Conversational Analytics

Talk to your data.
Get finished work back.

Q: How is conversational analytics different from a BI dashboard or an AI chatbot?

A dashboard hands you a number and leaves the why to you. A chatbot guesses at your metrics and can hallucinate the math. Tellius does the whole job: ask in plain language and Kaiya plans the investigation, computes it on Tellius's deterministic reasoning engine, and returns finished work — drivers, next steps, even a live app or a 24/7 mission — all traced to your governed data.

Q: How does Tellius keep numbers accurate and avoid hallucinated answers?

The LLM handles language, and Tellius's deterministic reasoning engine handles the math — so it never guesses on schema or hallucinates on numbers, and it sidesteps the chasm and fan traps that silently double-count. Every metric resolves to one governed definition in your semantic layer, so the same question returns the same answer every time — reproducible and traceable to its source.

Q: Is my data secure, and does Tellius train on it?

No — Tellius never trains on your data. It runs inside the security model you already have, on your LLM of choice (OpenAI, Azure OpenAI, Gemini, or native Vertex AI), with encryption in transit and at rest, row-level security and fine-grained access control, SOC 2 Type II, HIPAA, and customer-managed keys. Host it in the cloud or in your own single-tenant VPC.

Q: What can Kaiya actually do?

Far more. Kaiya can run a deep investigation (Deep Insights); build a governed data model — adding metadata, and enriching, cleaning, and prepping your data (Kaiya Architect); turn any analysis into a live, interactive app your team can just open to get refreshed data (Kaiya Apps); or schedule a mission that re-investigates on its own and delivers a finished brief to your inbox or Slack (Kaiya Missions).

You talk to Kaiya, and it understands what you mean — and turns it into a deep investigation, a live app, a 24/7 mission, or a data model.

kaiya · deep insightsLive

Conversation

Why did unit sales drop in the West last month? Rank the drivers.

Auto-detected Business View: Retail Sales. Planned a 5-step investigation — SQL for the numbers, Python for the variance.

Top driver: out-of-stocks at three store clusters — 38% of the gap.

Auto detect BV

Live investigation

West Region — Unit Sales Driver Brief

Deep InsightsSQL + Python

−9.2%

units vs. prior mo.

▼ material

clusters out-of-stock

−38% of gap

+2.4%

store traffic

▲ not the cause

StockPromoPriceComp

kaiya · missions

Conversation

Track state-wise profit every week — total profit, margin, rankings, and the top and bottom performers.

Generated a 3-step plan for state-wise profit: SQL → Python → Summary. Preview it on the right.

Generate the mission summary and deliver it to my inbox at 9 a.m. every Monday.

Scheduled. The finished brief lands in your inbox and Slack every Monday at 9 a.m. — re-run on the latest data each time.

Describe your mission or refine the plan…

Mission StepsSummary OutlineDiagram

Analysis Steps (3)

1SQLAggregate total profit, revenue, discount & profit margin by state→ state_profit

2PythonEnrich with profit share, ranking & performance tiers→ state_profit_analysis

3SummarySummarize state-wise profit with charts & actionable insights

Save Plan and Continue

kaiya · appsLive

Conversation

Turn this into an app for regional managers. Rank categories by revenue, and add a card that flips to reveal the driver behind each number on hover.

App generated — Product & Category Performance, bound to your governed Business View.

Added hover-to-flip cards. Ask me to add a chart, KPI, or section anytime.

Ask a question about your app…

PlanApp Remix

Product Analytics

OverviewCategory PerformanceTier AnalysisMonthly TrendsRecommendations

Top: Furniture$1.52M Total

$531K

Furniture · 34.9%

+18% units MoM · AOV $2,417 · new store cluster driving it

$514K

Electronics · 33.8%

−4% margin · promo-led · watch discount depth

$477K

Office · 31.3%

steady · highest repeat rate in the catalog at 61%

↻ hover a card to flip it and see the driver behind the number

kaiya architect · retail_business_viewRedshift · Live

Conversation

Which key should I use to join returns, inventory, and shipping to your orders table?

Use date_id — it's the most reliable shared key across all three.

Done — joined all three on date_id (left outer); the fan-trap risk is handled. Here's the final plan. Approve to build?

Answer the question above to continue…

PlanDiagramYAMLData Sources

✓ APPROVED

Retail Business View Plan

Combine 4 fact + 7 dimension tables from Redshift for retail analytics across orders, returns, inventory & shipping. Sources: 11 tables, all live.

