Oil and gas related document retrieval and management simplified for this leading energy intelligence provider

The Business Need

A lead information provider based out of London had been providing critical data insights like the recent asset and corporate valuations, M&A deals, corporate financials, and operating data to the oil and gas industry for the past three decades. The timely data furnished by the company as subscribed services has aided major oil and gas players in making key market decisions and in monitoring their competitors.

To present rich and accurate analytics and research intelligence, the company had to delve into a treasure trove of information seen in a huge volume of industry reports and presentations. A robust, scalable, and easily accessible web-based document search management system became the need of the hour to smoothen collation of data by the researchers.

Mobius being an avant-garde enabler of data across industries, saw through the challenges faced by the company and built an extensive application to upload, index, tag, download, bookmark, subscribe, and search documents made complete with an intuitive user interface.

Challenges we faced

Documents had to be tagged and indexed in a way that facilitates their retrieval at both document and page level. Mobius came up with a comprehensive, ML-infused solution involving the open-source, enterprise-level, full-text search engine - ElasticSearch for content search, Amazon S3 for document retrieval and MySQL to manage the backend content database.

How we solved the problem

During every data transaction, care was taken to keep client data secure and safe.

  • Documents to be tagged were uploaded to the tightly secured Amazon S3 server directly by the client which is then registered with a unique ID
  • The incoming documents to the virtual private cloud were distributed by an elastic load balancer accordingly thus taking advantage of its auto-scaling feature.
  • Provisions were made for users to tag the documents and suggested tags were pulled up from the ML-trained repository
  • The documents were then split into pages to aid page-level retrieval and then pushed to the Elasticsearch repository
  • Page indexing and content-based tagging within the page were achieved in Elasticsearch that makes it possible for editors to look up documents through keywords as well as phrase searches.
  • The document was then published in the custom portal developed by Mobius, that was well-fitted with user role-based access restrictions. The portal loaded with intuitive, UI-rich features that enable a researcher to download, bookmark, subscribe, tag and view the status of the registered documents.


The software solution provided by our experts brought all the documents to a single repository, skyrocketing the operational efficiency and productivity of the editors. The custom-built portal that could be accessed from any device and featured role-based limits for its users paved way for seamless collaboration among the content users.

Keyword search and phrase search made possible

Document-level & page-level indexing achieved

Custom-built portal with role-based limits & multiple device access

Machine learning solution implemented to intelligently suggest relevant document tags for editors

Looking out for a similar solution?