Data topics

This buyer's guide compares Tellius Kaiya, Glean, Hebbia, Snowflake Cortex, and DIY RAG approaches across structured analytics, unstructured document intelligence, agent orchestration, governance, semantic understanding, explainability, and total cost of ownership. Learn where enterprise search tools excel, where warehouse-native AI fits, where custom RAG stacks create maintenance challenges, and why a dedicated agentic analytics platform may be the best choice for organizations looking to automate investigation, root-cause analysis, and decision-making across both structured and unstructured data.

AI agents are quickly moving from experimental tools to practical, high-impact solutions for enterprise data teams—and the biggest opportunity is in deploying them against real, repeatable analytical workflows. This blog outlines 14 high-value AI agent use cases that teams can implement this quarter, spanning areas like automated root cause analysis, anomaly detection, forecasting, pipeline monitoring, and marketing attribution.

Tellius introduces Kaiya Architect, an AI data modeling agent that builds governed semantic layers from raw warehouse data through a single conversation — eliminating the multi-week engineering bottleneck between business need and analysis.

Most dashboards show what changed—but not why. Learn how AI-powered root cause analysis uncovers the drivers behind business metrics in seconds, helping teams move from reactive reporting to instant, actionable insights.

AI-powered narrative generation turns variance analysis into board-ready CFO commentary in seconds instead of days. The blog shows how agentic analytics monitors performance continuously, decomposes variances, and auto-writes tailored summaries for executives, business partners, and the board—while keeping every sentence traceable to source data. Finance leaders still own the judgment and messaging, but they reclaim close-week time by automating the repetitive “what happened and why” part of reporting.

Most CPG brands lose money on the majority of their trade promotions because they don’t actually measure true incrementality or ROI—just raw lift and spend. The blog explains how AI-powered, “measurement-first” trade promotion analytics fixes this by automatically building baselines, separating real incremental volume from pantry loading and cannibalization, and running root-cause analysis on every event, not just a few. With AI agents continuously monitoring promotions, teams can catch underperforming events mid-flight, reallocate spend toward tactics that work, and move from reactive post-mortems to autonomous, in-quarter optimization.

AI agents and workflow automation are often treated as competing approaches, but the real power emerges when they work together. As agents evolve from simple chatbots to autonomous decision-makers and automation platforms become more intelligent and flexible, organizations can combine both to achieve higher reliability, faster execution, and deeper insights. This post breaks down the agent and automation spectrums, explains why Mode 1 (Execute) and Mode 2 (Explore) are both essential, and shows how their convergence enables smarter, safer, and more scalable AI-driven operations.

In today’s data-rich world, asking questions is no longer enough—what matters most is taking action. This blog introduces Tellius Agent Mode, a transformative leap into agentic analytics where AI agents don’t just answer queries—they plan, analyze, explain, and act. You’ll learn how Tellius marries a semantic layer, multi-agent orchestration, and business context to create an analytics ecosystem that moves beyond dashboards. With Agent Mode, you can automate multi-step workflows like root-cause detection, forecasting, and scenario planning—all through conversational goals. The result? Analysts elevate their focus to strategy while operational teams get trusted insights when they need them. Whether you're scaling analytics across sales, finance, or supply chain, this blog shows why Agent Mode is the architecture ready for enterprise intelligence at the speed of decision.

Dive into how organizations can safely scale multi-agent analytics without losing control or accuracy. This post outlines five essential frameworks that ensure strong governance—from semantic layers that enforce consistent metric definitions, to auditing structures that verify agent decisions, and policy controls that manage permissions and risk. Learn how these frameworks help balance autonomy & trust, reduce ambiguity, and make data analysis reliable and repeatable in enterprise contexts.

Many AI-analytics demos promise magic: impressive visuals, slick dashboards, and smooth interactions. But in real business settings, those polished demos often break down. This post pulls back the curtain and explains what it really takes to make AI analytics work at enterprise scale. You'll learn about: Building agentic architecture that supports complex queries and multi-step insights Ensuring reliability with intent parsing, root cause detection, and strong validation layers Managing data quality, security, and governance so insights can be trusted Scaling models, visualizations, and workflows to support thousands of users and billions of rows of data If you're evaluating AI analytics tools or planning your own build, this is your guide to separating real capabilities from toy demos and focusing on what drives measurable performance.

