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Very cool - I've been working on an open source python package that lets you do some similar things ( https://github.com/expectedparrot/edsl ). Here's an example of the Enron email demo using the edsl syntax/package & a few different LLMs: https://www.expectedparrot.com/content/6607caa1-efc5-439f-85...
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Appreciate the note. 1. I completely agree. Last-mile accuracy is crucial for enterprise buyers, and the challenge isn't just the AI. It's about mapping their business logic and workflows to the product in a way that demonstrates fast time to value. 2. Thanks for the feedback. We're still refining the messaging and don't want to be overly focused on just the extraction aspect. Do you think positioning it as ETL for u…
2. I think that "AI" and "unstructured data" sounded "cool" 5 years ago :) I'd be mindblown if you said, "We turn PDFs into structured data with 99.99% accuracy. Here is how:" And then tell me about fine-tuning human-in-the-loop stuff.
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I created automated descision suppport systems in asset based finance. For daily needs you get customer financials and other risk data from both official national sources and the likes of Dunn and Bradstreet, Graydon etc. The choice of providers depends on both the customer and deal risk/size. While the "api"'s to these providers might be clunky (putting structured request file on an ftp server and polling for a resp…
Thanks. I see the context now. Our asset managers are indeed lucky to have Bloomberg and such, which are easily integratable (and indeed, have been "SQL" for more than a decade now). I'm aware of the third party providers of customer financial information. Lucky to operate in a niche that is not served by them. Graydon (the only one I've been in contact with) is facing a massive disruption though. Their higher tiers…
The choice they have is submit a formal request to IT, be rejected 95% of the time with the remaing 5% being put in the planning with an eta 2-5 years in the future, or, DIY it with tools at hand. In an ideal world this would not be needed, in reality it is DIY or nothing.
Very cool - I've been working on an open source python package that lets you do some similar things ( https://github.com/expectedparrot/edsl ). Here's an example of the Enron email demo using the edsl syntax/package & a few different LLMs: https://www.expectedparrot.com/content/6607caa1-efc5-439f-85...
That is very cool, thank you for sharing.
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We use a combination of fine-tune LLMs models that're specialized at extraction, data validation and parsing and large foundational models for more general reasoning tasks. Model routing architecture has been quite interesting to explore.
Have you tried the Structured Output feature that OpenAI released last week?
Interesting! One quick question, how did you validate your data and ensure its correctness, since the ground truth is unstructured?
I assume that in the validation step if you don't get all those data points, then that routes to an error state for further review or something.
Very cool - I've been working on an open source python package that lets you do some similar things ( https://github.com/expectedparrot/edsl ). Here's an example of the Enron email demo using the edsl syntax/package & a few different LLMs: https://www.expectedparrot.com/content/6607caa1-efc5-439f-85...
> a major commercial bank I work with couldn’t improve credit risk models because critical data was stuck in PDFs and emails. Great use case! Worked on exactly this a decade ago. It was Hard™ then. Could only make so much progress. Getting this right is a huge value unlock. Congrats!
Make sure you have an on-premise option for this type of customer. I've worked at two software companies in Europe with tangentially similar products related to document analysis. On premise is a key requirement. Even though it's 2024, banks, financial institutions like insurance companies etc. tend to be _very_ cautious with valuable documents involving customers. There are also regional regulations that prevent thi…
Interesting! One quick question, how did you validate your data and ensure its correctness, since the ground truth is unstructured?
> a major commercial bank I work with couldn’t improve credit risk models because critical data was stuck in PDFs and emails. Great use case! Worked on exactly this a decade ago. It was Hard™ then. Could only make so much progress. Getting this right is a huge value unlock. Congrats!
Better still if it can then become a source of truth for further departures from reality.