Level AI is a state-of-the-art AI-native solutions company that was designed to drive efficiency, productivity, scale, and excellence in sales and customer service. Pulse 2.0 interviewed Level AI founder and CEO Ashish Nagar to learn more about the company.
Ashish Nagar’s Background
What is Ashish Nagar’s background? Nagar said:
“Although I have a degree in Applied Physics, my background is at the intersection of technology and business, with a focus on product development and launching and building new companies. I’ve been deeply involved in product roles, which led me to AI.”
“I began in AI in 2014 with Rel C, a mobile search company similar to Perplexity AI, which was later acquired by Amazon. At Amazon, I led the Alexa team’s efforts on the Alexa Prize project. This initiative aimed to create a computer capable of understanding and responding to any question, inspired by the “Star Trek computer.” We launched it as a global AI challenge, collaborating with top institutions—a challenge we did not effectively address.”
“Since then, technologies like ChatGPT have come along and achieved what we envisioned in our earlier concepts. However, the large companies behind these different solutions and tools, like Amazon and Google, have been primarily focused on AI models without addressing end-to-end workflows. We saw this gap and launched a voice assistant for frontline workers, raising $2 million in seed funding. Customer feedback led us to pivot to contact centers, where existing solutions were outdated. We built a new platform for customer experience intelligence and service automation, addressing AI’s challenges with understanding intent, context, and domain-specific knowledge in customer service.”
Core Products
What are the company’s core products and features? Nagar explained:
“We are dedicated to advancing AI technology, specifically generative AI and contact center-specific LLMs, to enhance both contact centers and their customer interactions. Harnessing the power of unstructured conversational data, we have built a series of AI Agents to automate quality assurance, serve as a CX Copilot, and systemize the voice of the customer through deep generative AI insights. Approximately 90% of all business data is unstructured, underscoring the critical role of a solution like ours.”
“Looking at our offerings, we focus on the initial customer experience via Agent Assist which gives agents all the information they need at their fingertips while simultaneously giving manager’s unprecedented visibility into ongoing conversations across their team in real-time. We then have solutions that help improve your entire customer experience via Coaching, Voice of the Customer, and Analytics. These tools allow companies to continually monitor and improve their processes and teams at a level that was impossible with legacy systems.”
Challenges Faced
What challenges have Nagar and the team faced in building the company? Nagar acknowledged:
“This is one of the fastest-changing areas in technology, and several challenges come to mind. First, there’s the issue of customer education. The space is flooded with information, and there are often unrealistic expectations—sometimes too high, sometimes too low—about what can be achieved with AI technologies like OpenAI/ChatGPT.”
“Second, data privacy, security, and trust are major concerns. Enterprises seek assurance in these areas, but their systems and procurement teams are often not prepared to address the right questions or plan accordingly. We are actively working with our customers to address these issues.”
“Third, scaling and cost are critical challenges. As a vertically integrated company, we handle everything from the GPU layer to the AI application layer. We face issues with GPU availability and need to scale our systems rapidly due to increasing demand for our generative AI solutions.”
“The pace of innovation is so rapid that we often find ourselves catching up to drive new products as we onboard large enterprise customers. The market is evolving almost hourly, and there’s a common misconception that all AI is the same. Concerns about OpenAI, for instance, get applied broadly, even though our AI solution differs significantly from others like ChatGPT or Claude, despite using similar underlying tech. This misconception complicates our efforts, especially when addressing privacy concerns tied to inappropriate data usage in other models.”
Evolution Of Level AI’s Technology
How has the company’s technology evolved since launching? Nagar noted:
“Our first product wasn’t a customer service solution; it was a voice assistant for frontline workers, such as technicians and retail store employees. We raised $2 million in seed funding and showed the product to potential customers. They overwhelmingly requested that we adapt the technology for contact centers, where they already had voice and data streams but lacked the modern generative AI architecture. This led us to realize that existing companies in this space were stuck in the past, grappling with the classic innovator’s dilemma of whether to overhaul their legacy systems or build something new. We started from a blank slate and built the first native large language model (LLM) customer experience intelligence and service automation platform.”
State Of Customer Service
What is the current state of customer service and why do contact centers play a critical role in the customer experience? Nagar pointed out:
“According to data from Accenture, U.S. businesses collectively lose $1.6 trillion each year because of poor customer service. On the flipside, customers are willing to pay a 16% price premium with companies that deliver great customer experiences, so smart organizations are doing everything they can to ensure that customers are happy every time they engage with their brands. Since contact centers are often the first line of defense in the realm of CX, there’s perhaps nothing more important for an organization’s bottom line than optimizing contact center operations and ensuring every interaction is a productive and positive one.”
Customer Success Stories
After asking Nagar about company success stories, he highlighted:
“Using our automated QA solution, a large ecommerce customer specializing in stationery printing and worldwide merchandise development saw significant results. They achieved up to $20 million in ROI on their bottom line by improving their service reps’ handling of refund, order returns, and customer care conversations. Before implementing Level AI, they were spending about $110 million annually on refunds and returns. After better coaching and training based on our data, they reduced this expense by $30 million. We accomplished this through the automatic evaluation of service behaviors and identifying training and coaching opportunities.”
“Another example involves the use of our real-time Agent Assist and CX Copilot for managers and teams. This solution reduced repeated manual workflows by 15-20%, leading to improved productivity. Additionally, a large ecommerce furniture retailer automated their quality reviews from 20% to over 80% using Level AI. This automation enabled their team to train and coach reps with real data at scale, resulting in a stronger customer experience impact.”
Funding
When asking Nagar about the company’s funding details, he revealed:
“We recently closed a $39.4 million Series C funding round, bringing our total funding raised to date to $73.1 million. This round was led by Adams Street Partners, with additional investment from Cross Creek, Brightloop, many notable CX leaders; and participation from existing investors including Battery Ventures and Eniac Ventures.”
Total Addressable Market
What total addressable market (TAM) size is the company pursuing? Nagar assessed:
“The total available market (global customer service & automation intelligence) is $35 billion; the service addressable market (enterprise and commercial US customer service and automation intelligence) is $7 billion; and the service obtainable market (CX intelligence installed revenue) is $3.4 billion.”
Differentiation From The Competition
What differentiates the company from its competition? Nagar affirmed:
“We see our competitive advantage as a “T”. First, we offer the broadest AI and CX bundle available, providing a one-stop shop for all AI and CX applications. Customers don’t need multiple AI solutions for different functions—such as auto QA, invoice management, or real-time copilots—because our integrated AI solution covers all these needs in one place.”
“Second, our differentiated technical architecture is a key strength. We have a customer service-native LLM that’s home-built and vertically integrated for CX use cases. This architecture delivers 10 times better performance compared to other solutions, with 90% less reliance on professional services and 3 times faster time to market. Additionally, we offer full control over data privacy and security for enterprise customers, which further sets us apart.”
Future Company Goals
What are some of the company’s future company goals? Nagar concluded:
“With our recent funding, we’re heavily investing in our go-to-market teams and expanding our product suite around customer intelligence. Our goal is for Level AI to become the go-to, end-to-end solution for AI in customer experience.”
