Yellow AI for Workflows

 New to me, clearly useful idea.

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.”

EVENT

Transform 2023

Join us in San Francisco on July 11-12, where top executives will share how they have integrated and optimized AI investments for success and avoided common pitfalls.

Register Now

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.”    .... '