Left	Right	Key	Cov	Note
fact_orders_rds	dim_customer_rds	customer_id	100%	inner · Strong
fact_orders_rds	fact_returns_rds	date_id	64%	left · Fan trap
fact_orders_rds	fact_shipping_rds	date_id	61%	left · Fan trap

Then: 9 validation checks → YAML → dry-run → publish

See Tellius in Action

Tellius delivers conversational analytics for the enterprise: Kaiya auto-detects the right Business View, plans a multi-step investigation, runs the math on Tellius's deterministic reasoning engine, and returns an answer that traces to its source. Chat-based BI copilots give you the what but never the why — ungrounded, missing your business context. Tellius reasons across your governed semantic layer to get to the why, consistent and reproducible every time.

Trusted by the world’s most innovative teams

The Problem

Chat-based AI fails on the questions that actually matter

A copilot can answer "what were sales last week." It falls apart the moment the question has stakes — because it doesn't know your business, and it guesses on the math.

The same metric means three different things

"Break sales performance down by region, rep, and product line for Q3 versus Q2, and flag where we're falling behind."

Semantic layer

Kaiya uses the definition your team set for the metric in the semantic layer, so every answer matches.

You can't afford wrong math

"What's our true profit across regions, channels, and product lines after returns and discounts?"

Deterministic reasoning

Text-to-SQL silently double-counts on chasm and fan traps. Tellius computes it exactly — no hallucinated numbers.

The questions that matter have many layers

"Break the revenue miss down by region and segment, and tell me which accounts are about to churn."

Multi-step investigation

One-shot prompts answer one thing. Kaiya runs it as a single multi-step, multi-agent investigation and returns every layer connected in one answer.

Your data lives across many systems

"How is demand shifting across our warehouse data, field call notes, and contract terms this quarter?"

Detects the right data

Copilots see one source. Kaiya reasons across warehouses, documents and call notes — and auto-selects the right data for each question.

A general AI doesn't know your world

"How's my top product tracking in my region against the national trend, and which accounts are driving it?"

Grounded ontology

Kaiya is grounded in your ontology and definitions, so "my region" resolves to yours — precisely personalized to each person.

You shouldn't have to spell out every step

"Why did we miss plan last month, what were the top drivers, and what should we do next?"

Autonomous analysis

Kaiya figures out which investigation to run on its own — and hands back the summary, key drivers, next steps, follow-up questions, and charts.

One Conversation

Conversation isn't a feature.
It's the front door.

Investigate

Kaiya Deep Insights

You"Why did unit sales drop in the West last month, which stores drove most of it, and is it price or availability?"

Kaiya

Auto-detects the right Business View, then plans a multi-step investigation on its own
Runs SQL for the numbers, Python for variance and change-point work, and ranks the drivers
Returns a governed narrative with the next move — and re-maps to inventory or promo in the same thread

Explore Kaiya Deep Insights

Any data

Kaiya Architect

You"Build a Business View from these 8 tables for the sales-ops role, and add the metrics behind our 15 weekly questions."

Kaiya

Five-phase build — Intent → Tables → Joins → Columns → Metadata — you approve at each step
Runs nine validation checks: chasm and fan traps, double-counting, join-key validity, and more
Finds a problem, proposes the fix, and re-validates on command — no analyst who knows every join

Explore Kaiya Architect

Build an app

Kaiya Apps

You"Turn this into an app for regional managers, show only the top 5 categories by sales, and add a flip card that reveals the driver behind each number on hover."

Kaiya

Generates a live, interactive app — not a dashboard — layout, KPIs, charts, and filters from the conversation
Every number binds to your governed Business View; ask for a chart or KPI and each edit re-queries in place
Every change is versioned and stays in draft until you publish

Explore Kaiya Apps

Automate

Kaiya Missions

You"Every Monday, track regional sales pacing vs plan, flag every category more than 10% behind, explain why, and send the brief to Slack."

Kaiya

Runs the whole investigation on a schedule against fresh data — root cause, variance, anomaly — every run
Ships a finished PPTX, PDF, or DOCX to your inbox or Slack
A scheduled report re-prints the same numbers; a Mission re-investigates and tells you what changed

Explore Kaiya Missions

How It Works

Describe what you want.
Watch it become finished work.

