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).

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.