AvelinLabs Changelog

v0.1.0 - Initial Public Beta Content

Initial AvelinLabs public beta content.

Included:

• public introduction to AvelinLabs as a decision intelligence API platform
• API-first onboarding flow for registration, email verification, login, and runtime API-key creation
• developer documentation for job analysis, job classification, occupation intelligence, market intelligence, and health/readiness endpoints
• request and response field explanations for the documented public API areas
• guidance on how to interpret scores, confidence, occupation matches, skill signals, and explanations
• use case pages focused on U.S. labor market intelligence, skill intelligence, and O*NET 30.3-grounded occupation intelligence
• links to the official AvelinLabs API examples repository for executable Python, cURL, payload, response, and Postman assets

v0.1.1 - O*NET 30.3 Documentation Refresh

Updated public beta documentation to reflect the current O*NET 30.3-grounded labor-market intelligence scope.

Included:

• clearer public wording for O*NET 30.3 occupation intelligence
• software/technology wording aligned with the current O*NET 30.3 software-skills model while preserving public API field names
• output interpretation guidance for intentionally damped confidence on weak, vague, noisy, or non-occupational inputs
• refreshed API and homepage copy for the validated beta decision-layer behavior

v0.1.2 - Taxonomy and Market Quality Foundation

Added taxonomy and market-quality foundations used to improve workforce-signal quality over time while preserving the current public API contract.

Included:

• ESCO reference import foundation for future taxonomy-aware workforce intelligence work
• optional ESCO matcher integration for market term quality evidence
• failure-safe behavior so ESCO evidence does not override existing exclusions or force ambiguous terms into market outputs
• market term normalization improvements for noisy, ambiguous, or education-requirement-like terms
• continued grounding in O*NET 30.3 for the public occupation-intelligence experience

Public API contracts and response schemas were not changed by this foundation work.

v0.1.3 - SEO and Social Metadata Hardening

Improved public frontend metadata and SEO validation for the beta site.

Included:

• completed SEO metadata cleanup for public frontend pages
• added missing social image metadata for the privacy page
• added 404 metadata improvements
• validated SEO audit with zero warnings

v0.1.4 - Workforce Intelligence Positioning Refresh

Updated the homepage positioning to make AvelinLabs easier to understand in the first few seconds of a visit.

Included:

• new homepage positioning around workforce intelligence infrastructure
• clearer hero messaging for AI-native hiring systems
• explicit boundary language: not a job board, not a raw job-data feed, and not another AI wrapper
• updated homepage metadata to match the current GTM direction

v0.1.5 - Docs and Examples GTM Alignment

Updated public documentation and examples to align with the current AvelinLabs positioning as a workforce intelligence layer for AI-native hiring systems.

Included:

• clearer distinction from job boards, raw data feeds, ATS platforms, recruiting agencies, and generic AI wrappers
• improved explanation of occupation alignment, skill evidence, confidence and uncertainty, weak signals, ambiguity, review routing, and market context where available
• improved example readability by replacing pipe-delimited Markdown tables with stacked field sections
• refreshed examples and annotated response guidance for developer evaluation

v0.1.6 - Response Field Reference

Added a response-first API field reference to help developers understand how to use AvelinLabs outputs in their own products and workflows.

Included:

• new Response Field Reference page under the API documentation
• field-level guidance for confidence, trust, uncertainty, decision routing, ambiguity, job signals, ranked occupation results, O*NET codes, skill evidence, quality signals, domain grounding, and explanations
• guidance on what each field means, how to use it, and what not to use it for
• cross-links from API overview, Job Analyze, Output Interpretation, Examples, and the annotated response guide

v0.1.7 - Response-Derived Metrics

Added guidance for measuring the value of AvelinLabs responses in customer products and evaluation workflows.

Included:

• response-derived metrics for classification coverage, O*NET mapping coverage, review routing, confidence, uncertainty, weak signals, ambiguity, skill evidence, and explanation coverage
• guidance that thresholds are customer-defined and should be tuned to each workflow
• clarification that auto-accept-style outputs are routing and low-risk workflow automation signals, not final hiring decisions
• cross-links from API overview, Output Interpretation, Examples, and Response Field Reference

v0.1.8 - Evaluator Guide

Added a practical evaluator guide for teams assessing whether AvelinLabs fits their workflow.

Included:

• new Evaluate AvelinLabs in your workflow page under the API documentation
• 30-60 minute evaluation flow using Job Analyze, ranked occupation results, O*NET codes, skills, explanations, confidence, uncertainty, ambiguity, weak-signal handling, and review routing
• guidance for testing strong and weak inputs
• evaluator checklist for reviewing evidence, thresholds, automation boundaries, and human-review needs
• cross-links from API overview, Response Field Reference, Output Interpretation, Examples, and Getting Started

v0.1.9 - Early Access Page Refinement

Strengthened the public Early Access page for qualified beta evaluators.

Included:

• clearer guidance on who should request early access
• explanation of what early access includes during controlled beta
• practical request-email checklist for company context, use case, evaluation data, volume, target workflow, endpoint needs, and constraints
• lightweight post-request beta process description
• link to the evaluator guide before requesting runtime access
• beta boundaries for manual API-key issuance, decision-support usage, customer-defined thresholds, and evaluation fit

v0.1.10 - Input Quality Examples

Added practical examples showing how input quality affects AvelinLabs outputs and evaluation patterns.

Included:

• new Input Quality Examples page under Examples
• interpretation patterns for strong job descriptions, title-only inputs, vague or generic job text, ambiguous role titles, and noisy or non-occupational inputs
• guidance that lower confidence, higher uncertainty, ambiguity flags, weak-signal warnings, or review routing can be useful outputs rather than failures
• evaluator checklist for comparing strong and weak inputs, reviewing confidence behavior, inspecting ambiguity and weak-signal fields, and identifying upstream input-quality improvements
• cross-links from Examples, Evaluator Guide, Response Field Reference, and Output Interpretation

The public beta documentation focuses on the current labor market intelligence domain and will evolve as the API surface, examples, and onboarding experience mature.