case study

Parameta Solutions

Parameta Solutions – Web Search Engine and Entity Extraction

Information Technology & Services

Business Impacts

32

different keywords searched in prospectus documents across the web

>90%

accuracy of classification model for identifying the downloaded documents

12

different entities/fields fetched from the documents

Customer Key Facts

  • Location : United Kingdom
  • Industry : Information Technology

Problem Context

Parameta Solutions uses search engines to assist with its regulatory compliance. Due to the lack of an automated search framework, the client had to manually search and browse through the web to locate the prospectus documents, and further analyze the document content from a regulatory perspective.

They were looking for a Search and Extract solution to search, extract, and analyze public prospectus documents on the web using predefined keywords, automating the existing manual process.

Challenges

  • Laborious process of searching the relevant documents on the internet
  • Manual classification of prospectus/non-prospectus documents
  • Limitation in identifying the key entities/fields for regulatory compliance
  • Access to the latest data on the internet

Technologies Used

Google Cloud

Google Cloud

Google Cloud Identity Access Management

Google Cloud Identity Access Management

Google Cloud Storage

Google Cloud Storage

Google Cloud Scheduler

Google Cloud Scheduler

Google Cloud Functions

Google Cloud Functions

Google BigQuery

Google BigQuery

Google Cloud Auto ML

Google Cloud Auto ML

Google Cloud Pub/Sub

Google Cloud Pub/Sub

Solution

  • Quantiphi built an easy-to-use customized Web Search and Entity Extraction solution for Parameta Solutions.
  • Powered by Google’s Programmable Search Engine, the solution helps to search and locate the prospectus documents from the internet using 32 predefined keywords. 
  • The identified documents are downloaded and stored in Google Cloud Storage buckets. A classification model categorizes these documents into two types: Prospectus and Non-prospectus. 
  • These documents are then passed through an end-to-end automated entity extraction pipeline which helps extract the required entities from the documents using AutoML models.
  • The extracted entities are stored in BigQuery for downstream analytics, and can be easily exported as .CSV files. 
  • The entire solution is supported by a robust GCP infrastructure.

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