Mapademics Embedded API
Embed syllabus skills extraction and labor market intelligence into your application using the Mapademics Embedded API.
The Mapademics Embedded API lets you embed advanced skills and labor-market intelligence directly into your application - without building your own extraction pipelines, taxonomies, or data infrastructure.
Who this API is for
This API is designed for platforms that want to:
Extract structured skills from academic content (like syllabi or curricula)
Enrich experiences with real-time labor market insights
Power features like skills mapping, career alignment, program analytics, and workforce reporting
If your users ask questions like:
"What skills does this course teach?" "How does this program align with the job market?"
This API is built for you.
What you can build with the Embedded API
Syllabus Skills Extraction
Automatically extract standardized, structured skills from syllabi.
Typical use cases:
Power course and program-level skills dashboards
Support accreditation, assessment, or outcomes reporting
Drive curriculum development and improvement
Show students which skills a course teaches
Map courses to occupations or career paths
What the API does:
Ingests PDF documents
Identifies relevant skills using Mapademics' skills intelligence
Returns normalized, machine-readable skill data you can embed directly in your UI
You focus on the experience. We handle extraction, infrastructure, and algorithms.
Labor Market Intelligence
Bring labor market data directly into your workflows - tied to skills, occupations, regions, and education pathways.
Typical use cases:
Show demand, wages, and growth for skills or occupations
Align academic programs with workforce needs
Enrich advising, planning, or analytics tools
Power reports for institutions, employers, or policymakers
What the API does:
Provides structured labor market signals
Links skills to occupations and outcomes
Supports regional and national views
No scraping job boards. No stitching datasets together. Just clean, usable data.
How customers use the Embedded API
Most customers use the Embedded API in one of two distinct ways, depending on the product they're building.
Skills extraction for curriculum and academic workflows
Skills extraction is typically used in curriculum development and academic analysis.
Customers use it to:
Analyze syllabi, courses, or programs
Understand what skills are being taught
Support curriculum design, review, and alignment
In this mode, the focus is on academic content → structured skills. Labor market data is often not required.
Labor market intelligence for catalogs and planning tools
Labor market intelligence is commonly used in catalogs, planning tools, and discovery experiences.
Customers use it to:
Browse occupations, skills, and outcomes
Explore workforce demand and trends
Inform advising, planning, or reporting workflows
In this mode, the focus is on workforce data → insights and exploration. Skills extraction is typically out of scope.
Where they connect
Some customers choose to connect both capabilities to:
Contextualize curriculum with workforce demand
Support program review or strategic planning
Link what's taught to what's needed
This is optional — the APIs are designed to work independently or together, depending on your product.
You can adopt one capability without committing to the other.
Getting started
If you want to see value quickly, we recommend:
Authentication - Generate an API key and configure access
Make your first request - Try extracting skills from a sample syllabus or querying labor market data
Embed the results - Use the structured responses to power features in your product
Make your first request in minutes
Generate API keys and configure access
The Mapademics Skills Library
The Mapademics Skills Library underpins everything the API does. It's the world's first skills taxonomy designed exclusively for education-to-career alignment, providing a consistent, normalized vocabulary for skills across education and workforce contexts.
You don't need to manage it or expose it directly unless you want to. It's there to ensure:
Skills are consistent across documents and datasets
Results are comparable across institutions and regions
Your product stays future-proof as skills evolve
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