Client lacked the ability to skim through subsections of documents. So, we built a text extraction model, which leveraged relevant information from various text documents to cluster and interpret sections.
SOLUTION APPROACH
Data Sources
Scrape the required documents from various online and offline databases
Clean-up the data to be in the right format and extract the right set of key words
Create vectors based on the right set of similar attributes
Leverage algorithms such as Levenshtein distance, cosine similarity to build the final score of the model
VALUE CREATED
Measurable results
Meaningful outcome to random text documents
Classification of the document fields and a search UI to skim through the sub-sections of documents
CONTACT
Have a question about AI strategy?
Helping you is our business. We make complicated solutions simpler, every day. Ask us anything, we usually reply within 24 hours.