every loop.

  • Outcome memory, every decision and its result, written back to your data cloud
  • Causal AI: incrementality across channels, not last-touch guesswork
  • Feedback loops retune FIRE scores, audiences, and agent prompts
  • Compounding memory, the brand-specific learning asset competitors cannot replicate
Outcomes feeding back into the audience and interest graph as the system compounds
The Decision Trace Loop

From Clicks and Sessions
to Decision Traces

Analytics tracked anonymous traffic. The Decision Trace loop tracks identified activity, human and agent, from event to outcome.

The old world · clicks, views, sessions
  • Anonymous sessions stitched by cookies
  • Clicks count traffic, not people
  • Last-touch guesses at what worked
  • AI agents acting on your behalf are invisible to analytics
The new world · identified activity, traced
  • Humans and agents are first-class, identified actors
  • Every action ties to an immutable ID
  • Each decision links to its outcome in one trace
  • Causal measurement replaces guesswork
EVENTSEvery human and agent action, captured server-side
IDENTITYResolved to immutable IDs: person, account, or agent
SIGNALSScored, consented, FIRE-ranked in real time
DECISIONSWho, when, what, where, inside policy gates
OUTCOMESRevenue, pipeline, and lift tied back to each decision
every result becomes the next signal
loops back to EVENTS
Audit-Ready by Default

Every Decision Leaves a Trace

Who, why, action, outcome. Auditable by humans and agents alike.

WIN-BACK LOOP
Decision Trace #8412
[Sample]
Who
2,314 lapsed buyers, segment: category fatigue
Why
repurchase window exceeded 1.6x · FIRE ≥ 68 · consent verified
Action
Klaviyo flow B + Meta exclusion refresh, inside Harness gates
Outcome
+$41k revenue vs. holdout, 14 days

Every decision. Every reason. Every outcome. Audited.

Integrates with the data clouds, CRMs, and ad platforms your team already runs on

Snowflake logoDatabricks logoGoogle Cloud logoReltio logoHubSpot logoLinkedIn Ads logoGoogle Ads logoMeta logodbt Labs logoSnowflake logoDatabricks logoGoogle Cloud logoReltio logoHubSpot logoLinkedIn Ads logoGoogle Ads logoMeta logodbt Labs logo

Features FAQs

What growth and data teams ask us.

Blank canvases fail: iCustomer ships with domain expertise built in: proven plays, an opinionated data model, and decision science refined across D2C and B2B growth. Your team does not design a system from scratch; you set goals and guardrails, and a fully AI-native stack, built for agents rather than retrofitted with them, does the heavy lifting. Your taste stays in charge.
Your AI tools should be able to ask your data questions: the iCustomer MCP Server exposes audiences, FIRE scores, and decisions as governed tools any agent can call, with the Harness enforcing guardrails on every action. One API instead of tool bloat: your copilots, agents, and workflows all act on the same trusted context, with PII protected throughout.
Yes. If you can describe it in Claude Code or Codex, you can ship it: connect to the MCP Server, and your assistant can query audiences, draft decision loops, and deploy them through the CLI into the warehouse and tools you already run. Claude-native and headless teams run the entire loop as code, versioned in git, without ever opening a UI.
Yes, by design. A brand runs many decision loops at once, and many people run them together: growth sets goals and targets, data governs the foundation, ops approves budgets and policies, and AI agents execute inside those guardrails. Everyone, human or agent, works from one shared context, ontology, and audit trail, so there are no conflicting segments, no double-sends, and no wondering who changed what. One system, one login, every team in the loop.
No. There is one platform and three ways in. Audience Loop is the self-serve way in: product-led, credit-based, one-prompt launch, fast install, every module, Audience through Measurement & Learning. Growth Engineer led is the enterprise way in: a platform and a partner, our engineers embed with your team until your loop compounds, audience-tiered, full causal AI and iWorker depth. And for code-led teams, Claude Code, Codex, or headless CLI, the same loop ships as code into the data cloud you already run. One platform underneath, always.
Locked down by design. Nothing moves and nothing gets replaced: iCustomer is not a CDP, it runs warehouse-native on Snowflake, Databricks, and BigQuery with zero data copies and zero raw-data egress, plus PII and leak protection on every activation payload. SOC 2 Type II certified, consent enforced at decision time, and every action leaves an auditable Decision Trace.

Turn your data
into outcomes

Start free with Audience Loop. Or talk to us about a Platform pilot on your own data warehouse.

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