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For platforms

Your customers are asking whether you handle this.

If your product ingests documents on someone else's behalf — an ATS, a claims system, a procurement tool, an AI assistant with an upload button — the hidden-text question now lands in your inbox, not ours. Embedding a scanner is a week of your roadmap or an afternoon of ours.

What embedding gives you
  • A verdict, findings and sanitised text as JSON, per document
  • Runs inside your infrastructure — documents never reach us
  • No model calls, no per-token cost, no external dependency at request time
  • About a second per document, on one CPU
  • PDF, DOCX and XLSX
  • Your branding on the report, or no report at all — just the JSON
Pricing
From $1,500/mo

Flat or per-scan, whichever suits your volume. Includes the self-hosted build, updates as new concealment techniques appear, and a direct line when something looks wrong.

Start a conversation
The integration
inside your service
>>> from docxray.api import scan, safe_text, clean

>>> result = scan("uploads/candidate_4471.pdf")
>>> result["verdict"]
'CRITICAL'
>>> result["findings"][0].kind
'hidden-text'

>>> safe_text(result)          # feed this to your model instead
'Jordan Avery Ellis  Portland, OR ...'

Three calls: is it clean, what was found, and give me the text that is safe to pass on. There is an HTTP endpoint too if a library is the wrong shape for you.

Why not build it yourself

You can, and the first eighty percent is genuinely easy — look for white text and tiny fonts. The remainder is where the engineering is:

That last one is the expensive part. A detector that cries wolf gets switched off in a fortnight.

Tell us what you are building.

We will tell you honestly whether embedding makes sense or whether you should just write it yourself.

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