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Launch HN: Trellis (YC W24) – AI-powered workflows for unstructured data
71–80 of 124 posts
Re: Launch HN: Trellis (YC W24) – AI-powered workflows for unstructured data
#72Two quick questions: any plans on being hipaa compliant? Probably one of the biggest use cases for this is in health insurance, etc. How do your capabilities compare to Google Document AI or Watson SDU? Also what about standalone competitors such as Indico Data or DocuPanda?
Yes, HIPAA compliance is on the roadmap and should be out in a few weeks. We spent a lot of time on healthcare/sensitive data use cases.
Google Document AI and Watson SDU seem to be an afterthought for IBM/Google. The accuracy and configurability often fall short when you want to use them in a production setting.
Comparing to other legacy document processing companies, I think there are a few areas where we differentiate:
1. We handle end-to-end workflows from integrating with data sources, defining the transformation, and automatically triggering new runs when there’s an update to the data. 2. We built our entire stack on LLM and Vision transformers and use OCR/parser to check the results. This allows the mapping and tasks to be a lot more robust and flexible. 3. We have validations, reference checking, and confidence score metrics that enable fast human-in-the-loop iteration.
Re: Launch HN: Trellis (YC W24) – AI-powered workflows for unstructured data
#73I don't understand why you need an LLM for this, wouldn't a simple NER + entity normalization do this at a fraction of the cost? (congrats on the launch!)
Good question—NER and entity normalization work well for documents that have been standardized (e.g., IRS 1040a tax forms). However, the moment something slightly changes about the form, such as the structure of the table, the accuracy of NER drops dramatically.
This is why logistics companies, financial services, and insurance firms have in-house teams to process these documents (e.g., insurance claims adjusters) or outsource them to BPOs. These documents can vary significantly from one to another.
With LLMs fine-tuned on your data, the accuracy is much higher, more robust, and more generalizable. We have a human in the loop for flagged results to ensure we maintain the highest accuracy standards in critical settings like financial services.
Re: Launch HN: Trellis (YC W24) – AI-powered workflows for unstructured data
#74Curious how this compares to platforms like https://unstructured.io/
Re: Launch HN: Trellis (YC W24) – AI-powered workflows for unstructured data
#75Wow, this is game changing!
With your inventions, interestingly we might also be discovering reverse ETL use cases, where the insights/analytics obtained from the troves of unstructured data can be fed back into ERP/CRM/HCM systems, closing the complete loop and amplifying more business value!!
Congratulations to the Trellis team :)
Regards, Avinash
Re: Launch HN: Trellis (YC W24) – AI-powered workflows for unstructured data
#76Earlier quoted context omitted.
perhaps im misunderstanding the product offering here, isn't this just throwing PDFs (which also has unparsable content like formulas, symbols and large tables even with OCR) on an LLM with structured outputs and running SQL queries? isn't it obvious that this would be a problem that will eventually be solved by the LLM providers themselves including the ability to flag and apply business logic on top of the structur…
This misunderstanding is valid. Another example is why subscription/recurring billing software exists when payment gateways can solve this problem themselves. The elephant in the room is the complexities involved down the funnel that need very specific focus/solutions.
then please elaborate on "complexities involved down the funnel" and where I am misunderstanding with examples.
Re: Launch HN: Trellis (YC W24) – AI-powered workflows for unstructured data
#77I don't understand why you need an LLM for this, wouldn't a simple NER + entity normalization do this at a fraction of the cost? (congrats on the launch!)
NER is good for really simple things (like getting names, addresses, etc.).
A lot of the use cases that we see, like extracting data from nested tables in 100-page-long private credit documents or flagging transactions and emails that contain a specific compliance violation, are impossible to do with NER.
NER is good for really simple things (like getting names, addresses, etc.).
A lot of the use cases that we see, like extracting data from nested tables in 100-page-long private credit documents or flagging transactions and emails that contain a specific compliance violation, are impossible to do with NER.
With Trellis, the idea is taht you can write any mappings and transformations (no matter how complex the tasks or the source data are).
Re: Launch HN: Trellis (YC W24) – AI-powered workflows for unstructured data
#78Hey, 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).
Jason and Richard from Roe AI are amazing people! We were in the same YC batch and section. Excited for what Roe AI is building and their focus on building a new type of data warehouse.
At Trellis, we're focused on building the AI tool that supports document-heavy workflows (this includes building the dashboard for teams to review, update, and approved results that were flagged, reading and writing directly to your system of record like Salesforce, and allowing customers to create their own validations around the documents).
Re: Launch HN: Trellis (YC W24) – AI-powered workflows for unstructured data
#79Love the name! Electronic gardening vibe.
Re: Launch HN: Trellis (YC W24) – AI-powered workflows for unstructured data
#80Curious how this compares to platforms like https://unstructured.io/
Unstructured seems to be focusing a lot on the document chunking and data ingestion into RAGs part. Trellis handles the process end-to-end from extraction to transforming the data into the schema that you need for downstream applications.
The way unstructured built their parsing and extraction are mostly based on traditional OCR and rule based extraction. We built all preprocessing pipeline in an LLM and vision model first way that allows us to be flexible when the data is quite complex (like tables and images within documents).