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Conversational AI platform yellow AI announced the launch of YellowG, a next-generation conversational artificial intelligence (AI) platform designed specifically for automation technology. Leveraging the capabilities of generative AI and enterprise GPT, Yellow AI aims to empower companies to develop customized solutions for various industries, streamlining complex workflows, improving existing processes, and fostering innovation.
The platform features state-of-the-art Large Multi-Language Model (LLM) architecture that undergoes continuous training in billions of conversations. The company claims that this architecture ensures exceptional scalability, speed, and accuracy, allowing businesses to harness the full potential of the platform.
Yellow AI says it believes businesses can achieve elevated levels of automation by integrating AI-powered chatbots like YellowG into customer and employee experiences across multiple channels. The company said that such integration not only significantly reduces operating costs, but also enables 90% automation within the first 30 days.
“Our new platform is the first to achieve zero setup time, ensuring instant use from the moment a bot is built,” Raghu Ravinutala, CEO and co-founder of Yellow AI, told VentureBeat. “With its strong enterprise-grade security, it ensures maximum security through a combination of centralized global and proprietary LLMs. Our real-time generative AI production is specifically designed to drive business conversations. This means that YellowG can dynamically build workflows while easily handling complex scenarios.”
AI with a human touch
The new tool allows users to build workflows at runtime and make decisions in real time using dynamic AI agents, Ravinutala said. Additionally, he adds a unique human touch to AI conversations by demonstrating near-human empathy while maintaining an impressively low hallucination rate, close to zero.
In addition to its multi-LLM architecture, YellowG uses business data and industry-specific insights to navigate complex scenarios. The chatbot’s ability to understand the context of conversations allows it to provide personalized responses that are finely tailored to specific use cases.
“YellowG’s Workflow Builder is powered by ‘AI Dynamic Agent’, our orchestration engine that harnesses the power of multiple LLMs,” said Ravinutala. “It uses data insights from our proprietary platform, anonymous historical record of customer interactions, and business data.”
Yellow AI claims a response intent accuracy rate of over 97%. In addition, the company asserts its ability to learn from big data, allowing it to generate answers to even the most complex queries that traditional conversational AI platforms may find challenging.
Automation of business workflows through generative AI
When a customer’s message enters the conversational interface, YellowG quickly parses it to decipher the request and develop a strategic plan to meet your goal. The generative AI then interacts with the business system to retrieve all the relevant data needed to process the user’s request.
Leveraging this data, the platform uses an LLM orchestration layer to formulate and tune the AI bot’s response. This ensures accurate alignment between the response generated, the information obtained, and the initial request from the client.
YellowG implements responsible AI practices during the post-processing stage by rigorously vetting security, compliance, and privacy measures. After that review, he gives answers that exhibit human-like characteristics, displaying exceptional accuracy and virtually no hallucinations.
“In the meantime, he remains focused on achieving business goals,” Ravinutala said. “Our multi-LLM architecture combines the intelligence of centralized LLMs with the accuracy and security of proprietary LLMs.”
Real-time generative AI
By integrating advanced AI and Natural Language Processing (NLP) technologies, the platform provides customers with a human-like experience. The company said that the platform generates responses that are not pre-written by using generative AI in real time, resulting in a more natural and fluid flow of conversation.
“Our platform has been designed to detect and interpret the emotional tone and sentiment expressed in the customer’s message,” Ravinutala explained. “It can recognize various emotions such as frustration, confusion, happiness or the need for help, allowing it to tailor responses and provide emotional support that would normally be expected from a human agent. This empathic interaction establishes a deeper level of understanding, reassuring clients that their feelings are truly acknowledged.”
A prominent feature of YellowG is its ability to adapt to a client’s unique communication style and requirements. For example, if a customer prefers short and concise answers or requires more complete explanations, YellowG may adjust its responses accordingly.
The platform’s AI agent also leverages real-time analysis of user responses to guide the conversation, resulting in highly personalized and tailored interaction.
Zero setup for instant LLM onboarding
YellowG’s Zero Configuration feature allows you to ingest and analyze your clients’ documents and websites. This comprehensive knowledge integration enables the platform to provide instant answers to any query that falls within the scope of these resources.
“For customers with extensive knowledge repositories, this capability alone allows us to offer a high level of automation from day one,” Ravinutala said.
In addition, the platform’s no-code solutions facilitate seamless connectivity to customer APIs, enabling the implementation of static workflows that unlock a new realm of automation. However, the company said it’s important to note that static workflows have limitations when handling fluid conversations, often imposing rigid conversation flows on users.
“To overcome this limitation, we have implemented dynamic workflows at runtime that adapt based on user input,” Ravinutala added. “This approach allows us to automate a significantly large number of customer inquiries.”
Ravinutala said the company has successfully developed data-enabled proprietary LLMs in-house for various domains and use cases, including document Q&A, contextual history, and summary.
Yellow AI’s primary focus is addressing complex scenarios end-users face within customer service, marketing, and employee experience, where real-time decision-making is crucial. Ultimately, the goal is to leverage LLMs during runtime to redefine and improve end-user experiences.
“One of those use cases that we solved using an internal model is the summary for situations that demand fast response times,” he said. “We’ve also created a proprietary context model that enables our AI dynamic agents to understand the context of the conversation more accurately.”
Protecting customer data through security compliance
According to the company, YellowG is designed to be genuinely multi-cloud and multi-region, adhering to the most stringent security standards and compliance requirements. In addition, it implements rigorous measures to hide LLM Personally Identifiable Information (PII) from third parties, effectively protecting client data.
In addition, the platform successfully meets the criteria set by the SOC 2 Type 2 certification. This certification attests to the fact that YellowG’s systems and processes are purposely designed to protect customer data while maintaining exemplary levels of security and privacy. .
“To improve data access control, Yellow AI employs a role-based access control (RBAC) system, which gives clients ultimate authority to define access privileges,” Ravinutala said. “All messages exchanged through our platform are encrypted at rest using AES 256 encryption and in transit using TLS 1.2 and higher.”
What’s next for the yellow AI?
Ravinutala said that Yellow AI envisions a future where AI is accessible to all, allowing customers, employees and businesses to connect effortlessly. To shape this vision, the company strives to lead AI generative innovation and continually invest in research and development.
Furthermore, this vision involves harnessing the potential of multi-trained LLMs in use cases as the future of generative AI in the conversational AI domain. Therefore, the company is actively experimenting and leveraging the power of different LLMs, while also developing internals specifically designed for business use, further strengthening the platform.
“Beyond building chatbots, we are focusing on using LLM as a strong intelligence layer to provide solutions for complex use cases faced by end users that require real-time decision making,” said Ravinutala. “Our AI-powered generative features, such as goal-oriented conversations, have gained significant interest and rapid adoption. Furthermore, we also recognize the importance of responsible and ethical AI practices.”
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