Turn messy documents into clean AI-ready context.
Upload PDFs, DOCX files, Markdown notes, HTML exports, or ZIPs. FileDigest extracts, cleans, and packages them into an inspectable digest you can use with ChatGPT, Claude, RAG, or analyst workflows.
Example job queue
Research review packet
research-brief.pdf
46 pages
methods-appendix.docx
12 pages
review-notes.md
8.4k chars
3
Files
66
Pages
42k
Tokens
# Document Digest ## Sources - research-brief.pdf - methods-appendix.docx - review-notes.md ## Core Ideas The packet summarizes a document-heavy research workflow and preserves source boundaries for review. ## Quotable Notes - Each claim should remain traceable to a source file. - Human review remains part of the workflow.
{
"status": "completed",
"engine": "private-processing",
"artifacts": [
"digest_md",
"manifest_json"
],
"private_downloads": true
}Processing path
How it works
The path is simple: upload a packet, let FileDigest prepare it, review the result, then export clean context for your AI workflow.
Upload
Drop in PDFs, DOCX files, notes, HTML exports, or ZIPs.
Process
FileDigest extracts text and prepares clean context server-side.
Review
Inspect the prepared digest before using it with AI.
Download
Export clean Markdown and structured metadata through private links.
Document workflows FileDigest serves first
Each workflow starts with messy source material and ends with clean, inspectable AI context.
Research analysts
Turn papers, chapters, and literature packets into synthesis-ready context before AI review.
Consulting analysts
Turn client reports, decks, exports, and notes into reviewable context for synthesis and memo work.
AI builders
Turn messy document folders into structured context for RAG, agents, and evaluation workflows.
Examples
Inspect sample prepared context and source metadata before trying it yourself.
From source documents to reviewable AI context
FileDigest turns PDFs, DOCX files, text files, HTML exports, and ZIP bundles into a clean context pack you can inspect before using it with AI.
- Clean Markdown digest
- Each completed job produces readable Markdown designed for context windows and downstream AI workflows.
- Traceable metadata
- Structured metadata records source files, processing status, pages, artifacts, and token estimates for repeatable review.
- Ready for AI work
- Use the prepared output with ChatGPT, Claude, RAG ingestion, research synthesis, or analyst review.
FileDigest job
Source packet digest
board_packet.pdf
48 pages
chapter_notes.docx
12 sections
interview_export.html
source mapped
digest.md
14.2k tokens
## Acquisition notes
Built for private document workflows
The product handles authentication, private uploads, plan limits, billing, job history, and signed downloads before processing starts.
- Direct private uploads
- Files upload to private paths owned by the signed-in user instead of being exposed through public links.
- Limits before processing
- File count, job size, OCR access, and token quotas are checked before heavy processing starts.
- Private downloads
- Prepared outputs are served through short-lived signed URLs and ownership checks instead of public bucket links.
Upload
Private source files
Process
Server-side extraction
Review
Inspect prepared context
Download
Signed output links
What FileDigest prepares
The core product is intentionally narrow: upload source files, review a clean prepared context pack, then reuse it wherever AI work happens.
Queue PDFs, DOCX files, Markdown, text, HTML, and ZIP bundles from the FileDigest workbench.
Inspect the prepared Markdown before copying, downloading, or reusing it in an AI workflow.
Use source-aware metadata to understand what was parsed, skipped, counted, and packaged.
Keep uploads, processing, and downloads behind authenticated server-side checks.
Use paid limits for larger packets, OCR, longer retention, and higher monthly output quotas.
Bring the prepared context to ChatGPT, Claude, RAG pipelines, research review, or consulting analysis.
Where FileDigest fits first
FileDigest is for document-heavy work where the useful step is preparing clean context before the AI tool, not chatting inside another file viewer.
Research analysts
Papers, chapters, and literature packets
Turn papers, chapters, and appendices into synthesis-ready context before using ChatGPT, Claude, or a research workflow.
Consulting analysts
Client packets, decks, reports, and notes
Turn client reports, decks, exports, and notes into reviewable AI context before synthesis, memo drafting, or proposal work.
AI builders
RAG prep, agent context, and prompt packets
Turn messy document folders into structured context for retrieval, evaluation, and agent tools without hand-cleaning every source.