Knowledge ingestion
Live (Private)Knowledge Ingestion Engine
Private local-first ingestion engine — noise reduction, filtering, no recurring cloud costs.
Overview
Private local-first ingestion layer — gathers, filters, and organises information according to personal interests.
Problem
When transformation and research work spans many sources, finding what matters often takes more effort than acting on it — especially across documents, inboxes and local files.
Why I built it
I wanted a private, local-first ingestion engine that continuously gathers, filters and organises information according to my own priorities without recurring cloud costs.
Who benefits
Developers and technical users who need to process and search documents locally — in my case: notes, technical PDFs, and office files from transformation and programme work.
Current status
Live (Private) — private local-first ingestion with filtering and no recurring cloud costs.
Architecture
Current implementation — based on what is built today.
Technology stack
Python · FastAPI · Docker · Redis · Elasticsearch
Lessons learned
- Modularity helps extension but increases operational overhead
- Error handling is the biggest time sink in pipelines
- Small projects surface enterprise problems in miniature
Future direction
- Transfer findings into Ada ingestion
- Evaluate OCR layer for scanned documents
- No active continued operation planned