Reason across every data source
structured, unstructured, and syndicated sources.
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Snowflake
Salesforce
Veeva
++
IQVIA
Nielsen
MMIT
++
Payer contracts
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Board deck
Gong
++
Fragmented data and context
are why your AI efforts stall
"What" and "why" live apart
Your warehouse holds the metrics — the what happened. Your contracts, call notes, transcripts, and retailer commentary hold the why. Connecting them today is manual and unscalable.
Generic LLMs don't know your business
NBRx, gross-to-net, "active customer" — your team defines them one way, the model assumes another, and you spend the prompt explaining the difference. Re-explaining your business in every prompt is a bottleneck.
Rebuilding understanding from scratch sucks
Memory is fragmented and investigation methods live in people's heads, so every answer arrives without the benefit of the thousand answers before it.
Connect. Model. Sharpen.
Every source. Structured, unstructured, syndicated
— queried where it lives.
Native connectors to Snowflake, Databricks, Redshift, BigQuery, and your operational systems — plus first-class ingestion of PDFs, call notes, transcripts, and management commentary. Queries push down to your warehouse and run at cloud scale: no extracts, no second copy, no data egress. Everything joins the same intelligence layer.
Snowflake · brand_warehouse
Connected · 2 min ago
Struct
IQVIA Xponent · Q3 refresh
Connected · 2 min ago
Synd
Veeva CRM · call_activity
Streaming · live
Synd
payer_contracts_Q3.pdf
Processed · 14 min ago
Unstr
Nielsen Scan · weekly
Connected · 1 hr ago
Synd
MSL_call_notes_NOV.docx
Processed · 3 hrs ago
4.2M rows scanned · 1.24s
Unstr
Analysis-ready data in one conversation.
Import your semantic layer or simply describe what you want in plain language. Architect connects to your warehouse, discovers tables, infers joins, creates industry-specific metrics, and publishes a validated, governed Business View — with full SQL and Python escape hatches when you want them. Define your business meaning once, and every Mission, App, and Kaiya conversation works from the same model.
Models & joins
Metrics & KPIs
Data-explosion checks
De-dup
Version control
dbt YAML
Kaiya Architect
Connected to Snowflake
# Brand Performance Business View business_view: name: brand_performance_bv schema: PHARMA_ANALYTICS refresh: weekly tables: - name: iqvia_claims type: fact grain: weekly - name: hcp_roster type: dimension - name: territory_master type: dimension joins: - from: iqvia_claims.npi to: hcp_roster.npi_number cardinality: N:1 join_type: LEFT measures: - name: market_share formula: brand_trx / total_market_trx * 100
Consistent, accurate answers. Sharper every day.
Business Views map your concepts — NBRx, gross-to-net, lift, pipeline coverage — to your physical data, once. Every output traces back through them. And Memory compounds: validated patterns, recurring drivers, what worked. The intelligence layer gets smarter every time it runs.
"Why did margin slip 80bps this close?"
Kaiya is reasoning
Applying 4 Business Views · checking Memory
Resolved gross_to_net Business View v3
Applied product mix definition CFO-approved
Cross-checked with Memory 142 prior
Reranking drivers by validated impact
Memory: sharpening since Mar 2024 · 1,247 investigations applied
Your data stays put. Every answer traces back.
01
Stays in place
No migration
It never leaves your perimeter
Connect Snowflake, Databricks, your apps and your documents where they already live. Live Mode pushes the query down to your warehouse — no extracts, no second copy.
Snowflake
Salesforce
Veeva
Documents
++
0 bytes leave your VPC
02
Unified once
One model
One model, one source of truth
Structured and unstructured map into one semantic layer. Your definitions — NBRx, gross-to-net, lift — set once, so the same question returns the same answer across every team and tool.
Semantic layer
1 governed model
03
Traceable
Audit-ready
Every answer traces back
Each output traces through the exact logic that produced it — right down to the source rows. Reproducible and audit-ready by design.
Source
→
Dataset
→
Business View
→
Answer
Reproducible · audit-ready
See it on your data
Watch the layer read
across your stack. Live.
30 minutes. Your data. Our engineers. Live, end to end.
On your live data
Real, governed output
Traceable by design
Your data. Your perimeter.
Your governance.
Ask Across All Your Data
0 bytes egress. Pushdown to Snowflake, Databricks, Redshift, BigQuery. Data never leaves your perimeter.Row + column governance
Enforced at the semantic layer, not the dashboard. One policy, every surface.
Every query traced
Full audit log with Business View attribution. Every output reproducible to its source.
SOC 2 Type II
Audited annually. Continuous controls monitoring across security, availability, confidentiality.
HIPAA + GDPR ready
Deployed in regulated pharma and financial environments since 2018. BAA available.
HIPAA + GDPR ready
SSO · SAML 2.0 · SCIM · RBAC. Okta, Azure AD, Ping — all native.
SOC 2 Type II
HIPAA
GDPR
AI Native since 2016
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.
How Agent Debugger Audits a Kaiya Analysis End to End
See how Kaiya’s Debug Agent provides an independent second opinion on AI-generated analysis by auditing the full analytical trace—not just the final answer. It reviews the data model, semantic layer, SQL and logic, skills, summaries, and recommendations to uncover errors that can otherwise look convincing. When it finds a problem, it determines whether to fix the individual analysis, the underlying skill, or the data model—helping enterprises make AI analytics more transparent, explainable, and trustworthy.
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How Agios Pharmaceuticals Runs AI-Native Commercial Analytics at Launch Scale
See how Agios Pharmaceuticals uses AI-native commercial analytics to support launch-scale decision-making. This blog explores how the team brings data and AI into commercial workflows to accelerate insights, investigate performance drivers, and give teams faster answers as launch complexity grows.
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Manufacturing variance analysis: how to recover lost margin
This guide explains how manufacturers can use variance analysis to identify where margin is being lost and why. It covers common cost, price, volume, mix, and operational drivers, and shows how AI-powered analytics can automate root cause analysis, surface hidden performance issues, and help finance and operations teams act faster to protect profitability.
AI-Native Analytics at Launch Scale | PMSA Agios Full Webinar
Watch the Agios Pharmaceuticals PMSA webinar and see how AI-native analytics is helping commercial teams accelerate insights at launch scale. The discussion explores how AI can streamline analysis, uncover performance drivers, and help pharma teams make faster, data-driven decisions.
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.
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.
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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.