Thank you. Yeah I looked at a few, another one is air.dev. The problem is they are recreating the actualy coding pane. Switchboard runs terminal directly.
We're building Doctly.ai - PDF Extraction with AI.
We started out with document conversions to Markdown but quickly realized that most use cases were for JSON conversion. We recently launched our "Extractor Studio" where you can have AI analyze a few sample variations of your documents and come up with a schema for you and publish it to an API endpoint.
We've built a technique on top of AI models that dramatically improves run to run consistency of JSON output.
Customers are willing to pay for accuracy compared to existing solutions out there. We started out in need of an accurate solution for a RAG product we were building, but none of the solutions we tried were providing the accuracy we needed.
We've been getting great results with those aswell. But ofcourse there is always some chance of not getting it perfect, specially with different handwritings.
Give it a try, no credit cards needed to try it. If you email me ([email protected]) i can give you extra free credits for testing.
Great question. The language models are definitely beating the old tools. Take a look at Gemini for example.
Doctly runs a tournament style judge. It will run multiple generations across LLMs and pick the best one. Outperforming single generation and single model.
Haha for sure. Naming isn't just the hardest problem in computer science, it's always hard. But at some point you just have to pick something and move forward.
We'll definitely be doing more tests, but the results I got on the complex tests would result in a lower score and might not be worth the extra cost of the judgement itself.
In our current setup Gemini wins most often. We enter multiple generations from each model into the 'tournament', sometimes one generation from gemini could be at the top while another in the bottom, for the same tournament.
We need to update the examples on the front page. Currently for things that are considered charts/graphs/figures we convert to a description. For things like logos or images we do an image tag. You can also choose to exclude them.
The difference with this is that it took the entire page as an image tag (it's just a table of text in my document). rather than being more selective.
I do like that they give you coordinates for the images though, we need to do something like that.
Give the actual tool a try. Would love to get your feedback for that use case. It gives you 100 free credits initially but if you email me ([email protected]), I can give you an extra 500 (goes for anyone else here also)
From my testing so far, it seems it's super fast and responded synchronously. But it decided that the entire page is an image and returned `` with coordinates in the metadata for the image, which is the entire page.
Our tool, doctly.ai is much slower and async, but much more accurate and gets you the content itself as an markdown.
I love mistral and what they do. I got really excited about this, but a little disappointed after my first few tests.
I tried a complex table that we use as a first test of any new model, and Mistral OCR decided the entire table should just be extracted as an 'image' and returned this markdown:
```
```
I'll keep testing, but so far, very disappointing :(
This document I try is the entire reason we created Doctly to begin with. We needed an OCR tool for regulatory documents we use and nothing could really give us the right data.
Doctly uses a judge, OCRs a document against multiple LLMs and decides which one to pick. It will continue to run the page until the judge scores above a certain score.
I would have loved to add this into the judge list, but might have to skip it.
The problem with this solution is that if A writes Token_A to resource, and then A dies, then the resource can never be written to again. How do you avoid not needing to time out the 'curr_lock'
https://github.com/doctly/switchboard/releases/tag/v0.0.8