Solve business challenges by using unstructured text data through Machine learning and Natural language processing.
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Remove Duplicate Customer Records
When stored across different data sources, customer information such as names and addresses, can be written differently, so it’s not feasible to directly match sources. Deduplication solves this challenge.
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How can I resolve cases where the same customer is present in different data sources? See more
Fuzzy Matching techniques match data from different sources. Additional techniques are used to perform this matching in a time-efficient manner.
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Create Standard Product Catalogs for MDM
Use text descriptions to map your products into a standard catalog & hierarchy.
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How can product descriptions be used to predict a category, subcategory, brand, and sub-brand? See more
Product Mapping and Classification uses machine-learning algorithms to classify data, such as product information, into a standard hierarchy.
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Drive Insights from Voice of Customer Data
Extract sentiment and key topics from product reviews, sales performance reviews, tweets, and surveys to understand what customers say and feel about your products and services.
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How can you extract customer opinions from text? See more
Sentiment Analysis identifies ‘sentiment carrying’ words, features, and aspects about products and brands.
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How can you find out the most prominent topics being talked about by customers? See more
Topic Modeling discovers topics in a collection of text documents based on their similarity.
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What prominent aspects of your brand, product, or service are customers discussing? See more
Context Extraction uses unsupervised text mining to identify and extract specific aspects from text data.
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Our Thinking
How knowledge graphs are the answer to better decisions for your business
Knowledge graphs systematically solve enterprise data integration challenges related to AI adoption by creating a fluid data environment. Find answers from Sanjeev Kumar as he shares how these knowledge graphs are the answer to better decisions for your business.
Case Studies
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0 min. readThe art of conversation
See how our automated solutions improved customer service for a leading general insurance firm.
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2 min. readLeverage unstructured data to improve preventive care
A health insurer uses unstructured data to better predict customer claims and enable focused care.
The Big Picture A major US health insurance firm wanted to assess the riskiness of its customers. Traditionally, the company used structured data sources, such as customer demographics, past claims data, and past health...
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2 min. readEnable guided selling using natural language search queries
A leading CPG builds a cognitive search engine to improve product recommendations.
The Big Picture A large health and wellness products company was looking to build a cognitive learning platform that would enable guided selling based on specific consumer needs. It was also looking to create a...
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2 min. readMatch customer information across multiple data sources
A credit information services provider uses Fuzzy Matching to identify 80M more candidates to serve.
The Big Picture One of the largest providers of credit information and information management services in the world wanted to develop an algorithm to appropriately link names and addresses across multiple datasets. The...
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2 min. readIdentify social media followers’ interests to deliver targeted offers
An analytics company uses personality identifiers to uncover targetable traits of Twitter followers.
The Big Picture A leading loyalty analytics company wanted to identify the personality traits of ~64K of its loyalty shoppers based on their Twitter feeds. This uncovered a few challenges that needed to be examined...
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1 min. readIdentify relevant report information from unstructured data
A bank uses unstructured text to automate and expedite the cataloguing and organization of reports.
The Big Picture A leading bank was conducting projects on socioeconomic issues, which resulted in the production of unstructured data in the form of a plethora of files. Given a database of documents and concepts as use...
Our People
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Suraj Amonkar
VP - AI@Scale
- Building horizontally scalable, easy-to-deploy AI solutions for various problem-statements
- Innovation to extend cutting-edge AI capabilities for better accuracy for complex problem statements
- Building next-generation capabilities that leverage A.I. algorithms and cutting-edge technology.
