AI-enabled contextualization and engagement across every channel, so every touchpoint knows the customer.
This isn't a CDP or a marketing automation tool. It's a contextualization architecture: a system that unifies identity, behavioral signals, and structured rules to deliver relevance at the moment of engagement, across any channel, including AI-powered workflows.
Each layer is independently addressable via API: systems can query for contextual suggestions at runtime, or the framework can push proactive engagement based on rule evaluation.
Two primary modes of operation:
The customer profile is the authoritative record for each person. A single individual may engage across multiple institutional contexts and roles—an instructor at one institution, a student at another, or both simultaneously. Every activity, order, and support interaction is tagged with product and course context, building an integrated picture that powers every downstream decision.
A single profile record per person links every system identity—IAM accounts, CRM records, platform logins—with global attributes like opt-in status, tags, and country. The profile is the root of all contextual intelligence.
One person can hold multiple engagement contexts: an instructor at Institution A teaching biochemistry, and a student at Institution B taking statistics. Each context carries its own institutional affiliation, courses taught or taken, and role-specific tags.
Every course (e.g., biochemistry) has a unique Course ID mapped to all titles—ours and competitors—as well as instructor resources, surveys, and marketing campaigns. Products are ISBNs. This reference data graph is what makes precision targeting possible.
Every explicit action—placing an order, logging a support case, requesting a demo, downloading resources—is automatically tagged with both product (ISBN) and course (Course ID) context, producing structured data that feeds tags, signals, and the rules engine.
Signals capture what the person did or didn't do: demoed a product, sampled a title, completed a survey, requested help. Signal processing enriches each event with course and product context, building a behavioral fingerprint that sharpens with every interaction.
AI consumes the full identity graph—profile, contexts, signals, course–product mappings—to determine what the customer should see next: what will interest them, where they need assistance, and how they can extract more value from the platform and its resources.
Standardized attributes derived from product usage, orders, support cases, survey data, and profile declarations. Tags are the customer's state.
| Category | Examples |
|---|---|
| Role | Instructor, Student, Administrator, Decision-Maker |
| Product | Active adopter, trial user, lapsed, multi-product |
| Engagement | Webinar attendee, event registrant, content downloader |
| Lifecycle | New user, renewal window, at-risk, champion |
User activities enriched with context: the events that trigger rules and feed the relevance engine. Signals are the customer's momentum.
Every engagement event feeds back as a new signal. Tags update. Rules re-evaluate. The customer's context sharpens with every touchpoint, and the next interaction is more relevant than the last.
Rules are the decision layer: structured conditions that evaluate a customer's profile, signals, and context to determine what content, action, or experience to surface. Rules are composable, prioritized, and auditable.
| Component | Purpose |
|---|---|
| Conditions | Tag matches, signal thresholds, date ranges, cohort membership |
| Actions | Surface content, trigger messaging, update tags, notify systems |
| Priority | Weighted scoring to resolve conflicts when multiple rules match |
| Suppression | Frequency caps, deduplication, and cooldown windows |
Rules support both proactive push (scheduled evaluation) and real-time pull (API request from any channel at the moment of engagement).
Beyond binary rule matching, the scoring engine uses content-based filtering to rank all eligible content by how closely it aligns with the customer's profile, behavior, and current context.
Content metadata matched against customer tags and attributes using embedding similarity, producing a relevance score that adapts as the profile evolves.
Recent behavioral signals amplify or suppress relevance: a customer actively exploring a topic area sees more depth, not more breadth.
A weighted formula combines metadata similarity and signal weight into a single relevance score, returning recommendations in rank order with full auditability.
The same contextualization powers every touchpoint. The experience layer adapts the delivery format; the intelligence stays consistent.
| Channel | How context is applied | Example |
|---|---|---|
| Website | Contextual banners, personalized resource recommendations, dynamic content blocks | Returning adopter sees renewal timeline and new features, not generic marketing |
| Segment-of-one content selection, triggered sequences, contextualized CTAs | At-risk account receives targeted success resources, not a sales push | |
| Product | In-app modals, guided workflows, contextual help surfaces | New user gets onboarding guidance tied to their specific course and role |
| AI Agents | API-driven context retrieval for agentic workflows and conversational interfaces | Support agent pulls customer context, history, and recommended actions in real time |
| Events | Registration enrichment, personalized follow-up, session recommendations | Webinar attendee receives discipline-specific resources, not generic recordings |
| Before | After | |
|---|---|---|
| Identity | Fragmented across systems | Unified, resolved at every touchpoint |
| Targeting | Broad segments, manual lists | Signal-driven, rule-evaluated, individually ranked |
| Channels | Each operates independently | Shared intelligence, channel-adapted delivery |
| AI Readiness | No structured data for agents | API-first context for any AI workflow |
| Learning | Static: set and forget | Every interaction enriches the next |
A contextualization architecture that unifies identity, behavioral signals, and structured rules to deliver relevance at the moment of engagement, powering AI-enabled personalization across any channel.
Every touchpoint knows the customer. Every interaction makes the next one better.