Cue Keep: document conversion that keeps strikethrough, highlight and redline meaning for agents
Open-source Word/PDF-to-Markdown converter that keeps formatting meaning as explicit tags, with a paid hosted API. RAG and agent builders pay.
Competitive · Paper shows every converter drops formatting
Written for: Solo builder
Next step
This week, write a python-docx and pdfplumber prototype that emits deleted and highlight tags. Run it on public policy documents with track changes, publish a before/after comparison against MarkItDown and Docling, and post it on Hacker News and in r/LocalLLaMA and r/RAG.
Why now
Agents now act on policies and contracts, not just summarise them, so a deleted clause that reads as live is a real error. Reducto's a16z appearance and Legora's $550M round at $8.8B show buyers pay for the document layer. The paper also gives you a ready failure set to benchmark against. The big parsers sell OCR accuracy, not this. Nobody markets 'we keep the strikethrough', so the gap is open.
What became possible
A new paper tested ingestion on 76 real policy documents. Every mainstream converter loses the meaning an author puts in how text looks: struck-through clauses, highlighted exceptions, coloured or bold warnings. The agents reading the output end up confidently wrong instead of visibly unsure. The failure sits before the model, so a better ingestion layer fixes it without touching the model.
Two ways in
- cue-keep CLI and library
Reads .docx track changes, strikethrough, highlight and colour runs, and PDF annotation layers. Outputs Markdown with explicit tags like <deleted>, <highlight> and <warning>, plus a short note to put in the system prompt explaining the tags.
- Customer
- Developers building RAG or policy agents in Python and TypeScript
- Model
- MIT-licensed core, with a hosted API at $0.01 per page and a free tier
- Cue Loss Report
Upload a sample of your corpus and get a report on how much formatting meaning your current converter (Docling, Unstructured, MarkItDown, pandoc) throws away, with side-by-side examples.
- Customer
- Small legal-tech and HR-policy chatbot startups
- Model
- Free report as lead magnet, upsell to the API
Competition
The basic job of keeping tracked changes, strikethrough and highlights as tags is already done. Reducto offers it as a paid API with legal-redline marketing, and undocx, pandoc, Redlines and Tensorlake cover the Word side in open source or as hosted APIs.
Full competition notes (8 competitors)
The basic job of keeping tracked changes, strikethrough and highlights as tags is already done. Reducto offers it as a paid API with legal-redline marketing, and undocx, pandoc, Redlines and Tensorlake cover the Word side in open source or as hosted APIs. A new entrant cannot win on simply having the feature. What looks open is the meaning layer: mapping colour and highlight conventions to labels such as warning, separating a real tracked deletion from cosmetic strikethrough, and shipping a ready-made system-prompt note so agents read the tags correctly. Reducto's tags describe appearance, not intent. The Cue Loss Report also looks unclaimed. I found no tool or benchmark measuring how much of this meaning Docling, Unstructured, MarkItDown or pandoc lose, and that makes a good hook for getting noticed. A small, permissively licensed library in Python and TypeScript that handles both Word revisions and PDF annotation layers locally can undercut Reducto on cost and privacy for small legal-tech and HR startups. The risks are that Reducto already sells to these buyers, that Docling or MarkItDown could add similar tagging cheaply, and that the tagging is easy to copy, so the lasting advantage has to be the benchmark corpus and the meaning conventions. I did not confirm whether Docling, MarkItDown or Unstructured currently handle any of this, so check their repositories before relying on that gap.
- Reducto (change_tracking and highlight options)
Hosted parse API. It wraps strikethrough and underline in s, u and change tags and highlights in mark tags, returns PDF comments as separate blocks, and detects strikethrough visually on scanned pages. It targets redlined legal contracts directly, so it overlaps most with the paid-API side.
- undocx
Open-source Rust Word-to-Markdown converter with Python bindings, built for RAG. It marks insertions with ins tags and deletions as strikethrough, and keeps footnotes and comments. It overlaps most with the open-source CLI.
- Pandoc track-changes option
Pandoc's track-changes flag (accept, reject or all) keeps Word revisions in the output. It is widely used in agent skills, including Anthropic's docx skill.
- Redlines (Elixir)
Open-source library that extracts tracked changes from DOC, DOCX and PDF into typed insertions, deletions and replacements with author and date metadata. Its PDF handling relies on detecting strikethrough and underline.
- Adeu
MIT-licensed MCP server and SDK that shows Word files to agents as Markdown with CriticMarkup and writes edits back as native tracked changes. It has an optional paid cloud tier.
- docx-mcp
Legal redlining MCP engine that writes w:ins and w:del markup with comments from LLM-produced changes. It focuses on editing rather than ingestion.
- Tensorlake DOCX parsing
Hosted document-ingestion API whose Word parser outputs HTML with ins and del tags when the file contains revisions.
- pdf_annotation_extraction
Small open-source tool that pulls highlights, underlines, strikethroughs and comments out of PDF annotation layers.
Checked 2026-10-22
Risks
- Big parsing vendors can add the feature quickly once the paper circulates
- Scanned PDFs carry no formatting runs, so you would need vision models, which cost more and are less accurate
- Developers may take the free library and never pay for the API
Strong team fit; market pull and open space are the weak point (7).
tech 8 · pull 7 · space 7 · fit 9 · build 8
Exposed
Companies that could benefit or be threatened. News and context, not investment advice.
Momentum of its main topic
Sources
- What the Author Wrote, What the Agent Reads: Silent Cue Loss in Enterprise Document IngestionarXiv
- The Missing Infrastructure for AI Agents: Documents | Reducto’s Adit AbrahamThe a16z Show
- Legal AI startup Legora valued at $8.8 billion in new $550 million funding round - ReutersReuters
- Legal AI startup Legora raises $550M Series E at $8.8B valuationTechCrunch Venture
Status history
- 2026-10-22 Flagged as buildable