I worked recently on an internal tool to achieve this kind of things, mostly plugging mistral OCR to gemini to extract structured data from documents. We then perform automated diffs too.
There seems to be an insane amount of competition in the "Intelligent Document Processing" market, like for instance parseur, whose founder is often on HN himself.
What do you think sets you apart from competition like :
1) Mistral document AI : depending on the model, it looks way cheaper than yours, OCR model pricing ranges from 0.001 to 0.004 EUR / page and they have structured output wired in the OCR API if needed (things then get fed to one of their LLMs) + EU-based and GDPR ready
2) parseur / rossum / docsumo / nanonets (which is YC 2017) ?
1. We are working with the assumption that OCR is (or soon will be) solved at super low prices.
So if we have the extracted data, what can we do with it?
Where we see Parsewise making a difference is for use cases that span across documents.
I.e. if you are extracting the same 5 fields from every invoice, there are lots of solutions as you listed (+ reducto etc). However, once you have a set of documents (e.g. an entire mortgage application package) and you are trying to get a structured response out, then your option is either an LLM API (if things fit into context and you are okay with limited citations), or building a pipeline with LLMs. I posted it in another comment but an example of trawling through 90k pages is here: https://www.parsewise.ai/officeqa-sota
2. While we rely on LLMs, the outcomes will be non-deterministic, so the bottleneck is and will remain the human verification (that is for somewhat complex use cases). The architecture that we have built is optimizing for the human reviewer to provide as granular values and citations as possible. This is either through our platform, or API clients.
you show this in the first paragraph, before many other details
> We would love to welcome builders and tinkerers
Love? really .. cognitive dissonance here.. I read this as " we are security state friendly so we can get that big security state funding" plus "people who work for free like love, so we say that word"
coupled with the free-riding of VC capital on decades of open work, I just can not, not say this
I learnt a lot at Palantir, though always worked in commercial so no ties to security state (for the better or worse).
(Also side-note, we are working towards enabling frontier performance with smaller open models that allows our customers to protect their data. https://www.parsewise.ai/officeqa-sota )
And I do get genuine joy from helping our users, so love it is:)
Have you really ever thought deeply about your reasoning that it’s all fine and dandy and just love when your paycheck every two weeks of what you spent 50 hours a week doing was coming from, pretty convenient to look the other way when it’s a categorical fact your old employer knowingly assists in genocide, mass deportations, democratic backsliding.. your intentions may be genuine and good natured and if that’s the case then hopefully this new chapter of your company will serve good things, and hopefully not more triangulation of data points vis a vis document parsing to help continue what palantir and the likes are doing
Planning to serve good things for sure, and appreciate your note.
Ofc I didn't agree with everything Palantir was doing (also to the extent that we even knew about them at the time). I was working on vaccine distribution and cancer research as well, so definitely felt like helping.
I worked recently on an internal tool to achieve this kind of things, mostly plugging mistral OCR to gemini to extract structured data from documents. We then perform automated diffs too.
There seems to be an insane amount of competition in the "Intelligent Document Processing" market, like for instance parseur, whose founder is often on HN himself.
What do you think sets you apart from competition like : 1) Mistral document AI : depending on the model, it looks way cheaper than yours, OCR model pricing ranges from 0.001 to 0.004 EUR / page and they have structured output wired in the OCR API if needed (things then get fed to one of their LLMs) + EU-based and GDPR ready 2) parseur / rossum / docsumo / nanonets (which is YC 2017) ?
Great question!
1. We are working with the assumption that OCR is (or soon will be) solved at super low prices.
So if we have the extracted data, what can we do with it? Where we see Parsewise making a difference is for use cases that span across documents. I.e. if you are extracting the same 5 fields from every invoice, there are lots of solutions as you listed (+ reducto etc). However, once you have a set of documents (e.g. an entire mortgage application package) and you are trying to get a structured response out, then your option is either an LLM API (if things fit into context and you are okay with limited citations), or building a pipeline with LLMs. I posted it in another comment but an example of trawling through 90k pages is here: https://www.parsewise.ai/officeqa-sota
2. While we rely on LLMs, the outcomes will be non-deterministic, so the bottleneck is and will remain the human verification (that is for somewhat complex use cases). The architecture that we have built is optimizing for the human reviewer to provide as granular values and citations as possible. This is either through our platform, or API clients.
Ah probably should add a link to our website: https://www.parsewise.ai/api
"retaining lineage"
"That is a great catch!"
> implemented AI workflows at Palantir
you show this in the first paragraph, before many other details
> We would love to welcome builders and tinkerers
Love? really .. cognitive dissonance here.. I read this as " we are security state friendly so we can get that big security state funding" plus "people who work for free like love, so we say that word"
coupled with the free-riding of VC capital on decades of open work, I just can not, not say this
I learnt a lot at Palantir, though always worked in commercial so no ties to security state (for the better or worse). (Also side-note, we are working towards enabling frontier performance with smaller open models that allows our customers to protect their data. https://www.parsewise.ai/officeqa-sota )
And I do get genuine joy from helping our users, so love it is:)
Have you really ever thought deeply about your reasoning that it’s all fine and dandy and just love when your paycheck every two weeks of what you spent 50 hours a week doing was coming from, pretty convenient to look the other way when it’s a categorical fact your old employer knowingly assists in genocide, mass deportations, democratic backsliding.. your intentions may be genuine and good natured and if that’s the case then hopefully this new chapter of your company will serve good things, and hopefully not more triangulation of data points vis a vis document parsing to help continue what palantir and the likes are doing
Planning to serve good things for sure, and appreciate your note. Ofc I didn't agree with everything Palantir was doing (also to the extent that we even knew about them at the time). I was working on vaccine distribution and cancer research as well, so definitely felt like helping.