ElectrifAI Executive Interview
Russ Vetrano, SVP of Sales, ElectrifAI
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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
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
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.