This blog dives into the practical challenges (“battle scars”) encountered while moving agentic AI analytics from demo to production. Covering real-world engineering and product lessons, it highlights pitfalls like ambiguous language, mismatched definitions, performance tail‑latency, lack of observability, and unreliable multi‑step logic. For each scar, it provides strategies—governed semantics, demander/validator separation, deterministic planning, explicit feedback loops, and transparency—that help build robust, trustworthy analytics agents at enterprise scale. If you’re rolling out AI analytics agents, these lessons can help you avoid common traps and ship with confidence.

In the age of agentic AI, analytics is no longer about isolated metrics—it’s about meaningful context. This blog explores how semantic layers act as the vital bridge between raw data and business insights, empowering AI agents to deliver accurate, traceable, and intelligent outcomes. See how well-defined business logic, trusted definitions, and knowledge graphs form the foundation for autonomous, explainable, and decision-ready AI. Discover why in a world of intelligent agents, metrics alone fall short—and why semantic evolution matters now more than ever.

For years, analytics tools promised guided insights—but often delivered static dashboards, confusing filters, and a steep learning curve. This blog explores how modern AI agents are changing the game. Unlike their predecessors, these intelligent systems understand user intent, dynamically investigate data, and return clear, actionable results conversationally. They uncover deeper “why” analysis, adapt to context, integrate seamlessly into workflows, and make insights accessible to business users without technical expertise. Discover how Tellius is ushering in a new era of empowered decision-making: fast, trusted, and built for the way we think.

As AI agents take over routine data tasks, the traditional analyst role isn’t disappearing—it’s evolving. In the “agentic era,” analysts transition from data cleaners and report builders into orchestrators who guide AI workflows, interpret results, and embed insights into business decisions. This blog explains the mechanics of agentic analytics, explores how AI handles repetitive work, and defines the new skills needed—like prompt engineering, bias review, and ethical oversight—to thrive alongside autonomous systems.

Dive into the essential ingredients for building a genuinely enterprise-ready agentic analytics platform. You’ll learn why AI agents alone aren’t enough—why convergence with a robust semantic layer, rigorous governance, scalable compute, and clear explainability matters. Explore how Tellius’s architecture weaves together conversational AI, domain-aware knowledge models, multi-agent orchestration, and trust-first design to enable autonomous analytics workflows that deliver accurate insights and measurable business outcomes at scale.

The 2025 Gartner Magic Quadrant is out—but the real story goes beyond the dots. Discover what it reveals (and misses) about the future of AI analytics and why Tellius is built for what’s next: agentic intelligence, faster answers, and smarter decisions.

LLMs made it easier to talk to machines. But the next wave of value will come from machines that think with us, not just talk to us. That’s the future of Conversational AI—systems that deliver answers, actions, and impact. And it’s already here with platforms like Tellius. If you're done with dashboards that require you to swim through lots of graphics for answer, and copilots that need hand-holding, it’s time to meet your first agent.

The next era of analytics is goal-driven, adaptive, and intelligent. Tellius brings this to life by combining deep analytics with conversational intelligence, multi-agent reasoning, and business-aware feedback loops. It’s not about replacing analysts—it’s about scaling intelligence across your business with a system that gets smarter every day.

AI analytics has evolved from dashboards and copilot tools to agentic intelligence Business users now get autonomous recommendations, not just answers Platforms like Tellius combine conversational AI, agent orchestration, and memory The result: faster insights, better decisions, and less dependency on data teams. In 2025, AI analytics isn’t a feature. It’s a fundamental shift in how decisions get made.

Tellius AI Agents and AgentComposer transform business analytics by automating complex multi-step analysis through specialized autonomous agents. Unlike generic chatbots or RPA tools, these agents leverage your enterprise data and business context to deliver deep insights across sales, marketing, finance, and manufacturing—turning questions into actions in minutes instead of days. With no-code agentic workflows, organizations can achieve 100X productivity gains and continuous, data-driven decision making.

AI agents aren’t just hype—they’re the engine behind the next generation of self-service analytics. This post breaks down how agentic AI enables multi-step, contextual analysis workflows that automate the grunt work of business intelligence. Learn what makes agentic systems different, what to look for in a true AI-native platform, and how Tellius is pioneering this transformation.

TLDR: agents on the rise, conversational everything, governance & data quality = king, driving/demonstrating ROI, the power of people never gets old.

Key learnings from Novo Nordisk to help you along your journey to unlocking business value with AI in the commercial pharmaceutical space.

Let's explore benefits of HR analytics, the challenges of traditional approaches, and how AI and augmented analytics are revolutionizing every part of people management— planning, recruiting, onboarding, management, upskilling, and beyond.