Yellow AI launches YellowG, a generative AI platform for automation workflows

Yellow AI launches YellowG, a generative AI platform for automation workflows

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Conversational AI platform yellow AI announced the release of G yellow, 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 enable companies to develop tailored solutions for various industries, streamlining complex workflows, improving existing processes and fostering innovation.

The platform boasts a state-of-the-art multi-large language model (LLM) architecture that is continuously trained over billions of conversations. The company says this architecture delivers exceptional scalability, speed, and accuracy, enabling businesses to leverage the platform’s full potential.

Yellow AI says it believes companies can achieve high levels of automation by integrating AI-powered chatbots like YellowG into customer and employee experiences across various channels. The company said such 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, ensuring immediate use from the moment a bot is created,” Raghu Ravinutala, CEO and co-founder of Yellow AI, told VentureBeat. “With its robust enterprise-level security, it ensures maximum security through a combination of global LLMs and centralized owners. Our real-time generative AI production is specifically designed to drive business conversations. This means that YellowG can generate workflows dynamically while easily handling complex scenarios”.


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AI with a human touch

The new tool allows users to generate runtime workflows and make real-time decisions using dynamic AI agents, said Ravinutala. Additionally, he adds a uniquely human touch to AI conversations by demonstrating near-human empathy while maintaining an incredibly low hallucination rate close to zero.

In addition to its multi-LLM architecture, YellowG uses business data and industry-specific knowledge to navigate complex scenarios. The chatbot’s ability to understand the context of conversations allows it to provide personalized responses that are finely tuned for specific use cases.

“YellowG Workflow Builder is powered by ‘Dynamic AI Agent’, our orchestration engine that harnesses the power of multiple LLMs,” said Ravinutala. “Uses our proprietary platform data insights, anonymous historical logging of customer interactions, and corporate data.”

The yellow AI claims a response intent accuracy rate of over 97%. Additionally, the company asserts its ability to learn from large volumes of data, allowing it to generate answers to even the most complex questions that traditional conversational AI platforms might find challenging.

Automate 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 to achieve their goal. Subsequently, the Generative AI 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 fine-tune the AI ​​bot’s response. This ensures an accurate alignment between the response generated, the information obtained and the initial customer request.

YellowG implements responsible AI practices during the post-processing phase by rigorously reviewing security, compliance and privacy measures. After that review, it provides answers that exhibit human-like characteristics, displaying exceptional accuracy and virtually no hallucinations.

“All the while, he stays focused on achieving business goals,” said Ravinutala. “Our multi-LLM architecture combines the intelligence of centralized LLMs with the precision and security of proprietary LLMs.”

Real-time generative AI

By integrating advanced artificial intelligence and natural language processing (NLP) technologies, the platform offers customers a human-like experience. The company said the platform generates responses that aren’t pre-specified using generative AI in real time, resulting in a more natural and seamless conversation flow.

“Our platform was 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 need for assistance, allowing it to tailor responses and provide the emotional support one would normally expect from a human agent. This empathic interaction establishes a deeper level of understanding, assuring clients that their feelings are truly acknowledged.”

An important feature of YellowG is its ability to adapt to the communication style and unique needs of the client. For example, if a customer prefers short, concise answers or requests more complete explanations, YellowG can tailor their answers accordingly.

The platform’s AI agent also leverages real-time analytics of user responses to guide the conversation, resulting in highly personalized and tailored interaction.

Zero setup for instant LLM incorporation

YellowG’s zero-configuration feature allows it to import and analyze its customers’ documents and websites. This comprehensive knowledge integration allows the platform to provide immediate answers to any question that falls within the scope of these resources.

“For clients with large knowledge stores, this capability alone allows us to provide a high level of automation from day one,” said Ravinutala.

Additionally, 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 in handling smooth conversations, often imposing rigid conversation flows on users.

“To overcome this limitation, we implemented dynamic runtime workflows that adjust based on user input,” added Ravinutala. “This approach allows us to automate a significantly large number of customer inquiries.”

Ravinutala said the company has successfully developed data-trained proprietary LLMs in-house for various domains and use cases, including document Q&A, contextual history, and summary.

Yellow AI’s primary focus is on addressing complex end-user facing scenarios in customer service, marketing, and employee experience where real-time decision making is critical. Ultimately, the goal is to leverage LLMs during runtime to redefine and improve end-user experiences.

“One such use case that we’ve solved using an internal model is summarizing for situations that require fast response times,” he said. “We also created a proprietary context model that allows our dynamic AI agents to understand the context of the conversation more accurately.”

Safeguard customer data through security compliance

According to the company, YellowG is designed to be truly multi-cloud and multi-region, adhering to the strictest security standards and compliance requirements. It also implements stringent measures to hide personally identifiable information (PII) from third-party LLMs, effectively safeguarding client data.

In addition, the platform successfully meets the criteria established by the SOC 2 Type 2 certification. This certification attests to the fact that YellowG’s systems and processes are specifically designed to protect customer data while maintaining exemplary levels of security and privacy.

“To improve data access control, Yellow AI uses a role-based access control (RBAC) system, which gives 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 higher.”

What’s next for yellow AI?

Ravinutala said Yellow AI envisions a future where AI is accessible to all, enabling customers, employees and businesses to connect effortlessly. To shape this vision, the company is committed to driving generative innovation in AI and continuously investing in research and development.

Furthermore, this vision involves harnessing the potential of use case-trained multi-LLMs as the future of generative AI in the domain of conversational AI. As such, the firm is actively piloting and leveraging the power of several LLMs, while developing in-house ones specifically tailored for corporate use, further strengthening the platform.

“In addition to building chatbots, we are focusing on using LLM as a robust layer of intelligence to provide solutions for complex end-user use cases that require real-time decision making,” said Ravinutala. “Our AI-powered generative capabilities, such as goal-oriented conversations, have attracted significant interest and rapid adoption. We also recognize the importance of responsible and ethical AI practices.”

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