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).
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.
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).