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ElectrifAI Executive Interview

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ElectrifAi drives rapid business value for the C-suite through pre-built Machine Learning (ML) and Natural Language Processing (NLP) solutions for the modern data stack in just 6-8 weeks. They offer their innovative solutions to large and mid-size companies across industries worldwide. For the contact centers, ElectrifAi offers a suite of solutions from automatic call summarization to 100% call scoring, real-time sentiment analysis, and business-specific call reason to name a few. These solutions enhance customer experience by increasing first-call resolutions and improving agent productivity and retention. Through these cutting-edge pre-built ML solutions, ElectrifAi delivers time to value while lowering costs and risks for their customers around the globe.   

Sheri Greenhaus: Can you please provide us with an overview of ElectrifAi and its operations?

Russ Vetrano: ElectrifAi is an artificial intelligence and Machine Learning company. Our pre-built ML and NLP solutions harness the power of enterprise data to solve customers' complex business problems. Unlike a typical new platform rollout or IT modernization initiatives, our approach is to identify a specific problem and deploy ready to use pre-built ML solutions. The models are built using deep domain expertise and integrate seamlessly into existing platforms and solutions, delivering outcomes in 6-8 weeks. We have developed more than 100 Machine Learning models to solve specific business problems of our numerous customers across various industries, such as BFSI, Retail, Healthcare, Transportation and Hospitality, to name a few.

I joined the company approximately six months ago with a focus on applying our existing ML models to the contact center and call center market.

Sheri Greenhaus: Could you explain the concept of Machine Learning and differentiate it from Artificial Intelligence?

Russ Vetrano: Machine Learning involves the ability to process and analyze data inputs in a way that surpasses what a human can do. For example, if there are only a few data inputs, the job can be done using tools such as spreadsheets. However, when the number of inputs increases to thousands or more, or when they change frequently, it becomes impossible for a human to compute and analyze them efficiently. Machine Learning models, on the other hand, excel by learning from data trends and patterns. They can effortlessly process vast volumes of information in real-time, continuously adapting and learning. Machine Learning serves as the driving force behind Artificial Intelligence (AI) systems. It's crucial to differentiate between rule-based AI that uses rule-based algorithms, while true AI possesses understanding, learning, and adaptive capabilities.

Sheri Greenhaus: How does ElectrifAi distinguish itself from other companies operating in the same space?

Russ Vetrano: We excel in providing solutions that streamline manual processes in contact centers. Take call summarization as an example. Instead of agents manually providing a summary, our Machine Learning solution converts the call to text and extracts relevant information automatically. By analyzing the text, we identify the speaker's intent and valuable insights that are generally overlooked by many companies.

Another area is call reason identification. Agents typically select reasons from a checkbox or dropdown menu, which can be subjective or inaccurate. Our Machine Learning solution learns from data and identifies new call reasons, capturing nuanced information beyond predefined options. For instance, it recognizes respiratory issues instead of just "COVID." This continuous learning enhances accuracy and provides valuable insights.

Our USP is simple-we focus on solving specific business challenges through our pre-built ML models rather than embarking on a full-scale platform overhaul. That's why we have an edge over other solution providers.

Sheri Greenhaus: How does the process work once the call reason or other insights are identified? Is it automatically populated for review, or does it require manual intervention?

Russ Vetrano: The way we handle it depends on the call center's preference and the specific configuration. The identified core reason can be presented to the agent for review or fed directly into their system via an API. Call centers have the freedom to choose how they utilize this information, whether for the specific call or session, to enhance agent service.

We work closely with our customers to develop the appropriate taxonomy, which can be a comprehensive list of reasons or a tiered structure. For instance, in COVID-related calls, the taxonomy can include various sub-reasons related to respiratory issues or other specific concerns. Customers have the flexibility to define and customize call reasons as per their needs. Our models are 80-85% prebuilt, but we finetune and adapt them in collaboration with each customer.

Sheri Greenhaus: What kind of data is required to make these Machine Learning models effective?

Russ Vetrano: Our models are already highly developed, but each customer's data may have unique characteristics or requirements. We collaborate closely with our customers to understand their specific needs and tune the models accordingly, ensuring that the models deliver the highest level of accuracy and relevance for their particular context.

Sheri Greenhaus: Based on feedback from your customers, what are the major benefits they derive from using ElectrifAi's tools?

Russ Vetrano: Our solutions offer significant benefits. Firstly, companies can seamlessly integrate our Machine Learning models, avoiding costly platform upgrades. They can easily plug and play the specific models they need, delivering cost-effective solutions that boost accuracy and efficiency.

Sheri Greenhaus: Looking ahead, where do you see Machine Learning heading in the future?

Russ Vetrano: As Machine Learning continues to evolve and advance, its adoption across industries will expand. Companies are now gradually realizing the value and benefits of these solutions. Machine Learning will increasingly act as an aid to human operators, saving time and improving the overall effectiveness of their tasks.

Sheri Greenhaus: It seems that currently, ElectrifAi's offerings complement human efforts rather than replacing them entirely, providing support to complete tasks more efficiently. Could you expand on that?

Russ Vetrano: Our Machine Learning models free supervisors from manual tasks, such as listening to and scoring calls, allowing them to focus on strategic responsibilities. With large call centers, automating the wrap-up process through our models leads to significant cost savings. Machine Learning tackles what humans cannot accomplish due to time constraints, capacity limitations, and bandwidth limitations. By leveraging our solutions, companies can streamline their operations and significantly improve their efficiency.

Sheri Greenhaus: Any final thoughts?

Russ Vetrano: Our capability enhances existing platforms without requiring a complete overhaul. With our extensive experience and pre-built models, we can quickly adapt and fine-tune them to meet the specific requirements of each customer. We believe that our established track record and the ability to easily integrate our solutions position us as a valuable partner for companies in the contact center landscape.

For more information on our pre-built ML solutions for contact centers, watch our video or download our ContactCenterAi Whitepaper.