Top 10 AI Features For Chatbot Vendor Assessment
Dong Liu and Nader Ghaffari

After an extensive deep dive into emerging AI chatbot vendors, including interviews and hands-on product trials, we are excited to present our comprehensive report on the state of the market and landscape. In this blog post, we share an excerpt from our report that sheds light on the AI capabilities that these chatbot vendors bring to the enterprise. Our goal is to educate you on the potential benefits for your company and provide a helpful framework for evaluating chatbot vendors.
Top 10 AI Features for Chatbot Vendor Assessment:
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A full definition for each of these 10 AI features is listed below.

1. Document Parsing: This feature enables the enterprise user to upload a document (Text, PDF, CSV, DOC, etc.) or a website as a data source, and the AI will automatically and instantly parse and understand the document to extract relevant information for answering customer questions.
2. Intent Matching: This feature enables the enterprise user to define and create a variety of intents with multiple utterances (example sentences) for each intent. It will then use AI to detect how similar the customer input is to any of the utterances for the intents. If the customer input is similar enough to an utterance, the intent for that utterance is then triggered to lead the customer through the intent’s flow.
3. Utterance Scoring: This feature uses AI to assign a score to each utterance of all the predefined intents created by the enterprise user. This score is based on how close the utterance matches the intent and how unique the utterance is in comparison to other intents’ utterances. The utterance that closely matches its intent and is distinct from the other utterances would receive a high score.
4. Match Threshold Setting: When the customer input is matched to the utterance of an intent, their similarity is calculated by AI into a match score. This feature enables the enterprise user to see that match score and adjust its threshold at which the customer input is deemed similar enough to trigger the intent. A lower match threshold means that inputs with lesser similarity to the intent’s utterances would also trigger the intent, while a higher threshold means that only those with greater similarity to the utterances would trigger the intent.
5. Unmatched Input Training: This feature enables the enterprise user to see all the customer inputs that were not matched to any predefined intents due to their lack of similarity to these intents’ utterances. These unmatched inputs are then matched by AI to the appropriate predefined intents. The user could then train these inputs by adding them to these intents’ utterances, or creating new intents and adding them to these new intents’ utterances.
6. Response Generation: This feature enables the enterprise user to customize the chatbot to automatically respond to customer questions and requests using AI. The chatbot’s responses, personality, and language would be defined by the user using natural language prompts (“Give the customer a travel tip in French.” “You are a helpful assistant who answers questions as politely as possible.”)
7. Variation Generation: This feature leverages AI to automatically generate different variations of the data created by the enterprise user in building the chatbot. The variations can be generated on the utterances for each intent, the entities for a particular topic, and the responses to customer input.
8. Guardrails Setting: This feature enables the enterprise user to set and control the guardrails for the proprietary or third-party LLMs used by the chatbot to answer customer requests. The user can manually adjust the AI model’s temperature, which determines the variability of its responses based on the user's prompt. The lower the temperature, the stricter the AI guardrails are set, resulting in more precise responses from the chatbot.
9. Contextual Memory: This feature enables the chatbot to remember the customer’s identity, previous messages, and past interactions. The contextual memory would then be used by AI to personalize the chatbot’s responses to each customer, skip certain questions if the answer is already recorded, and guide the customer back on track after the conversation flow is interrupted by an out-of-scope question.
10. Sentiment Analysis: This feature enables the chatbot to detect the emotional content of the customer input and respond appropriately and empathetically using AI. For example, if the customer talks about a happy personal event, the chatbot would respond in a congratulatory manner. If the customer shares news of a personal tragedy, it would respond with sympathy. If the customer displays overtly negative emotions, it would transfer the conversation to a human agent for more hands-on support.
11. By exploring these 10 key AI features across chatbot vendors, you can evaluate their capabilities and select the best fit for your organization. These AI features have the potential to enhance customer satisfaction, drive productivity, and boost sales in your enterprise.