1Plan

Kaiya turns your request into a plan you can see before anything runs.

Investigation steps, an app blueprint, a data-model build, or a mission schedule
You review, adjust by chatting, and approve — nothing executes until you say go

2Reason

It does the real work on the deterministic engine.

Runs the analysis, builds the layout, or validates the model
Grounded in your definitions, so the math ties out

3Deliver

It hands back finished work, not a to-do list.

A brief, a live app, a scheduled deck, or a Slack summary
Routed to the right person, where they already work

Kaiya Everywhere

Ask Kaiya wherever you work

Slack

@Kaiya what were our top 3 regions last quarter?

West, Northeast, South. West leads at 34% of revenue — want the drivers?

Slack

Mention @Kaiya in any channel — it picks the right data and answers in place.

Teams

@Kaiya how are we pacing vs plan this week?

+2.4% ahead. Two categories are behind — I can break them down.

Microsoft Teams

A dedicated Kaiya channel in Teams. Ask where your team already collaborates.

app.yourcompany.com

Kaiya

Analyze the two charts on this page and tell me what changed.

Ask about this page…

Your browser

Open the Kaiya browser extension on any page and ask, without leaving your work.

Your App

$4.2M

Revenue

+8.1%

vs plan

Kaiya

Ask about this dashboard…

Embedded in your tools

Embed Kaiya into your own apps, tools, portals, and dashboards.

Governed By Design

Not just the answer — the whole thought process behind it

See how it got there

The full thought process is shown step by step — the plan, the methods, the exact SQL and Python that ran — with every result traced back to the source Business View and data source.

Governed at the semantic layer

One row- and column-level policy, defined once on the semantic layer and enforced on every surface — chat, apps, missions, and embeds alike. Every metric resolves to one governed definition.

Your LLM, your perimeter

Kaiya runs on your LLM of choice — inside the security model you already have. No training on your data.

SOC 2 Type IIHIPAAGDPRSSO / SAML / SCIM / RBACCustomer-managed keys

Value On Day 1

From login to a live conversation in ten minutes

1Connect a source

2Build a Business View — by talking to Kaiya Architect

3Ask your first question, then turn it into an app or a mission

FAQ

Questions teams ask before they switch

How is conversational analytics different from a BI dashboard or an AI chatbot?

A dashboard hands you a number and leaves the "why" to you. A chatbot guesses at your metrics and can hallucinate the math. Tellius does the whole job: ask in plain language and Kaiya plans the investigation, computes it on Tellius's deterministic reasoning engine, and returns finished work — drivers, next steps, even a live app or a 24/7 mission — all traced to your governed data.

How does Tellius keep numbers accurate and avoid hallucinated answers?

The LLM handles language, and Tellius's deterministic reasoning engine handles the math — so it never guesses on schema or hallucinates on numbers, and it sidesteps the chasm and fan traps that silently double-count. Every metric resolves to one governed definition in your semantic layer, so the same question returns the same answer every time — reproducible and traceable to its source.

Where can I use Kaiya?

Wherever you already work. In addition to Tellius itself, you can integrate Kaiya into Slack and Teams — mention Kaiya in any channel and it answers in place. Open the browser extension on any page and ask, or embed Kaiya inside your own apps, portals, and dashboards. It's the same governed Kaiya on every surface — same definitions, same security, same finished work.

Is my data secure, and does Tellius train on it?

No — Tellius never trains on your data. It runs inside the security model you already have, on your LLM of choice (OpenAI, Azure OpenAI, Gemini, or native Vertex AI), with encryption in transit and at rest, row-level security and fine-grained access control, SOC 2 Type II, HIPAA, and customer-managed keys. Host it in the cloud or in your own single-tenant VPC.

What can Kaiya actually do?

Far more. Kaiya can run a deep investigation (Deep Insights); build a governed data model — adding metadata, and enriching, cleaning, and prepping your data (Kaiya Architect); turn any analysis into a live, interactive app your team can just open to get refreshed data (Kaiya Apps); or schedule a mission that re-investigates on its own and delivers a finished brief to your inbox or Slack (Kaiya Missions).

Does Tellius work with my existing data warehouse and BI tools?

Yes. Tellius connects to Snowflake, Databricks, Redshift, BigQuery, Azure Synapse, and 30+ more connectors, and can query your warehouse live without moving the data. It works with both your structured and unstructured data — so beyond your warehouses, it can also query your PDFs, call transcripts, and policy documents. It's platform- and LLM-agnostic, so you keep your warehouse, keep your BI tools, and keep your LLM of choice.

