Introduction to AI Studio

Sprinklr AI Studio is a powerful tool that allows Sprinklr Insights customers to create customized AI text classification models without requiring any coding or engineering expertise. This means that businesses can leverage the power of AI to accurately filter and categorize customer feedback, comments, and messages, without needing to rely on specialized technical staff.

With Sprinklr AI Studio, businesses can fine-tune their filters to ensure that they receive actionable mentions and messages, providing valuable insights that can be applied across the entire enterprise. This includes various departments such as sales, marketing, and customer care, allowing businesses to make informed decisions and take appropriate actions based on the information gathered.

AI Studio empowers businesses to leverage the benefits of AI without requiring specialized technical knowledge, which can improve the accuracy and efficiency of their data analysis, leading to better decision-making and overall performance.

Common use cases to use custom AI Models

Sprinklr’s AI Studio can be used to solve various business problems. Following are several common use cases where AI Studio can be effectively utilised to deliver insights and enhance decision-making capabilities.

Brand Disambiguation

To identify relevant messages that mention the brand, as opposed to messages that do not (e.g. Brand Message v/s Non-brand Messages).

The table provided contains messages that mention the word 'apple', but in different contexts. In order to distinguish whether the message is referring to the brand Apple or to the fruit, a classification model is needed. With AI Studio, it is possible to train and deploy a model to classify such messages at scale.

By providing AI Studio with a dataset of similar messages, we can train the model to accurately classify messages based on their context. Once the model is trained, it can be deployed to classify new messages in real-time, enabling us to process large volumes of messages quickly and efficiently. This can be particularly useful in applications such as social media monitoring, where large amounts of data are generated on a regular basis and need to be processed in real-time. With a classification model in place, we can streamline this process and gain valuable insights into consumer sentiment and behavior towards the brand.

Other use cases

• Spam Detection: To identify spam messages and distinguish them from non-spam messages.

• Custom Spam Categories: To further categorize spam messages according to a specific criterion (e.g. Random URL, Indeterminable Foreign Language, Political Commentary, Spam Usernames, External Advertising, Destructive Criticism).

• Custom Sentiment Model: To build or validate a custom sentiment model that can analyse and classify the sentiment of messages into custom categories such as Positive, Neutral, Negative, or Mixed.

• Influencer Discovery/Brand Advocacy: To identify messages that are either promoting or detracting from the brand (e.g. Detractors messages, Passive messages, Promoters messages)

• Brand Reputation Management: To monitor and identify potential threats to the brand's reputation, such as Data Leakage, Security Breach, Cyber Threats, Bans & Boycott, Racial, Cultural, Gender, & Ethnicity Bias, Political Affiliations, and other factors.

Top features of AI Studio

Following are the top features provided by Sprinklr’s AI Studio –

• AI Studio enables creating of both new custom models as well as validation Sprinklr’s existing models, including Text Classifier, Product Insights, and Sentiment.

• AI Studio supports an extensive range of over 90 languages, which can be found in the supported language list.

• AI Studio's custom fields and dimensions can be utilized in a listening dashboard to gain immediate insights into related AI project data.

• AI Studio's First Party Data Ingestion (FPDI) feature allows for reimporting previously trained models hosted outside of Sprinklr, without the need for redoing the tagging work.

• Validation Reports: AI Studio generates Standard Validation reports to provide a summary of validation-related data points for the user or admin.

• Golden Dataset & Accuracy Metrics: AI Studio's Golden Dataset feature enables the user to create or import their own baseline datasets against which accuracy metrics such as Precision, Recall, and F1 Score can be calculated.

• Notifications and Alerts: AI Studio offers in-platform and email notifications to alert users of project status, such as whether it is processing, failed, or completed. This feature saves time by avoiding the need for users to frequently access the platform to check the status of their projects.