What's New

Hello, everyone. I'm Ajay from Tellius. It is a crazy time in AI right now. Frontier models continue to push boundaries. GenAI powered conversational interfaces are everywhere, and AI agents are a hot topic of discussion in the enterprise. With all the excitement, I keep hearing one fundamental problem from data and business leaders. It's that enterprise data and knowledge remains stubbornly siloed, and no amount of chat with your enterprise data seem to be able to really help business teams get what they want, which is number one, useful context aware answers from enterprise data. Number two, valuable why it happened type root cause analysis. And number three, a way to put insights into action easily. Until now, I'm super, super excited to announce the availability of Agent Mode in Tellilus, the leading agentic analytics platform. So think of agent mode as ChatGPT for enterprise data analysis and deep research mode for multistep causal analysis and forecasting, and intelligent workflow automation, putting all these insights into action built on your own data and aware of your business. Agent mode turns Kaya, our conversational AI platform, platform, into a twenty four seven AI analyst capable of the most far reaching analytical tasks. So let's see this in action. Let's say you are a supply chain analyst and you want to forecast the impact of shorter delivery times on cancellation rates to decide if the juice is worth the squeeze. So Kaiya plans and turns multistep analysis and uses contextual intelligence to surface precise explainable results in seconds. As you can see, Kaiya gets at the Question behind the question because it's connected to your data and metadata. It's domain tuned to understand your business, and it's equipped with all the necessary tools to generate relevant answers and insights. All of this is powered by our semantic layer, contextual intelligence system, deterministic analytics engine, and robust multi agent architecture. Okay. Let's try something a little harder and in a totally different industry. Say you are a market access analyst at a major pharma company, and you want to see if the favorable access levels you have negotiated for your brand are actually driving pull through. Once again, our agentic planner understands the intent of the question, routes it to the correct underlying data sources, breaks the query down in the appropriate pieces, develops what if analysis, and comes up with the recommended next steps. So technically speaking, we've just performed a descriptive plus diagnostic plus temporal analysis successfully executing on a sophisticated predictive model that delivers actionable insights. Now try that with a vanilla LLM or a traditional BI tool and see what you come up with. These examples just scratch the surface of many, many analytical use cases of agent mode Intel. Yes. And demonstrate how this agentic approach democratizes deep, reliable insights while making analysis repeatable through governed workflows. We can't wait to see what you all will uncover now that analysis is no longer a barrier.