Speech, NLP & Automation

AI-Powered Learning Automation Platform

Context: Independent / Freelance Project· Role: AI Engineer / Architect· Status: Deployed AI Platform

A FastAPI platform for automated transcription, PDF extraction and content summarization, built for reviewing large volumes of multimedia learning content.

Business Problem

Reviewing audio, video and PDF learning content required extensive manual transcription and summarization before it could be checked or reused.

Users

Teams reviewing high volumes of multimedia learning content who need transcripts and summaries without manual listening or reading.

Constraints

High file volume Mixed media types Async processing at scale

Solution

I built a platform combining OpenAI Whisper transcription, PDF-content extraction and NLP summarization behind asynchronous FastAPI endpoints, so large batches of files process without blocking on any single item.

Architecture

Learning Automation architecture: audio/video input via Whisper transcription and PDF input via extraction, merging into NLP summarization, delivered through an async API
Audio / Video / PDF Input
→
Whisper Transcription + PDF Extraction
→
NLP Summarization
→
Async API Delivery

Your Contribution

Designed
the platform architecture end to end.
Integrated
OpenAI Whisper and built speech-to-text workflows.
Developed
PDF-extraction pipelines and NLP summarization.
Implemented
asynchronous processing across FastAPI endpoints.

Technology Stack

Python FastAPI OpenAI Whisper NLP Async APIs

Results

5,000+
files processed
~95%
transcription accuracy
−50%
manual review time

Challenges & Trade-Offs

Throughput vs. per-file turnaround

Processing files asynchronously in batches keeps throughput high across thousands of files, trading immediate turnaround on any single file for sustainable processing cost at volume.