EQi30
A 12-engine AI system powering assessment, coaching, and microlearning for an emotional-intelligence platform.
EQi30 is an emotional-intelligence platform. The AI service layer spans 12 engines covering assessment, coaching, microlearning, and adaptive scheduling, built in Python and FastAPI. A significant part of the work was data engineering: 28 separately authored microskill documents (docx and xlsx) had to be parsed and reconciled into one consistent structure the engines could run on.
Primary technologies
- Python
- FastAPI
- Content data engineering
01 / Context
What this project is
EQi30 is an emotional-intelligence platform. The AI service layer spans 12 engines covering assessment, coaching, microlearning, and adaptive scheduling, built in Python and FastAPI. A significant part of the work was data engineering: 28 separately authored microskill documents (docx and xlsx) had to be parsed and reconciled into one consistent structure the engines could run on.
Engineering problem
Translating a large body of emotional-intelligence content, authored across 28 separate documents with inconsistencies between them, into a structure a product could actually run coaching and scheduling logic on.
Approach
Built 12 AI engines spanning assessment, coaching, microlearning, and adaptive scheduling, and ran a dedicated content-mapping pass to resolve inconsistencies across the source documents.
02 / Stack
What it's built with
Technologies
- Python
- FastAPI
- Content data engineering
03 / Outcome
What shipped
Mapped the source content into 40 abilities across 6 competencies, with 30 of those abilities now carrying complete day-by-day programs for the coaching engines to run on.
Next step
Have something like this
you need built properly?
I work on the AI layer — retrieval, reasoning, and the plumbing underneath — and hand it over ready to integrate.