Discover how to bring responsible generative AI to your health system.
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Extract rich conversational insights from patient interactions across channels
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Meet our GPT-powered assistant, trusted by the world’s leading health systems. Stellar conversations, superior access to knowledge, and unrivaled explainability–that’s Spot™.
Unlimited web pages and FAQs funneled into one no-code interface.
Instant implementation, with no training data or maintenance required.
Reduce the resources required to maintain GPT-powered interfaces.
Understand what yourpatients are asking about
Dive into how patients are interacting with your data
Easily customize and
improve responses
Monitor engagement and conversion rates
Spot keeps an eye on the content it uses to generate answers, assuring accurate, safe, and helpful responses.
GPT models are trillion-parameters black boxes: You know the inputs, you see the outputs, but there’s no telling what happened in between. Fixing errors starts with knowing how to find the problem, and with GPT, there’s no way to investigate the root cause of wrong and misleading conversations.
Unpack every conversation Spot conducts.Pinpoint the content pulled to formulate answers, understand the root cause, and quickly address any knowledge gaps by sanitizing your data with relevant, up-to-date information. We call this ‘Explainable-GPT’.
Up to
10x*
Less Hallucinations
78%*
More Resolutions
Generate custom, powerful responses to queries while navigating patients to relevant pages for deeper exploration.
Automatically channel the most relevant content on your website through GPT to generate human-centric answers.
Steer patients to the services and information they need by offering pathways to further discovery.
Handle complex queries with diverse dialects, syntax, synonyms and slang with robust Natural Language Understanding (NLU).
Combine the conversational prowess of large language models (LLMs) with HIPAA-compliant and PII-secure data scrapingcapabilities. All outputs, under your control.
Answers pulled exclusively from your HIPAA-compliant data. No external or unauthorized sources.
Proprietary built-in safeguards prevent ‘hallucinations’ and off-target responses.
Closed API ensures the benefits of GPT without exposing your data to third parties such as OpenAI.
Large Language Models (LLMs) refer to advanced AI models that are trained on vast amounts of text data to understand and generate human-like language. They use deep learning techniques, particularly transformer architectures, to process and generate natural language. LLMs, such as OpenAI’s GPT-4, have the capability to comprehend and generate text in a wide range of topics and styles. These models have been trained on diverse sources like books, articles, and websites, enabling them to provide answers, generate creative content, assist with language translation, simulate human conversation, and perform various other language-related tasks. LLMs have been a breakthrough in the field of natural language processing and have found applications in various domains, including customer service, content generation, language translation, and more. Learn more about LLMs.
Enterprises are hesitant to implement GPT and other large language models (LLMs) for several reasons. Firstly, LLMs may not meet the precision and accuracy requirements of sensitive industries like healthcare and government. They may not consistently provide correct answers, making them unsuitable for critical tasks. Secondly, the lack of explainability in LLMs poses a challenge. Enterprises need transparency in the reasoning behind the model’s outputs, but LLMs lack visibility and explainability, hindering effective debugging and tracing of inaccuracies.
Moreover, concerns about security and predictability arise with LLMs. They can be manipulated by biases in training data or user prompts, leading to potential risks and liabilities, especially in data-sensitive industries. Additionally, the limited adaptability of LLMs to various channels beyond chat interfaces hinders omnichannel deployment, a priority for many enterprises. Lastly, enterprises require complex actions and integrations, which LLMs may not support adequately. Achieving end-to-end automation and proactive behavior often necessitates additional capabilities beyond what LLMs can offer. These considerations push enterprises to seek alternative solutions that better align with their specific needs.