Just tried the much-hyped "search across your PDFs" feature. The promise is solid: finally a decent way to query a pile of vendor contracts and old migration plans without opening fifty tabs. The reality, as usual, is a bit messier.
The OCR they're using seems completely inconsistent. Scanned a clean, typed 2023 SaaS agreement from a major vendor. It found every instance of "termination for convenience" perfectly. Then I uploaded a slightly older, but still perfectly legible, printed statement of work with some handwritten initials in the margins. It missed entire clauses, including a critical data processing addendum. That's not a minor miss; that's the kind of thing that gets you into compliance trouble.
This is classic survivorship bias. The people raving about this feature probably only fed it modern, digitally-born PDFs. For anyone dealing with the actual sprawl of enterprise documents—a mix of scans, old prints, and new contracts—the "hit or miss" description is generous. It's a useful trick for a narrow set of documents, but don't bet your procurement review on it just yet. You'll still be Ctrl+F'ing in Acrobat for anything important.
—jake
Your mileage will vary
Oh, the classic enterprise document graveyard. You're spot on about the survivorship bias, but I'd push back slightly on blaming just the OCR.
The real problem is expecting a single, simple "search across PDFs" feature to handle the absolute chaos of a real document corpus. You've got digitally-born PDFs with actual text layers, flat image scans from a 90s copier, scans of prints with coffee stains, and modern scans with wonky compression artifacts. Each one is a completely different problem.
A clean 2023 contract is basically just parsing text metadata. The older doc with handwritten initials? That's not just an OCR failure, it's a failure to even recognize that the handwritten bits *are* noise and should be isolated from the typed text. Most of these services are just wrapping a generic OCR engine and calling it a feature. They're not building pipelines with pre-processors to classify document *types* before they even try to read them.
So yeah, it's a trick. A parlor trick for tidy folders. For the actual sprawl, you'd need something closer to a dedicated document intelligence pipeline, not a checkbox in a search engine. But who's going to build that when they can just say "AI-powered search"?
🤷
You're absolutely right about the heterogeneous document corpus being the core challenge. Generic OCR can't possibly handle that spread.
The dedicated pipeline approach you mentioned is precisely where many observability platforms, including Datadog, have an edge for internal use cases. We ingest logs, traces, and yes, documents, but they run through a classification and enrichment phase before indexing. A PDF upload isn't just OCR'd; it's profiled. Is it machine text? A scan? A scan of a fax? The pipeline branches.
The issue with consumer tools is they skip that entire profiling stage to keep latency low. They throw a monolithic OCR model at everything and wonder why handwritten margin notes corrupt the text layer. For a real document graveyard, you need that pre-processing tier, which most search products consider too expensive.
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Completely feel this. I've been trying to use similar tools for our asset inventory, just to find old license PDFs buried on the network share.
The compliance point is so real. When we did our software audit last year, we found a critical license amendment scanned from a fax. I guarantee a simple search would have missed it. Makes you wonder if the "miss" rate is high enough that you could get a false sense of security.
So what do you do then? Just stick with manually reviewing everything important?
That's a really good point about needing to classify the document type first. So for those of us managing a bunch of SaaS vendor docs, would the real solution be to run files through something separate before they even hit a tool like You.com? A pre-filter for scans vs. digital text?
I'm new to this, but it sounds like without that step, the search results are basically untrustworthy for anything important.
Yeah, that pre-filter idea is basically the "pipeline" everyone's hinting at. But even classifying scans vs. digital text feels like a first step. I'm trying to learn this stuff myself, and from what I gather, you'd also need a different process for *bad* scans vs. good ones, right?
So then it's not just one tool, it's a whole flow: classify, then route to the right OCR or text extractor, maybe even a human review branch for the really messy ones. Seems like a lot of setup just to search your own files 😅
How do you even start building that without it becoming a full-time job? Is there a simple service that does the classification part?