Project Screen


Expert Witness Profiler

The Project

The US Judicial Firm initially invested a lot of time in making the profile of an “EXPERT”. It took days to deliver ‘The Profile’ asked by the client. Moreover, the process was exhaustive and required a high amount of precision work for very long hours.

They wanted an indispensable tool to reduce the amount of manual work by decreasing the time taken to create a single profile and eliminate the tiring process of proofreading an unimaginable pool of documents.

Major Facts

  • Took 3-4 days for a single profile
  • Analyzed a large number of documents
Problem Statement


Chunking 1
Ambiguity resolution 2
Inference resolution 3
Non-Local reliance 4

The Solution

Named Entity Recognition (NER) labels sequences of words in a text that are names of things such as person and company names or gene and protein names. We used Stanford’s NER, a part of CoreNLP, which is a Java implementation of a Named Entity Recognizer.

It comes with well-engineered feature extractors for Named Entity Recognition and many more options for defining feature extractors.

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  • Testimonial history
  • Challenge (Daubert/Frye) history
  • Disciplinary history
  • Licensing and certifications (including verification)
  • Educational background (including verification)
  • Professional background
  • Associations and memberships
  • Personal information



The website offers a number of products ranging from preliminary screening to an exhaustive report on an expert.

Material Design

Developed with a responsive material design that works flawlessly and hence, giving end users a premium experience

Custom Evaluation

Create a customizable page of the Expert Witness similar to a Wikipedia page for displaying assessment in a simple, effortless way

Project Screenshots

 Documents Scanned & Extracted


Documents Scanned & Extracted

Increased Business Growth


Increased Business Growth

Increased Efficiency of Work


Increased Efficiency of Work


Hear it from the client


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