Enterprise Case Study
One of the largest integrated primary producer of aluminum in Asia uses Pluaris Insight with human-like cognitive capabilities in comprehending data with precision in information retrieval, extraction, analysis, benchmarking, and creating useful outputs for its users in real-time.
To face the challenges of ever-evolving market and position the company in a sustainable growth path, one of the largest integrated Bauxite-Alumina-Aluminum-Power Complex in Asia, uses the strengths of Pluaris Insight. The built in functionalities leveraging machine learning (ML) and natural language processing (NLP) effectively retrieves, analyzes, and presents data to Users across functions assisting in decision-making to meet their costs and growth objectives.
The problem faced by National Aluminium Company Limited (NALCO) is to obtain insightful intelligence quickly from its huge databases. Like most other companies and even reported by IBM, 70% of time is spent finding data while only 30% analyzing it.
Use novel techniques to start with assimilation of data from diverse data entities to generate comprehensive and organization-specific intelligence. Provide a dynamic and smart search engine that serves as a precise information retrieval engine to create diagnostic and predictive action plans.
During the trials and to maintain privacy of Nalco’s data, Nowigence was asked to provide an end-to-end solution as simulation. A new employee Ram Kumar has joined NALCO as an Executive Assistant to the Chairman & Managing Director (CMD) Mr. Shridar Patra. The Annual General Meeting (AGM) is scheduled for 4 days later. Ram has to create up to date briefings on the topics that were discussed during AGM’s the previous 5 years and provide answers to possible questions that can be raised by investors.
The Problem Statement
During the trials, Nowigence was asked to provide an end-to-end solution as simulation. A new employee Ram Kumar has joined NALCO as an Executive Assistant to the Chairman & Managing Director (CMD) Mr. Shridar Patra. The Annual General Meeting (AGM) is scheduled for 4 days later. Ram has to create up to date briefings on the topics that were discussed during AGM’s the previous 5 years and provide answers to possible questions that can be raised by investors.
The following data processing steps were performed to achieve the goal, and they were shown the backend processing in Pluaris as well as the outputs within Insight.
- Upload Customer Data: A total of 35 documents including records of the previous AGMs were uploaded. Most were PDF files with internal reports containing structured and unstructured data.
- Named Entities Extraction: A total of 6018 labeled entities labeled under 10 categories were extracted. The most frequently occurring entities resulted in Ram knowing the major topics that were discussed during last AGMs.
- Ask Me and Benchmarking: Ram used Ask Me functionality to get precise answers to specific questions on most frequent NERs. He used Benchmarking functionality on frequently occurring NERs to search for insights in various documents related to the same topics.
- Key Insights Extractor: Ram used “Ctrl+F” frequently on the results displayed on Benchmark screen to look at the key points extracted for frequently occurring topics under high volume NERs.
- Compiling Smart Notebook & Sharing: He started pushing key points extracted from many documents on key topics to a Notebook. Ram then shared the contents of the Notebook with principal stakeholders and department heads to review.
He followed the same steps 1 to 5 listed above to create notebooks and collaborate with team members on other key topics.
The Key Stakeholders & Department Leaders made edits to the Notebook in real-time.
Ram reviewed the edits, printed the dozier, handed it over to the CMD for him to read during the evening and prepare for the AGM the next day.
During the trial, data was uploaded manually, and we did not connect to any of their internal systems. However, Pluaris Insight can integrate with data sources like CRMs, billing systems, customer issue resolution systems, shared drives, knowledge banks, Monday, HR systems, and other data applications that are required to address customer issues. These are all connected through a REST API integration.
Nowigence has built an array of capabilities centered around innovations in Auto Machine Learning, Data Unification, Data Science, and Natural Language Processing (NLP), all combined with a focus on delivering diagnostic, predictive, and prescriptive insights for users across functions in any organization. We automate reading and comprehension of data, be it text-based, image, sound or numerical with a “Google-Like” integrated intelligent AI Engine called Pluaris. It contextualizes what it reads from millions of documents from your private or public data sources.
Read 35 documents/PDFs
Identify key themes/topics
Key Point extraction to build dossier
Create Dossier per topic
Meet with key stakeholders
Total Time Spent
< 2 Hours