A smart vendor will shift into that space - they'll use that LLM themselves, and figure out some combination of finetunes, multiple LLMs, classical methods and human verification of random samples, that lets them not only "validate its performance, and deploy it with confidence into prod", but also sell that confidence with an SLA on top of it.
It's a bit late to start shifting now since it takes time. Ideally they should already have a product on the market.
But it is a good opportunity for a fast-moving OCR service to steal some customers from their competition. If I were working in this space, I'd be worried about that, and also about the possibility some of the LLM companies realize they could actually break into this market themselves right now, and secure some additional income.
EDIT:
I get the feeling that the main LLM suppliers are purposefully sticking to general-purpose APIs and refraining from competing with anyone on specific services, and that this goes beyond just staying focused. Some of potential applications, like OCR, could turn into money printers if they moved on them now, and they all could use some more cash to offset what they burn on compute. Is it because they're trying to avoid starting an "us vs. them" war until after they made everyone else dependent on them?
Then your secret sauce will be your fine tunes, etc.
Like it or not AI/LLM will be a commodity, and this bubble will burst. Moats are hard to build when you have at least one open source copy of what you just did.
Businesses that are just "today's LLM + our bespoke improvements" won't have legs.
Most of what we think software is today, will just be a UI. But UIs are also dead.
I would say most acceleracionist/AI bulls/etc don't really understand the true essential complexity in software development. LLMs are being seen as a software development silver bullets, and we know what happens with silver bullets.
Is that what you mean and, if so, is there anything in particular you've seen that leads you to see these problems being solved well or on the 18 month timeline? That sounds interesting to look at to me and I'd love to know more.
You can't do point sampling to figure out where things are going. We have to look at the slope. People see a paper come out, look at the results and say, "this fails for x, y and z. doesn't work", that is now how scientific research works. This is why two minute papers has the tag line, "hold on to your papers ... two papers down the line ..."
Copy and paste the whole thread into a SOTA model and have meta me explain it.
They're doubting you because the non-digital portions of processes change at people/org speed.
Which is to say that changing a core business process is a year political consensus, rearchitecture, and change management effort, because you also have to coordinate all the cascading and interfacing changes.
You are thinking within the existing structures, those structures will evaporate. All along the software supply chain, processes will get upended, not just because of how technical assets will be created, but also how organizations themselves are structured and react and in turn how software is created and consumed.
This is as big as the invention of the corporation, the printing press and the industrial revolution.
I am not here to tutor people on this viewpoint or defend it, I offer it and everyone can do with it what they will.
OK, sure, we can parse a PDF reliably now, but now we need to act on that data. We need to store it, make sure it ends up with the right people who need to be notified that the data is available for their review. They then need to make decisions upon that data, possible requiring input from multiple stakeholders.
All that back and forth needs to be recorded and stored, along with the eventual decision and the all supporting documents and that whole bundle needs to be made available across multiple systems, which requires a bunch of ETLs and governance.
An LLM with a prompt doesn't replace all that.
I have worked in large systems, both in code and people, compilers, massive data processing systems, 10k business units.
B2B is different from B2C, so if one vendor has a handful of clients and they won't switch away, there's no obliterating happening.
What's opened up is even lower hanging fruit, on more trees. A SaaS company charging $3/month for the left-handed underwater basket weaver niche now becomes viable as a lifestyle business. The shovels in this could be supabase/similar, since clients can keep access to their data there even if they change frontends.
There's definitely space here to help the customer realize their extraction vision because it's still hard to scale this effectively on your own!
- data is never shared between customers
- data never gets used for training
- we also configure data retention policies to auto-purge after a time period
One level of privacy is the workspace level separation in Mongo. But, if there is customer interest, other setups are possible. E.g. the way Databricks handles privacy is by actually giving each account its own back end services - and scoping workspaces within an account.
That is a good possible model.
LLM extractions are searched in OCR output, and if matched, the bounding box is displayed based on OCR output.
Demo: app.github.ai (just register an account and try) Github: https://github.com/analytiq-hub/doc-router
Reach out to me at [email protected] for questions. Am looking for feedback and collaborators.
1) I don't mind destroying the binding to get the best quality. Any idea how I do so?
2) I have a multipage double-sided scanner (fujitsu scansnap). would this be sufficient to do the scan portion?
3) Is there anything that determines the font of the book text and reproduces that somehow? and that deals with things like bold and italic and applies that either as markdown output or what have you?
4) how do you de-paginate the raw text to reflow into (say) an epub or pdf format that will paginate based on the output device (page size/layout) specification?
The problem then shifts from "can we extract this data from the PDF" to "how do we teach an LLM to extract the data we need, validate its performance, and deploy it with confidence into prod?"
You could improve your accuracy further by adding some chain-of-thought to your prompt btw. e.g. Make each field in your json schema have a `reasoning` field beforehand so the model can CoT how it got to its answer. If you want to take it to the next level, `citations` in our experience also improves performance (and when combined with bounding boxes, is powerful for human-in-the-loop tooling).
Disclaimer: I started an LLM doc processing infra company (https://extend.app/)