Here’s a demo video: https://video.runpulse.com/video/pulse-platform-walkthrough-....
Later in this post, you’ll find links to before-and-after examples on particularly tricky cases. Check those out to see what Pulse can really do! Modern vision language models are great at producing plausible text, but that makes them risky for OCR and data ingestion. Plausibility isn’t good enough when you need accuracy.
When we started working on document extraction, we assumed the same thing many teams do: foundation models are improving quickly, multi-modal systems appear to read documents well, what’s not to like? And indeed, for small or clean inputs, those assumptions mostly give good results. However, limitations show up once you begin processing real documents in volume. Long PDFs, dense tables, mixed layouts, low-fidelity scans, and financial or operational data expose errors that are subtle, hard to detect, and expensive to correct. Outputs look reasonable even though they contain small but important mistakes, especially in tables and numeric fields.
Running into those challenges got us working. We ran controlled evaluations on complex documents, fine tuned vision models, and built labeled datasets where ground truth actually matters. There have been many nights where our team stayed up hand-annotating pages, drawing bounding boxes around tables, labeling charts point by point, or debating whether a number was unreadable or simply poorly scanned. That process shaped our intuition far more than benchmarks.
One thing became clear quickly. The core challenge is not extraction itself, but confidence. Vision language models embed document images into high-dimensional representations optimized for semantic understanding, not precise transcription. That process is inherently lossy. When uncertainty appears, models tend to resolve it using learned priors instead of surfacing ambiguity. This behavior can be helpful in consumer settings. In production pipelines, it creates verification problems that do not scale well. Pulse grew out of our trying to address this gap through system design rather than prompting alone.
Instead of treating document understanding as a single generative step, our system separates layout analysis from language modeling. Documents are normalized into structured representations that preserve hierarchy and tables before schema mapping occurs. Extraction is constrained by schemas defined ahead of time, and extracted values are tied back to source locations so uncertainty can be inspected rather than guessed away. In practice, this results in a hybrid approach that combines traditional computer vision techniques, layout models, and vision language models, because no single approach handles these cases reliably on its own.
We are intentionally sharing a few documents that reflect the types of inputs that motivated this work. These are representative of cases where we saw generic OCR or VLM-based pipelines struggle.
Here is a financial 10K: https://platform.runpulse.com/dashboard/examples/example1
Here is a newspaper: https://platform.runpulse.com/dashboard/examples/example2
Here is a rent roll: https://platform.runpulse.com/dashboard/examples/example3
Pulse is not perfect, particularly on highly degraded scans or uncommon handwriting, and we’re working on improvements. However, our goal is not to eliminate errors entirely, but to make them visible, auditable, and easier to reason about.
Pulse is available via usage-based access to the API and platform You can sign up to try it at https://platform.runpulse.com/login. API docs are at https://docs.runpulse.com/introduction.
We’d love to hear how others here evaluate correctness for document extraction, which failure modes you have seen in practice, and what signals you rely on to decide whether an output can be trusted.
We will be around to answer questions and are happy to run additional documents if people want to share examples. Put links in the comments and we’ll plug them in and get back to you.
Looking forward to your comments!
I've got one! The pdf of this out-of-print book is terrible: https://archive.org/details/oneononeconversa0000simo. The text is unreadably faint, and the underlying text layer is full of errors, so copy-paste is almost useless. Can your software extract usable text?
(I'll email you a copy of the pdf for convenience since the internet archive's copy is behind their notorious lending wall)
If anyone is interested in the history of the family therapy movement—that is, the movement that started in the 1950s where psychotherapists started working with entire families rather than individual clients—this is a great book of interviews and incredibly readable.
From the chapter above, Jay Haley on Milton Erickson:
But, you know, the real tragedy with Erickson was he spent so much time over the years teaching hypnosis when he had a whole new school of thera- py to offer. People did not recognize the significance of his work until he was too old to really demon- Strate it
(I left in a couple of text glitches there...at least it's readable now!)
There is a section near the start where there are 4 options: Large accelerated filer, Non-accelerated filer, Accelerated filer, or Smaller reporting company.
In this option, "Large accelerated filer" is checked on the PDF, but "Non-accelerated filer" is checked on the Markdown.
I guess I should thank you for saving my time? Plenty of others in this space.
I am already seeing this trend in the recent releases of the native models (such as Opus 4.5, Gemini 3, and especially Gemini 3 flash).
It's only going to get better from here.
Another thing to note is, there are over 5 startups right now in YC portfolio doing the same thing and going after a similar/overlapping target market if I remember correctly.
That plus the ability to provide customized solutions that stitch together data extraction and business logics such as reconciliations for vendor payments or sales.
I think both these reasons are what's keeping all the OCR based companies going.
My only advice would be to figure out more USPs before native models eat your lunch. Like Nanonets has its own native OCR model.
Congrats on the launch.
I'm glad you found a solution that worked for you, but this is pretty surprising to hear - our new model, chandra, saturates handwriting-heavy benchmarks like this one - https://www.datalab.to/blog/saturating-the-olmocr-benchmark ,and our production models are more performant than OSS.
Did you test some time ago? We've made a bunch of updates in the last couple of months. Happy to issue some credits if you ever want to try again - vik@datalab.to.
[1] https://www.runpulse.com/blog/why-llms-suck-at-ocr
I paste screenshots into claude code everyday and it's incredible. As in, I can't believe how good it is. I send a screenshot of console logs, a UI and some HTML elements and it just "gets it".
So saying they "Suck" makes me not take your opinion seriously.