“A marketing research client of ours was facing problems assessing the veracity of the metrics, indices and charts in their reports. We developed an NLP-based text scraping and validation solution which would validate the similarity between data sources like World Bank website on one hand and a PDF report on country-wise per capita income on the other. This reduced their man-hours burden to perform the manual QA drastically saving them big bucks.”

“A Fintech (employee loans) client of ours was about to touch a million active users. During the onboarding process, they used to ask the place of work from their users. Being a free-text field, it was prone to errors – some would misspell Amazon as ‘Amzon’ and others ‘Amazin’. They were missing out on critical insights about user’s place of work because of this issue. We brought Levenshtein Distance based text matching to their rescue. All such misspellings were traced back to ‘Amazon’. Now they could customize their marketing campaigns for organizations for better RoIs.”

“A client who was an ecommerce aggregator was having a hard time optimizing the search on their website. They had an overwhelming 100mn+ product listings across retailers with different catalogue category for the same product across different retailers. We used Naïve Bayes based text classification to categorize 100mn+ products to 5400 Google shopping categories. This improved their search results leading to 5x better conversions.”