Get Started

See what finished work looks like on your data.

Ask a real question and watch Kaiya return a governed, reproducible answer — traced to its source.

See Tellius in Action Start for Free

DISCOVER MORE

Breakthrough Ideas, Right at Your Fingertips

Dig into our latest guides, webinars, whitepapers, and best practices that help you leverage data for tangible, scalable results.

Agentic AI in Supply Chain: Use Cases, Platforms, and What's Shipping (2026)

Supply chain leaders are moving beyond predictive dashboards and generative AI toward agentic systems that can monitor operations, investigate disruptions, recommend actions, and orchestrate workflows across the enterprise. This guide explores the state of agentic AI in supply chain management in 2026, comparing the leading platforms and highlighting the use cases that are delivering measurable business value today. Learn how organizations are deploying AI agents for demand sensing, inventory optimization, supplier risk monitoring, procurement intelligence, logistics planning, production scheduling, transportation optimization, and exception management.

Best FP&A Software in 2026: The Tools That Plan, and the Layer That Explains Why

Modern FP&A teams need more than planning software—they need the ability to explain financial performance, identify the drivers behind change, and act faster on emerging risks and opportunities. This buyer's guide compares the leading FP&A platforms in 2026 across budgeting, forecasting, scenario modeling, financial consolidation, reporting, AI-assisted planning, and enterprise analytics. It also introduces a new evaluation framework: distinguishing the systems that plan from the intelligence layer that explains why.

Best Pharma Launch Analytics Platforms in 2026: What Each One Does Best

Launching a new therapy has never been more data-intensive—or more complex. Commercial teams must coordinate brand strategy, field execution, HCP engagement, market access, patient adoption, omnichannel performance, and competitive intelligence while making decisions in near real time. This buyer's guide compares the leading pharma launch analytics platforms in 2026, evaluating what each solution does best across launch planning, forecasting, HCP targeting, territory performance, market access analytics, omnichannel measurement, patient journey insights, and AI-powered commercial intelligence.

Tellius 6.3: Kaiya Everywhere, Grounded in Your Business

AI answers are only useful when they're grounded in your business and available where decisions actually happen. See how Tellius 6.3 delivers fast, traceable insights across pharma, CPG, RevOps, and FP&A — right in the tools your team already uses.

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

Tellius 6.0: Agent Mode for Deep Analytics + Insights

PMSA Fall Symposium 2025 in Boston

Join Tellius at PMSA Oct 2–3 for two can’t-miss sessions: Regeneron on how they’re scaling GenAI across the pharma brand lifecycle, and a hands-on workshop on AI Agents for sales, HCP targeting, and access wins. Discover how AI-powered analytics drives commercial success.

Tellius AI Agents: Driving Real Analysis, Action, + Enterprise Intelligence

Tellius AI Agents transform business intelligence with dedicated AI squads that automate complex analysis workflows without coding. Join our April 17th webinar to discover how these agents can 100x enterprise productivity by turning questions into actionable insights, adapting to your unique business processes, and driving decisions with trustworthy, explainable intelligence.

View All Resources

Talk to your data.Get finished work back.

Chat-based AI fails on the questions that actually matter

Conversation isn't a feature.It's the front door.

Describe what you want.Watch it become finished work.

Ask Kaiya wherever you work

Slack

Microsoft Teams

Your browser

Embedded in your tools

Not just the answer — the whole thought process behind it

From login to a live conversation in ten minutes

Questions teams ask before they switch

See what finished work looks like on your data.

Breakthrough Ideas, Right at Your Fingertips

Agentic AI in Supply Chain: Use Cases, Platforms, and What's Shipping (2026)

Best FP&A Software in 2026: The Tools That Plan, and the Layer That Explains Why

Best Pharma Launch Analytics Platforms in 2026: What Each One Does Best

Tellius 6.3: Kaiya Everywhere, Grounded in Your Business

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

Tellius 6.0: Agent Mode for Deep Analytics + Insights

PMSA Fall Symposium 2025 in Boston

Tellius AI Agents: Driving Real Analysis, Action, + Enterprise Intelligence

Talk to your data.
Get finished work back.

Conversation isn't a feature.
It's the front door.

Describe what you want.
Watch it become finished work.