Tellius, the AI-powered decision intelligence platform, is pleased to announce Copilot, a new set of GPT-integrated product enhancements that leverage OpenAI’s large language models. The new features automatically generate natural language synonyms, code improvements, and enhanced data stories, adding to the company’s existing AI-powered analytics capabilities to further enable every insights-driven organization to rapidly implement and scale self-service analytics.
Tellius Copilot will power the following new experiences in the platform:
- Recommended data dictionaries. With a single click, Copilot generates synonyms, descriptions, and business-friendly names for data columns. This feature enhances the industry-leading natural language search capability to deliver contextual insights to anyone in the organization.
- Code generation, improvement, and validation. When utilizing SQL/Python code to transform data and apply advanced business rules, Copilot automatically identifies errors and suggests code fixes, adds comments and explanations to code, and even generates code from scratch from natural language input. This new feature makes it easier and faster for data experts to prepare data for analysis.
- Enhanced data storytelling. In addition to the product’s AI-generated insights and best-fit visualizations, Copilot goes one step further to provide narratives that summarize key insights that are easy for users to understand. Business teams can easily interpret data analysis and make decisions with confidence.
“Applying AI to data analytics has always been in our DNA, and integrating generative AI and LLMs is another step in eliminating friction in the analytics workflow and enabling customers to take their self-service analytics and data literacy initiatives to the next level,” said Ajay Khanna, Founder and CEO of Tellius. “Copilot is a natural extension of the Tellius platform to further democratize data access and speed up the journey from data to decisions.”
While the applications of GPT has the world abuzz with its impressive ability to generation human-like text responses from a broad array of prompts, it has limitations in analytics use cases due to its inability to handle complex or multiple data sources; lack of data visualizations; inability to answer questions about an organization’s data and business without additional fine-tuning; and documented tendency to return incorrect answers, all of which could lead to wrong conclusions, bad decisions, and reduced trust from end users.
SOURCE: PR Newswire