Tellius 6.1: AI Agents Across Every Metric, Document, and Conversation

Your enterprise data lives in two worlds: structured metrics in dashboards, reports, and spreadsheets, and everything else — contracts, call transcripts, research reports, payer policies — buried in documents. No connection between them. Tellius 6.1 changes that. See how Kaiya orchestrates a team of AI agents — some reason over your metrics, others over your documents and knowledge base — to deliver analytically accurate answers grounded in the full picture.
‍
This isn't a feature walkthrough. It's a fundamentally different way to work with enterprise data. Whether your team runs pharma commercial analytics, CPG category management, revenue operations, or FP&A — watch how AI agents that understand both your numbers and your documents turn questions you never thought to ask — or previously were impossible to ask — into decisions you can act on.

 What You’ll See:

⭐ Beyond Rows & Columns — Unstructured Data Analysis
‍Kaiya's AI agents now analyze PDFs, call transcripts, research reports, and policy documents alongside your structured data. Connect Google Drive, S3, Azure Blob, Gong, and more. Ask "What objections came up most in deals we lost?" and agents analyze 200+ calls, rank patterns, and cite sources — with document-level citations on every answer.

⭐ Contextual Memory — Agents That Know Your Business
‍Kaiya retains business context across turns and sessions. Ask "Did Thanksgiving impact TRx for Product X?" and Kaiya looks up the timing, applies the right comparison window, and keeps that context for follow-ups. No manual date lookups. No starting cold every session.

⭐ Just Talk — Voice-First Analytics
‍Speak naturally, change direction mid-sentence, and Kaiya keeps up. Voice mode handles multi-turn conversations with real speech patterns — pauses, corrections, mid-thought pivots — not just transcription. Field reps get pre-meeting answers without typing.

⭐ Smarter Agent Orchestration
‍Kaiya auto-routes every question to the right analysis path — SQL, Python, or Deep Insights — while a Reflection agent checks outputs for accuracy before delivering results. Your team asks questions. The agents figure out the rest.

‍

‍

‍