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I’m curious to hear more about your pivot from AI workflow builder to document parsing. I can see correlations there, but that original idea seems like a much larger opportunity than parsing PDFs to tables in what is an already very crowded space. What verticals did you find have this problem specifically that gave you enough conviction to pivot?
Earlier quoted context omitted.
"accurate document extraction is becoming a commodity with powerful VLMs" Agree. The capability is fairly trivial for orgs with decent technical talent. The tech / processes all look similar: User uploads file --> Azure prebuilt-layout returns .MD --> prompt + .MD + schema set to LLM --> JSON returned. Do whatever you want with it.
Totally agree that this is becoming the standard "reference architecture" for this kind of pipeline. The only thing that complicates this a lot today is complex inputs. For simple 1-2 page PDFs what you describes works quite well out of the box but for 100+ page doc it starts to fall over in ways I described in another comment.
I suspect this problem gets solved in the next iteration or two of commodity models. In the meantime, being smart about how the context gets divvied works ok.
I do like the UI you appear to have for citing information. Drawing the polygons around the data, and then where they appear in the PDF. Nice.
Congrats on the launch! A quick search in the YC startup directory brought up 5-10 companies doing pretty much the same thing: - https://www.ycombinator.com/companies/tableflow - https://www.ycombinator.com/companies/reducto - https://www.ycombinator.com/companies/mindee - https://www.ycombinator.com/companies/omniai - https://www.ycombinator.com/companies/trellis At the same time, accurate document extraction is bec…
Congrats on the launch! I’m curious to hear more about your pivot from AI workflow builder to document parsing. I can see correlations there, but that original idea seems like a much larger opportunity than parsing PDFs to tables in what is an already very crowded space. What verticals did you find have this problem specifically that gave you enough conviction to pivot?
Congrats on the launch! A quick search in the YC startup directory brought up 5-10 companies doing pretty much the same thing: - https://www.ycombinator.com/companies/tableflow - https://www.ycombinator.com/companies/reducto - https://www.ycombinator.com/companies/mindee - https://www.ycombinator.com/companies/omniai - https://www.ycombinator.com/companies/trellis At the same time, accurate document extraction is bec…
"accurate document extraction is becoming a commodity with powerful VLMs" Agree. The capability is fairly trivial for orgs with decent technical talent. The tech / processes all look similar: User uploads file --> Azure prebuilt-layout returns .MD --> prompt + .MD + schema set to LLM --> JSON returned. Do whatever you want with it.
Honest question but how do you see your business being affected as foundational models improve? While I have massive complaints about them, Gemini + structured outputs is working remarkably well for this internally and it's only getting better. It's also an order of magnitude cheaper than anything I've seen commercially.
I want to create something that can paginate through 12 pages of html, perform clicks, download pdf fund factsheet, extract data from this factsheet into excel or CSV. Can this help? What's the best way to deal with the initial task of automating webpage interactions systematically?
Earlier quoted context omitted.
"accurate document extraction is becoming a commodity with powerful VLMs" Agree. The capability is fairly trivial for orgs with decent technical talent. The tech / processes all look similar: User uploads file --> Azure prebuilt-layout returns .MD --> prompt + .MD + schema set to LLM --> JSON returned. Do whatever you want with it.
Why all those steps? Why not just file + prompt to JSON directly?