Nitrio's Transition to ML-Powered Intent Extraction for Advanced Sales Strategies

Nitrio is an artificial intelligence company that empowers sales and marketing teams through their state-of-the-art, data-driven NLP solution for sales optimization. The company's platform is designed to analyze inbound rep-to-lead messages to extract their intent and collect useful data about every sales representative's performance. This data is then utilized to drive data-proven buy-in strategies for Nitrio’s clients. The platform analyzes multiple different types of emails and inbound messages, which increases the demands for the accuracy of sentiment analysis. Nitrio's goal is to provide high-quality buy-in strategies for their clients while providing sales representatives with advice, thereby increasing the company’s potential to onboard enterprise clients.

Nitrio, an AI company specializing in sales optimization, was facing significant challenges with its Natural Language Processing (NLP) platform. The platform relied heavily on manual rules and heuristics-based models, which led to bottlenecks and scalability issues, hindering Nitrio's growth. The existing platform was unable to ensure the required level of accuracy for sentiment analysis of rep-to-lead messages, resulting in a significant number of messages being outsourced to a third party for manual analysis. This not only increased service costs but also created further bottlenecks and scalability issues. The platform's infrastructure demonstrated tight coupling between services, increasing their dependencies and negatively impacting team performance, causing data quality and consistency issues. Nitrio's platform was designed to efficiently analyze inbound rep-to-lead messages to extract their intent and collect useful data about every sales representative's performance. However, the reliance on manual processes and the inability to ensure 95% certainty in message intent identification were major setbacks.

To overcome these challenges, Nitrio collaborated with Provectus to design and build a new automated, ML-powered intent extraction platform for sales optimization. The new platform replaced over 4K regular expressions with a single model, preserving the same F1. The development and maintenance of regular expressions were replaced by crowdsourced data annotation and an Active Learning workflow. The ML platform utilized advanced neural networks coupled with natural language processing, developed using Tensorflow. Data annotation, data training, and data evaluation tasks run in the deep neural network were automated. The platform was located on a separate EC2 instance and worked based on hydrosphere.io. The messages that landed in or were sent from the ML platform were managed with Amazon SQS, eliminating the complexity and overhead while dealing with in and out messages. Continuous monitoring was built with Amazon CloudWatch, ensuring that Nitrio’s team had access to all the required logs, metrics, and events.

• The implementation of the ML-powered intent extraction platform brought about significant operational improvements for Nitrio. The accuracy of intent analysis increased, allowing Nitrio to deliver higher quality buy-in strategies for their clients while providing sales representatives with advice. This not only improved Nitrio’s outlook but also increased the company’s potential to onboard enterprise clients. The new machine learning platform mitigated scalability issues, as the team no longer had to heavily rely on third-party sentiment analysis companies. Structural bottlenecks were eliminated, and sales representatives could focus more on communicating with leads rather than processing dozens of emails manually to find out if a prospect was interested in a company’s product. This led to an overall improvement in Nitrio's operational efficiency and client acquisition.

• 50% increase in daily throughput

• 5x reduction in manual operations

• 20% reduction in operational cost