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Launch HN: Trellis (YC W24) – AI-powered workflows for unstructured data

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Re: Launch HN: Trellis (YC W24) – AI-powered workflows for unstructured data

#21

Post by hamsterbooster »

doctorpangloss wrote: post #19

> At the Stanford AI lab where we met... 80% of enterprise data is unstructured, and traditional platforms can’t handle it You guys came out of an academic lab, so you must know that hypothesis fishing expeditions are not viable. > ... a major commercial bank... couldn’t improve credit risk models because critical data was stuck in PDFs and emails. In this example there will be no improvement to the risk model or wha…

Thanks for the feedback. We built Trellis based on our experience with ingesting and analyzing unstructured customer calls and chats in a reliable way. We couldn’t find a good solution apart from developing a dedicated ML pipeline, which is quite difficult to maintain.

There are some elements that might resemble Dagster, but I believe the challenging part is constructing validation systems that ensure high accuracy and correct schemas while processing all kinds of complex PDFs and document edge cases. Over the past few weeks, our engineering team has spent a lot of time developing a vision model robust enough to extract nested tables from documents

Re: Launch HN: Trellis (YC W24) – AI-powered workflows for unstructured data

#23

Post by hamsterbooster »

localfirst wrote: post #18

looks like more solutions looking for a problem that can be solved at the vendor level

In many use cases, like flagging documents for compliance issues or processing customer emails, it's challenging to manage this at the vendor level because end customers want the ability to apply business logic and run different analyses.

For data ingestion and mapping, I agree that in an ideal world, we would all have first-party API integrations. However, many industries still rely on PDFs and CSV files to transfer data.

Re: Launch HN: Trellis (YC W24) – AI-powered workflows for unstructured data

#24

Post by hamsterbooster »

darkhorse13 wrote: post #17

Congrats on the launch. Serious question though, does YC only fund AI companies these days?

Thanks! There are still a lot of amazing hardware companies and vertical applications in our YC batch.

We believe that AI is only one part of our product. A significant amount of value comes from building robust integrations with different data sources and managing the business logic that operates on top of this unstructured data.

Re: Launch HN: Trellis (YC W24) – AI-powered workflows for unstructured data

#25

Post by dang »

darkhorse13 wrote: post #17

Congrats on the launch. Serious question though, does YC only fund AI companies these days?

Nope! From yesterday:

Launch HN: Synnax (YC S24) – Unified hardware control and sensor data streaming - https://news.ycombinator.com/item?id=41227369 - Aug 2024 (23 comments)

also recent:

Launch HN: Stack Auth (YC S24) – An Open-Source Auth0/Clerk Alternative - https://news.ycombinator.com/item?id=41194673 - Aug 2024 (140 comments)

Launch HN: Firezone (YC W22) – Zero-trust access platform built on WireGuard - https://news.ycombinator.com/item?id=41173330 - Aug 2024 (88 comments)

Launch HN: Airhart Aeronautics (YC S22) – A modern personal airplane - https://news.ycombinator.com/item?id=41163382 - Aug 2024 (618 comments)

That's 4 of the 8 most recent Launch HNs btw. But it's true that there are reams of AI startups nowadays.

Re: Launch HN: Trellis (YC W24) – AI-powered workflows for unstructured data

#27

Post by serjester »

It seems like your business strategy is contingent on foundational model providers not improving their product on a couple dimensions: price, grounding accuracy and file handling. This is a risky strategy, especially in such a competitive market. Wishing you the best of luck.

Re: Launch HN: Trellis (YC W24) – AI-powered workflows for unstructured data

#28

Post by artembugara »

Hey folks. Congrats on the launch.

Everyone here knows that it's a really big problem that no one has nailed yet.

My 2 cents:

1. It took us (newscatcherapi.com) three years to realize that customers with the biggest problems and with the biggest budgets are the most underserved. The reason is that everyone is building an infinitely scalable AI/LLM/whatever to gain insights from news.

In reality, this NLP/AI works quite OK out of the box but is not ideal for everyone at the same time. So we decided to do Palantir-like onboarding/integration for each customer. We charge 25x more, but customers have a perfect tailor-made solution and a high ROI.

I see you already do the same! "99%+ accuracy with fine-tuning and human-in-the-loop" is what worked great for us. This way, your competitor is a human on payroll (very expensive) and not AWS Tesseract.

Going from 95% to 99% is just a fractional improvement, but it can be "not good enough" to a "great solution" change that can be charged differently.

2. "AI-powered workflow for unstructured data" what does it even mean? Why don't you say "99%+ accuracy extraction"? It's 2024, everyone is using AI, and everyone knows you need 2 hours to start applying AI from 0. So don't lower my expectations.

Re: Launch HN: Trellis (YC W24) – AI-powered workflows for unstructured data

#29

Post by elicash »

darkhorse13 wrote: post #17

Congrats on the launch. Serious question though, does YC only fund AI companies these days?

This year, nearly yes in "some way":

> This year, we’ll fund more than 500 companies out of 50,000 applications, and almost all of them are related to AI in some way.

Source: https://www.ycombinator.com/blog/why-yc-went-to-dc/

(Edited to be more precise.)

Re: Launch HN: Trellis (YC W24) – AI-powered workflows for unstructured data

#30

Post by visarga »

hamsterbooster wrote: post #21
doctorpangloss wrote: post #19

> At the Stanford AI lab where we met... 80% of enterprise data is unstructured, and traditional platforms can’t handle it You guys came out of an academic lab, so you must know that hypothesis fishing expeditions are not viable. > ... a major commercial bank... couldn’t improve credit risk models because critical data was stuck in PDFs and emails. In this example there will be no improvement to the risk model or wha…

Thanks for the feedback. We built Trellis based on our experience with ingesting and analyzing unstructured customer calls and chats in a reliable way. We couldn’t find a good solution apart from developing a dedicated ML pipeline, which is quite difficult to maintain. There are some elements that might resemble Dagster, but I believe the challenging part is constructing validation systems that ensure high accuracy a…

What is your metric and score? Maybe you have reached perfect reliability, but in my experience information extraction is about 90% accurate for real life scenarios, and you can't reliably know which 90%.

In critical scenarios companies won't risk using 100% automation, the human is still in the loop, so the cost doesn't go down much.

I work on LLM based information extraction and use my own evaluation sets. That's how I obtained the 90% score. I tested on many document types. It looks like it's magic when you try an invoice in GPT-4o and skim the outputs, but if you spend 15 minutes you find issues.

Can you risk an OCR error confusing a dot for a comma to send 1000x more money in a bank transfer, or to get the medical data extraction wrong and someone could suffer because there was no human in the document ingestion pipeline to see what is happening?

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