Sprinklr Gen AI Service Features
Updated
Sprinklr AI+ is an advanced AI-powered suite integrated into Sprinklr's Unified-CXM platform, designed to transform unstructured customer experience (CX) data into actionable services. The following table gives details on multiple Gen AI features of Sprinklr Service.
Feature Name | Feature Description | Available in AI+ Studio (Y/N) | Can feature be turned on or off? | Is this feature access controlled? |
Conversational AI | ||||
Text to Speech | Text to Speech or TTS is a machine learning based language model that synthesizes text into a desired voice. The goal is to generate synthetic speech that sounds natural and resembles human speech as closely as possible. Now with Sprinklr’s all new TTS technology, give your customers a human-like experience while interacting with voice assistants. | No | Yes | No |
Brands can enhance their bot's quality and efficiency by discovering contact drivers for bot workflows through a LLM powered data-driven approach. This eliminates the need for guesswork when implementing bots, while ensuring the coverage for customer journeys. | No | No | No | |
No | Yes | No | ||
FAQ chatbots are RAG-driven virtual assistants that automate responses to common customer inquiries, providing instant, accurate answers. They utilize NLP and RAG to understand user queries and retrieve relevant information from a pre-built knowledge base. By handling routine questions, these chatbots free up human agents to focus on more complex issues, improving overall efficiency and customer satisfaction. FAQ bot leverages Large Language Models (LLMs) to interpret and respond to a broader range of user questions, including those not explicitly covered in the predefined knowledge base. This capability enables them to generate contextually relevant answers, enhancing the user experience and providing more comprehensive support. | No | Yes | No | |
Agent Assist | ||||
Get proactive KB/FAQ articles suggestions based on customer care conversations on live cases. | No | Yes | No | |
Smart Response employs RAG to learn from past agent-customer conversations and articulate relevant response suggestions. | No | Yes | Yes | |
Smart Compose accelerates crafting of replies by delivering AI-backed phrase completion for agents. It uniquely does this by learning from past conversations to offer personalized suggestions, which can be swiftly selected with a Tab or Right Arrow Key. | No | Yes | No | |
Smart Paraphraser enables agents to send articulate, grammatically correct responses faster with AI-powered paraphrased suggestions. Paraphrasing will help agents to bring much-needed variety to conversations as it will help in reducing the robotic nature of conversations by enlarging the scope of usual agent replies. | No | Yes | Yes | |
No | Yes | Yes | ||
Case Summary | Get a Generative AI driven case summary of the conversation between a customer and the brand upon request without having to read the full conversation. | Yes | Yes | No |
Writing Assistance helps you generate relevant and engaging content for posts, reels, and messages, all within the Quick Publisher. It also provides proofreading and editing tools—including grammar and spelling checks—to ensure high-quality content creation across multiple channels. Users can quickly reword, simplify, or expand existing text, modify tone, translate content, and even generate hashtags or tailored post variations. | Yes | Yes | Yes | |
Generate Case Summary via AI+. | No | No | No | |
Generate Groovy scripts, summarize existing ones, debug issues, and modify scripts to achieve complex use cases. This reduces the time and effort involved in writing Groovy scripts, making it easier for users, even those not proficient in Groovy, to accomplish complex tasks. | Yes | Yes | Yes | |
Conversational Analytics | ||||
Get a Generative AI driven case summary of the conversation between a customer and the brand upon request without having to read the full conversation. | No | Yes, disable the rule & edit record page. | No | |
This generative AI based approach is responsible for uncovering new or previously unidentified customer intents through unsupervised or semi-supervised methods. By analyzing conversational data, it identifies recurring themes or questions that can be classified as new intents. It is designed to interact with customer-agent conversations, leveraging AI to enhance business intelligence by identifying, categorizing, and managing intents within conversations. | No | Yes | Yes | |
Given a large dataset of customer-agent conversations, its crucial to extract temporal anomalies and/or trends for similar kind of conversations. Using “Statistical Insights”, we generate anomalous collection of conversation cases with similar dimensions and/or conversation intents, which have an unexpectedly positive or negative trend in the latest day/week/month. This proves crucial in analysing latest trends of the client’s product/service performance. | No | Yes, update enabled insight type via red. | Yes | |
Given a large dataset of customer-agent conversations, its crucial to extract granular and focused information for agents employed by partners. Using “Agent Focused Insights”, we generate actionable stories, which take into consideration conversations from similar agent focused issue root causes and agent skills. | No | Yes, update enabled insight type via red. | Yes | |
Given a large dataset of customer-agent conversations, its crucial to extract granular and focused information for Product/Service based partners. Using “Product or Service Focused Insights”, we generate actionable stories, which take into consideration conversations from similar product/service focused issue root causes, focus areas and conversational intents. | No | Yes, update enabled insight type via red. | Yes | |
Given a taxonomy and case conversations belonging to a particular brand, this feature suggests new Contact Driver Intents that could be added to the existing taxonomy of any number of levels. These new intents are generated on the basis of the case conversations that could not be aptly matched to any of the existing intents. | No | Yes, update enabled insight type via red. | Yes | |
KB Gap Analysis aims at improving KB articles by adding information to existing KBs or creating new KB Articles by using information present in customer-brand conversations. | No | Yes, update enabled insight type via red. | Yes | |
KB Refinement | This feature analyses any number of structured or unstructured knowledge bases (KBs) to identify redundant and ambiguous information. It provides recommendations comprising of the following two sections: Suggesting multiple KBs that could be merged to reduce duplication and scattered data. Suggesting potentially ambiguous information across KBs. | No | Yes | Yes |
Knowledge Base | ||||
KB Gap Analysis aims at improving KB articles by adding information to existing KBs or creating new KB Articles by using information present in customer-brand conversations. | No | Yes | No | |
KB Refinement | This feature analyses any number of structured or unstructured knowledge bases (KBs) to identify redundant and ambiguous information. It provides recommendations comprising of the following two sections: Suggesting multiple KBs that could be merged to reduce duplication and scattered data. Suggesting potentially ambiguous information across KBs. | No | Yes | No |
Voice | ||||
AI Autofill | AI Autofill is an AI-powered feature designed to streamline and automate After-Call Work (ACW) in contact centers. It leverages artificial intelligence prefill call summaries in asw as disposition form ,reducing agent workload and improving data quality. | Yes, using a DP. | Indirectly. We can control the access of ACW where this feature is enabled. | |