Decision-Layer Architecture
AvelinLabs is built around a decision-layer architecture: inputs are normalized, enriched, scored, ranked, and returned as structured outputs that can be consumed by applications, dashboards, and workflows.
What Is a Decision Layer?
Section titled “What Is a Decision Layer?”A decision layer sits between fragmented data and the systems that need to act on it. Its job is to turn raw or inconsistent inputs into structured evidence that can be evaluated, stored, displayed, routed, or audited.
In the current beta, the first documented decision domain is U.S. labor market intelligence. The same architectural idea can support broader decision workflows over time, but the public documentation focuses on the labor market intelligence domain.
Input Signals
Section titled “Input Signals”For the current domain, AvelinLabs can work with inputs such as:
- job titles and descriptions
- real job-market signals
- location or market context
- explicit or inferred skill signals
- O*NET 30.3-grounded occupation, essential skill, transferable skill, software-skill, and related-occupation context
These inputs provide the evidence needed to classify, enrich, score, rank, and explain results.
Conceptual Flow
Section titled “Conceptual Flow”At a high level, AvelinLabs follows a decision flow:
- Normalize incoming text and request context.
- Extract or identify useful signals, including skills and occupation cues.
- Enrich results with domain context such as O*NET 30.3-grounded occupation intelligence.
- Score and rank candidate outputs.
- Attach confidence, uncertainty, and quality signals.
- Return structured outputs that downstream systems can use.
This is a public conceptual model, not a low-level system map.
Output Model
Section titled “Output Model”AvelinLabs outputs are designed to support both automated systems and human review. Depending on the API area, responses may include:
- ranked occupation matches
- job classification results
- skill evidence
- market context
- confidence and trust fields
- uncertainty and ambiguity signals
- explanations and quality signals
- decision labels for routing
Why Confidence and Explainability Matter
Section titled “Why Confidence and Explainability Matter”Decision outputs are most useful when downstream systems understand how strongly a result is supported. Confidence, uncertainty, quality, and explanation fields help products decide whether to accept a result, show alternatives, request review, or collect more information.
Current Beta Scope
Section titled “Current Beta Scope”AvelinLabs currently focuses on U.S. labor market intelligence. The documented beta capabilities include job analysis, occupation enrichment, skill signal extraction, scoring, ranking, market context, and confidence-aware outputs, including conservative confidence handling for weak or noisy inputs.
Additional decision domains may be added over time, but the current public documentation focuses on labor market intelligence.