AI Agents for Modern Finance Teams

It's a decision layer, not a pretty report. Under the hood, it's a unified financial data model + analytics engine that sits across ERP (NetSuite, SAP, Oracle), planning tools (Anaplan, Adaptive, Workday), CRM (Salesforce), and data warehouses (Snowflake, Databricks). Dashboards are just one surface. The real value is that the same logic powers ad-hoc questions, recurring variance analysis, AI agents, and executive reporting—so every team pulls from one source of truth instead of maintaining shadow spreadsheets.a

Think of it as four steps, all wired together:
‍Unify the data. Connect and harmonize ERP, planning, CRM, operational systems, and data warehouses into one financial model. No more manual extracts and Excel stitching.

Ask in plain English. FP&A analysts ask questions like "Why is EBITDA 5% below forecast this month?" The system maps that to governed metrics and runs the right joins and aggregations.

Deploy AI agents. Agents watch hundreds of KPIs 24/7. When something meaningful moves, they automatically investigate root causes, quantify business impact, and assemble the story.

Receive proactive insights & recommendations. Instead of raw charts, you get a targeted brief: "EBITDA declined 5% vs. forecast primarily due to higher operating expenses in Sales & Marketing (60% of variance) and delayed product revenue recognition (25% of variance). Estimated impact: -$2.3M. Next steps: review Q4 hiring plan and pipeline timing with revenue operations."

Conversational analytics is the front door: it lets finance teams ask better questions faster. Agentic analytics is the back office: it keeps watch when no one is asking anything. You start with conversational analytics so people can finally get consistent answers on demand. Once that foundation is solid, agents take over the boring part: continuous monitoring, driver analysis, alerting, and narrative generation. You don't need agents to get value, but without agents you'll always be one meeting behind your own data.

The best AI analytics platform for finance depends on what you're trying to solve. If you need basic visualization, stick with Tableau or Power BI. If you need planning and budgeting, use Anaplan or Adaptive. But if you need intelligent variance analysis, automated root cause investigation, proactive monitoring, and executive narrative generation—capabilities that explain why metrics changed and surface issues before you ask—you need an agentic analytics platform purpose-built for finance. That's what Tellius delivers: a unified platform that sits on top of your existing systems, understands finance semantics natively, and deploys AI agents that work 24/7 to investigate, alert, and explain.

Traditional BI tools (Tableau, Power BI, Qlik) visualize financial data—showing dashboards and charts that display what happened. AI analytics platforms like Tellius explain financial data—automatically decomposing variances into drivers, quantifying contribution, and generating plain-English narratives ready for leadership. BI is reactive (you check dashboards); AI analytics is proactive (agents monitor and alert). Most finance teams keep their BI tools for visualization and add Tellius for intelligence—using BI for "what happened" and Tellius for "why it happened and what to do about it."

Conversational analytics lets you ask questions in plain English and get instant answers with automated root cause analysis. You drive the inquiry: "Why is EBITDA 5% below forecast?" Agentic analytics means AI agents work autonomously without being asked—continuously monitoring KPIs, detecting anomalies, investigating root causes, and alerting you proactively. Conversational is human-initiated; agentic is AI-initiated. You need conversational as the foundation for democratized data access, then add agentic for continuous intelligence and early warning. Together, they shift finance from reactive reporting to proactive decision support.

Yes. Tellius is designed to complement, not replace, your existing FP&A stack. It sits on top of your planning tools (Anaplan, Adaptive, Workday, Planful), ERP systems (NetSuite, SAP, Oracle), and data warehouses (Snowflake, Databricks)—adding an intelligence layer that automates variance analysis, root cause investigation, and proactive monitoring. Your planning workflows, budget models, and forecasting processes remain unchanged. Tellius simply makes the analysis of that data faster and more automated. Many finance teams describe it as "adding AI on top of Anaplan."

Tellius provides full governance through its semantic layer—ensuring that "revenue," "EBITDA," or "headcount" are defined consistently across all users and all analyses. Every question asked, every answer delivered, and every insight surfaced by AI agents is logged with a complete audit trail showing data sources, calculations performed, and users involved. For SOX compliance or internal audit requirements, this means variance analysis is auditable and repeatable. Same question asked by two different analysts always produces the same answer using the same governed definitions—eliminating the "five analysts, five different numbers" problem.

Typical deployment takes 8-12 weeks from kickoff to first production use case. This includes data integration (connecting to ERP, planning tools, data warehouses), semantic layer configuration (defining accounts, hierarchies, business logic), and initial use case implementation (variance analysis, CFO narratives, or proactive monitoring). First value is often delivered within 4-6 weeks when starting with a focused use case like monthly variance analysis. Unlike custom-built analytics solutions that take 12-18 months, Tellius is pre-built for finance use cases and designed for rapid deployment.