LLM Whisperer Reviews & Product Details

What I like most is that it handles “real-world PDFs” better than many tools I’ve tried — the ones with weird spacing, headers/footers, and inconsistent formatting. I’m using it for extracting structured content from reports where tables and layout matter, and it’s been noticeably more reliable than plain text extraction. The API-first approach also fits nicely into my pipeline, so I don’t have to hack around the output. Review collected by and hosted on G2.com.

The biggest downside for me is that you still need some iteration to get the best output for certain documents — especially when the PDF quality is poor or the structure changes across pages. I also wish there were more built-in “debug visibility” sometimes (like clearer indicators of why a certain table/section was interpreted a certain way). It’s not a dealbreaker, but it would make tuning faster. Review collected by and hosted on G2.com.

What I dislike is minimal — LLM Whisperer is already the cherry on top of my workflow. The only thing I miss is the ability to recognize and interpret color coding inside tables, especially when extracting complex PDF tables. It’s not a dealbreaker at all, but having that feature would make the tool even more powerful for data-heavy use cases. Review collected by and hosted on G2.com.

What I dislike is minimal — LLM Whisperer is already the cherry on top of my workflow. The only thing I miss is the ability to recognize and interpret color coding inside tables, especially when extracting complex PDF tables. It’s not a dealbreaker at all, but having that feature would make the tool even more powerful for data-heavy use cases. Review collected by and hosted on G2.com.

I am using the OCR on archaeological and numismatic data, and so far, it has been the most reliable tool to extract data from those books. I also test it on other old book formats to extract data, and the preservation of Layout has been a real lifesaver in working with those old scans, since here the missing column and row lines are represented simply by tab stops or multiple whitespaces. Therefore, the layout preservation is an important tool in digitising those older books. Review collected by and hosted on G2.com.

So far, I have not encountered problems with the software itself, but the online playground takes a bit to calculate the pages (if they have a higher resolution). I would assume with an API it would take less time, but for me, the playground is more convenient. Also, sometimes it fails to recognise that act as placeholders and autofills in columns, but that is probably due to the old font type (from the 80s). Review collected by and hosted on G2.com.

LLM Whisperer extracts data from our PDF documents quickly and accurately. Proper handling of checkboxes is of upmost importance to us - LLM Whisperer nails it every time! I also appreciate the generous daily extraction limit of 100 documents available in their free version. We started using it daily in our healthcare business. I am looking forward to integrating LLM Whisperer into our automated workflow using the provided API. Review collected by and hosted on G2.com.

Free version does not seem to provide an easy way to copy the extracted clean text for further processing, hopefully the API integration will make it easier for us in the future. Review collected by and hosted on G2.com.

For me personally, the easy integration into my existing systems is one of the most important points and is therefore very user-friendly (from a developer's perspective). The speed of processing is unbeatable while maintaining unbeatable quality. I can control in the request which information and in what format I want to receive it back. The costs are also unbeatable in this segment. I needed the support once and it was prompt and very helpful. The use is daily. Review collected by and hosted on G2.com.

There is hardly anything I don't like, but I would wish for servers in Europe and data processing agreements to be concluded according to EU law. Review collected by and hosted on G2.com.

This tool performs exceptionally well and is tailored specifically for LLM inputs. Before settling on this one, I experimented with several other OCR solutions, but this proved to be the most effective. The space-aware layout feature is particularly useful. For the most part, it simply works—the document outputs generally match expectations. Errors such as mismatched lines or incorrectly parsed text are rare. Also, the fact that it returns bounding boxes for each line makes it very easy to show citations for ai-generated results, which is helpful for verification. Review collected by and hosted on G2.com.

1-2 times a week, the service is down when making a request. This is a problem, especially since I'm trying to develop products for skeptical users. Also, my application requires parsing tables that sometimes have text (handwritten or typed) that spans multiple columns of the table, and the response sometimes breaks that text up in weird ways. I imagine it is difficult to confidently parse this kind of writing into a clean text document, but better performance here would be nice. Review collected by and hosted on G2.com.

What I really like it how it preserves the original document layout and the precision I've been getting when trying out. I've been testing it out for personal use with receipts as images and pdfs with payments details to extract data as table with the use of an llm and it has worked with perfect precision. I still have to try the API, but I also like it comes with a postman documentation for testing. The API also seems easy to use, at least from the examples I have read. I also realized it has a n8n and MCP integration which will be very usefull for my uses. Review collected by and hosted on G2.com.

I haven't got the problem, but I don't know if I like new page markers and also I think for some case it could be useful to have markers for images that aren't text to signal that something was there, like bar codes or stamps that could be useful when automating the resding of a document by an AI that needs to know the document include this features.. Review collected by and hosted on G2.com.

I highly recommend LLM Whisperer. While some of Unstract’s other offerings don’t perfectly align with our use case, their layout preservation tool has been an absolute game-changer. It’s remarkably accurate—so much so that I no longer feel the need to double-check the extracted data. I have complete confidence in LLM Whisperer, and it works seamlessly across multiple formats, including scanned and digital PDFs as well as spreadsheets.

Before discovering LLM Whisperer, I searched extensively for a reliable solution to preserve layout when extracting data from PDFs. Most tools I found were either overpriced or failed to meet the mark. LLM Whisperer was the only one that truly fit my needs.

There’s still room for improvement—for example, it can’t yet extract signatures—but its ability to preserve checkboxes (in form mode), text positioning, and overall layout accuracy is outstanding. Kudos to the Unstract team, and thank you for creating such a reliable tool! Review collected by and hosted on G2.com.

Honestly, not much. This particular offering works like a charm. I’d say I’d like to be able to extract signatures and illustrations one day. But that isn’t much about dislike as it is about product limitations, complexity of extracting that sort of data. Honestly, even compliance - can you imagine the issue with extracting signatures at scale!! Review collected by and hosted on G2.com.

The ease of use, even in the free option, where I can implement the API in the AI I am developing, and the allowed amount lets me continue testing and the ease of using the platform. Review collected by and hosted on G2.com.

I think the OCR can still be improved further, perhaps more advancements for more complex images, improving the high quality, as there are still issues with accents, for example. Review collected by and hosted on G2.com.

LLM Whisperer helped me solve a complex issue involving the extraction of print tables where there are only minor differences between lines. This addressed a significant challenge for us, and we have now integrated it into our extraction pipeline. The solution is straightforward to use, and we are currently utilizing it in a public project: https://github.com/claytonsilva/condomob-documents-ocr2data/blob/main/src/processors/llmwhisperer_analytical.py Review collected by and hosted on G2.com.

LLM whisperer still needs to address some errors when reading tables, but I believe this is just a matter of further refinement. Review collected by and hosted on G2.com.