The partnership will enable the growth of AI Chat applications for accesso’s current and future clients.
TAMPA BAY, FL. – Satisfi Labs, leading conversational AI platform for destinations and experiences, announced that it has partnered with accesso Technology Group, the premier technology solutions provider for attractions and venues worldwide. The strategic collaboration between the two companies will empower streamlined and intuitive engagement between leisure & entertainment venues and their guests with AI-driven customer communication.
Satisfi Labs’ AI Chat solutions are built by attractions and venues, for attractions and venues, leveraging a proprietary NLP and context response system. Its AI Chat has been a cornerstone feature for organizations looking to help answer customer questions and transact revenue across sports, tourism, and entertainment. In partnering with Satisfi Labs, current clients of accesso will be able to take advantage of the momentum of artificial intelligence with automated, 24/7 buyer communications and experiences.
“AI has become part of everyone’s vocabulary over the last few months and we have been building our product for moments like these,” said Don White, CEO of Satisfi Labs. “We are thrilled to be working with Accesso to bring artificial intelligence to the attractions and venue ecosystem. Our technology will deliver on this new AI economy because it provides data straight from the source, ensuring significant ROI for tourism organizations everywhere.”
“We are excited to partner with Satisfi Labs, leveraging their AI Chat technology to help our clients meet continued and growing demand for self-service solutions, enhance operational efficiency and improve the overall guest experience,” said Steve Brown, CEO of accesso.
Satisfi Labs recently announced a patent pending for its context-based Natural Language Processing (NLP) technology. Satisfi Labs’ proprietary training methods for its NLP technology allow for a deep and wide understanding of questions and revenue opportunities. The model employs a contextual algorithm that organizes natural language understanding concepts into tiers across global, vertical, sub-vertical, and local levels to allow for scale and granular accuracy.
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