How It Works
Prepare private files for AI.
NeuroAIgent is a local-first desktop workspace that helps you import messy private files, inspect what is there, clean and structure the contents, package the results, and export AI-ready knowledge packages to the tools you already use.
Your files stay under your control while you review metadata, chunks, source references, and readiness issues before anything moves downstream.
See Solutions, explore Vertical AI, or browse Resources for practical examples.
What happens to your files?
NeuroAIgent moves your work through a practical six-step workflow: Import → Inspect → Clean → Structure → Package → Export. The goal is not generic document chat. The goal is to turn messy private files into structured outputs your AI stack can actually use.
- Import folders, files, and project assets
- Inspect file types, metadata, duplicates, failed files, and readiness issues
- Clean content through conversion, extraction, and [[NEEDS INPUT: OCR and media processing details]]
- Structure outputs with metadata, tags, sections, tables, chunks, and source references
- Package records for search, retrieval, analytics, and automation
- Export to the AI tools and storage targets you already use
Workflow
How does the workflow move from files to usable outputs?
Import
Bring in folders, files, and project assets so work starts from the source material you already have.
Typical inputs include PDFs, Word docs, spreadsheets, images, Markdown files, audio, and video.
Inspect
Review file types, metadata, duplicates, failed files, and readiness issues before you push anything into downstream AI workflows.
Clean
Convert documents, extract text, normalize content, and prepare messy source files for consistent downstream use.
Structure
Generate metadata, tags, sections, tables, chunks, and source references so outputs are easier to search, trace, and reuse.
This is where private files become AI-ready knowledge packages instead of loose text dumps.
Package
Assemble structured outputs for retrieval, search, analytics, automation, and vertical workflow use cases.
See how this supports PermitPilot, PresenceOS, and other Vertical AI workflows.
Export
Export to Markdown, JSONL, CSV, local folders, vector databases, Obsidian, Ollama, LM Studio, and many more!
The point is simple: prepare the files once, then use the outputs across the AI tools you already use.
What do you get out of the workflow?
- Extracted text
- Structured metadata
- Chunked JSONL
- Search-ready records
- Source references
- Processing logs
- File readiness score
- Exportable knowledge packages
These outputs help you move from raw files to search, RAG, analytics, automation, and vertical workflows with more structure and less manual cleanup.
Why keep the workflow local-first?
NeuroAIgent starts on your machine. Local-first file processing helps you keep private files under your control while you organize work by project, review metadata and chunks before export, and avoid messy one-off scripts.
- Process private files locally
- Organize work by project
- Review metadata and chunks before export
- Avoid messy one-off scripts
- Export to the tools you already use
Where does this workflow lead next?
Solutions
See the main use cases and spoke pages on the Solutions hub.
Vertical AI
Explore focused workflows built on the same engine: Vertical AI, PermitPilot, and PresenceOS.
Resources
Read practical guides in Resources, including private document prep, chunking PDFs into JSONL, and organizing files for AI search and automation.
Pilot
Want to see what your files look like when they are AI-ready?
Run a guided File Readiness Pilot to review a sample set of files, see what can be extracted and structured, and identify what is missing, duplicated, broken, or not ready.
Common questions
Is this a cloud upload workflow?
No. NeuroAIgent is positioned as a local-first desktop tool for preparing private files for AI without sending sensitive files to the cloud.
What kinds of files can it start with?
Known examples include PDFs, Word docs, spreadsheets, images, Markdown files, audio, and video.
What comes out at the end?
Structured outputs can include extracted text, metadata, chunked JSONL, search-ready records, source references, processing logs, readiness scoring, and exportable knowledge packages.
Where can exports go?
Known export targets include Markdown, JSONL, CSV, local folders, vector databases, Obsidian, Ollama, and LM Studio.
Can this support vertical workflows?
Yes. PresenceOS and PermitPilot are presented as focused vertical workflows powered by the same core engine.