Platform Overview
Live on the platformIntelligence begins with governed data, clear context and accountable services.
How intelligence is assembled, governed and delivered
Lifelong Careers intelligence architecture describes the path from source information to useful, reviewable outputs. It should connect data ingestion, identity and permissions, profile services, knowledge structures, analytics, AI models and user-facing experiences through explicit contracts. The architecture is not a single model; it is a governed set of components selected for different tasks.
Understand the architecture behind each intelligence experience and review how data, models and controls work together.
The governed AI chain (genome -> twin -> readiness -> graph -> knowledge) is live and independently tested.
In depth
A layered architecture
Source connectors and user inputs feed validation and normalization services. A profile and evidence layer stores authorized human capital records. Taxonomies and graph structures represent relationships among skills, occupations, learning and credentials. Feature services prepare task-specific context. Rules, statistical models and generative services produce different kinds of outputs, which are exposed through APIs and product interfaces.
Data and knowledge foundations
Quality, lineage, consent, recency and access scope should travel with information. Reference taxonomies help align terms such as occupations, skills and qualifications, while knowledge graphs can represent relationships that are difficult to express in flat records. Vector retrieval may help locate semantically related material, but retrieved content must remain attributable and permission-filtered.
Model and service orchestration
The architecture should route a task to an appropriate service: deterministic rules for policy checks, analytics for descriptive summaries, predictive models for forecasts, and language models for conversational or synthesis tasks. Agent orchestration may coordinate tools but should not erase the boundary between suggestion and action. Every service should declare inputs, outputs, dependencies and failure behavior.
API-first integration
The web application should call documented services through authenticated APIs. Contracts need stable schemas, versioning, validation, idempotency where relevant, trace identifiers and clear error responses. Events can support asynchronous enrichment and workflow updates, provided event payloads are minimized and consumers enforce authorization.
Observability and change control
Measure latency, availability, error rates, model quality, data freshness and safety signals. Version datasets, prompts, models and rules. Changes should pass offline evaluation, staging tests and controlled release gates. A fallback mode should be defined when a model, connector or knowledge source is unavailable.
How it works
Capability domains
Frequently asked questions
Does one AI model power the platform?
The architecture may use different services for rules, analytics, prediction, retrieval and generation. The deployed configuration must be verified.
How is information protected?
Access, purpose limitation, minimization, retention and service boundaries should be enforced through documented controls.
Platform Overview
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