Hey, congrats! Are you competing / is there some overlap / what are the key differences with Roe AI (YC W24) - roe.ai (just launched recently on HN https://news.ycombinator.com/item?id=41202694 as well).
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
#42Great idea. I used to work at Instabase, which you probably compete with. The better you are at dealing with dodgy PDFs and document scans, the more valuable this will be to big banks, shipping companies, etc.
Thanks! Always surprised to see how many dodgy PDFs and scans there is in enterprises.
Re: Launch HN: Trellis (YC W24) – AI-powered workflows for unstructured data
#43Re: Launch HN: Trellis (YC W24) – AI-powered workflows for unstructured data
#44> 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…
> 80% of enterprise data is unstructured I've seen quotes like this many times. It's silly. I worked at a big bank for over a decade. 95% of the data we cared about was already in a SQL database. Maybe ~80% of our data was "unstructured", but it wasn't stuff we cared about for risk management or other critical functions. > people are not willing to pay more money for less risk I'd disagree here. Banks are willing to…
Yep, and nowadays, banks are already deploying this stuff internally via their own IT teams. They have 1-2 decades of having built up ETL/orchestration talent + infra, and have been growing deals with openai/azure/google/aws/databricks for the LLM bits. Internally, big banks are rolling out hundreds of LLM apps each, and generally have freezes on new external AI vendors due to 'AI compliance risk'. NLP commoditized so it's a different world.
It makes sense on paper from a VC perspective as a big bet.. but good luck to smaller VC-funded founders competing with massive BD teams fronting top AI dev teams. We compete in adjacent spaces where we can differentiate, and intentionally decided against going in head-on. For those who can, again, good luck!
Re: Launch HN: Trellis (YC W24) – AI-powered workflows for unstructured data
#45Both fulltext (BM25 or SPLADE) and dense vector search have issues with documents of different lengths. Part of what makes recursive sentence splitting work so well are its length normalization properties.
Filters are a really important feature downstream of that which this system can provide.
We have also worked with the Enron corpus for demos and fast, reliable ETL for a set of documents that large is more difficult than it seems and a commendable problem to solve.
Exciting stuff!
Re: Launch HN: Trellis (YC W24) – AI-powered workflows for unstructured data
#46Digitizing and organizing old document scans for birth, marriage, and death records would be a huge win for genealogy research. The Mormon church would be a great customer for you.
For them and all other 50 AI PDF scanning wrappers that were featured on Show HN in the past month.
Re: Launch HN: Trellis (YC W24) – AI-powered workflows for unstructured data
#47Earlier quoted context omitted.
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 h…
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Re: Launch HN: Trellis (YC W24) – AI-powered workflows for unstructured data
#48Hey 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 qui…
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.
Re: Launch HN: Trellis (YC W24) – AI-powered workflows for unstructured data
#49Didnt work for me as expected
Re: Launch HN: Trellis (YC W24) – AI-powered workflows for unstructured data
#50I've had do some of this recently, as a one-off, to extract the same fields from thousands of scanned documents.
I used OpenAI's function calling (via Langchain's https://python.langchain.com/v0.1/docs/modules/model_io/chat... API).
Some of the challenges I had:
1. poor recall for some fields, even with a wide variety of input document formats
2. needing to experiment with the json schema (particularly field descriptions) to get the best info out, and ignore superfluous information
3. for each long document, deciding whether to send the whole document in the context, or only the most relevant chunks (using traditional text search and semantic vector search)
4. poor quality OCR
From the demo video, it seems like your main innovation is allowing a non-technical user to do #2 in an iterative fashion. Have I understood correctly?