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Conversational AI platform Yellow AI announced the release of YellowG, a next-gen conversational artificial intelligence (AI) platform designed specifically for automation technology. Leveraging the capabilities of generative AI and enterprise GPT, Yellow AI aims to empower enterprises to develop tailored solutions for various industries, streamlining intricate workflows, enhancing existing processes and fostering innovation.
The platform boasts a cutting-edge multi-large language model (LLM) architecture that undergoes continuous training on billions of conversations. The company claims that this architecture guarantees exceptional scalability, rapidity and precision, enabling businesses to harness the platform’s full potential.
Yellow AI says it believes that businesses can achieve elevated levels of automation by integrating AI-driven chatbots like YellowG into customer and employee experiences across various channels. The company said that such an integration not only significantly reduces operational costs but also enables 90% automation within the first 30 days.
“Our new platform is the first to achieve zero setup time, guaranteeing instant usage from when a bot is built,” Raghu Ravinutala, Yellow AI CEO and cofounder, told VentureBeat. “With its robust, enterprise-level security, it ensures maximum safety through a blend of centralized global and proprietary LLMs. Our productization of real-time generative AI is designed specifically to propel enterprise conversations. This means YellowG can generate workflows dynamically while easily handling complex scenarios.”
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AI with human touch
The new tool empowers users to generate runtime workflows and make real-time decisions using dynamic AI agents, said Ravinutala. Moreover, it 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 utilizes enterprise data and industry-specific knowledge to navigate complex scenarios. The chatbot’s capacity to comprehend the context of conversations enables it to provide personalized responses that are finely tailored to specific use cases.
“The YellowG workflow generator is powered by the ‘dynamic AI agent,’ our orchestrator engine that harnesses the power of multiple LLMs,” said Ravinutala. “It utilizes knowledge from our proprietary platform data, the anonymized historical record of customer interactions and enterprise data.”
Yellow AI claims a response intent accuracy rate of more than 97%. In addition, the company asserts its capability to learn from extensive volumes of data, enabling it to generate responses to even the most intricate queries that traditional conversational AI platforms may find challenging.
Automating business workflows through generative AI
When a customer’s message enters the conversational interface, YellowG promptly analyzes it to decipher the request and develop a strategic plan for fulfilling their goal. Subsequently, generative AI interacts with the enterprise system to retrieve all relevant data necessary for processing the user’s request.
Leveraging this data, the platform utilizes an LLM orchestration layer to formulate and fine-tune the AI bot’s response. This ensures accurate alignment between the generated response, the obtained information and the customer’s initial request.
YellowG implements responsible AI practices during the post-processing stage by rigorously examining security, compliance and privacy measures. After that review, it delivers responses exhibiting human-like characteristics, showcasing exceptional accuracy and virtually no hallucinations.
“All the while, it remains focused on achieving the business objectives,” said Ravinutala. “Our multi-LLM architecture combines centralized LLMs’ intelligence with the precision 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-scripted by utilizing real-time generative AI, resulting in a more natural and seamless conversation flow.
“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 assistance, allowing it to adapt responses and provide the emotional support that one would typically expect from a human agent. This empathetic interaction establishes a deeper level of understanding, assuring customers that their sentiments are truly acknowledged.”
A prominent feature of YellowG is its capability to adapt to the customer’s unique communication style and requirements. For example, whether a customer prefers brief and concise answers or requires more comprehensive explanations, YellowG can adjust its responses accordingly.
The platform’s AI agent also leverages real-time analysis of the user’s responses to guide the conversation, resulting in highly personalized and tailored interaction.
Zero setup for instant LLM incorporation
YellowG’s zero setup feature empowers it to ingest and analyze its customers’ documents and websites. This comprehensive integration of knowledge enables the platform to deliver instant answers to any inquiries that fall within the scope of these resources.
“For customers with extensive knowledge repositories, this capability alone allows us to deliver a high level of automation from day one,” said Ravinutala.
Furthermore, the platform’s no-code solutions facilitate seamless connectivity with 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 conversational flows on users.
“To overcome this limitation, we have implemented dynamic runtime workflows that adapt based on user input,” Ravinutala added. “This approach empowers us to automate a significantly large number of customer queries.”
Ravinutala said the company has successfully developed proprietary data-trained LLMs in-house for various domains and use cases, including document Q&A, contextual history and summarization.
Yellow AI’s primary focus is tackling complex end-user-facing scenarios within customer support, marketing and employee experience where real-time decision-making is crucial. Ultimately, the goal is to leverage LLMs during runtime to redefine and enhance end-user experiences.
“One such use case that we solved using an in-house model is summarization for situations that demand fast response times,” he said. “We have also created a proprietary context model that empowers our dynamic AI agents to understand the conversation’s context more accurately.”
Safeguarding customer data through security compliance
According to the company, YellowG is engineered 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 conceal Personally Identifiable Information (PII) from third-party LLMs, effectively safeguarding customer data.
Moreover, the platform successfully fulfills the criteria SOC 2 Type 2 certification sets forth. This certification attests to the fact that YellowG’s systems and processes are purposefully designed to protect customer data while maintaining exemplary levels of security and privacy.
“To enhance data access control, Yellow AI employs a role-based access control (RBAC) system, giving customers the ultimate authority to define access privileges,” said Ravinutala. “Every message exchanged through our platform is encrypted at rest using AES 256 encryption and in transit using TLS 1.2 and above.”
What’s next for Yellow AI?
Ravinutala said that Yellow AI envisions a future where AI is accessible to all, empowering customers, employees and enterprises to effortlessly connect. To shape this vision, the company strives to lead in generative AI innovation and continuously invest in research and development.
Furthermore, this vision entails harnessing the potential of use-case-trained multi-LLMs as the future of generative AI in the conversational AI domain. Therefore, the company is actively experimenting with and leveraging the power of different LLMs while also developing in-house ones specifically tailored for enterprise use, further fortifying the platform.
“Beyond creating chatbots, we are focusing on utilizing LLMs as a robust intelligence layer to provide solutions for complex end-user-facing use cases that require real-time decision-making,” said Ravinutala. “Our generative AI-powered features like goal-oriented conversations have gained significant interest and rapid adoption. Additionally, we also recognize the importance of responsible and ethical AI practices.”
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