Reason across every data source

Connect, model, and get deep insights across
structured, unstructured, and syndicated sources.
Structured Data
warehouses · apps · CRM
Snowflake Salesforce Veeva ++
Syndicated Data
third-party panels & feeds
IQVIA Nielsen MMIT ++
Unstructured
docs · notes · transcripts
Payer contracts Board deck Gong ++
Kaiya
Architect
Describe it in plain language — Architect builds the model: schema, metrics, semantics, ontologies, and business context.
Minutes, not weeks
Intelligence Layer
Your data — ready for any question.
ALWAYS-ON · GOVERNED · TRACED
Ask in plain language
Which segment is dragging coverage?
Answers across Pharma CPG FP&A RevOps
Outcomes
Finished briefs
Missions run on a schedule, deliver polished investigations.
Governed apps
Build analytic apps your team can trust.
Traced answers
Plain-language Q&A — every answer cites its source.
Trusted by the world’s most innovative teams
The Gap

Fragmented data and context
are why your AI efforts stall

Your data doesn't connect to your business meaning, so your intelligence never compounds.
Two worlds

"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.

Prompt tax

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.

Enterprise amnesia

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.

Capabilities

Connect. Model. Sharpen.

Connect every source at cloud scale. Model it once in your language. Sharpen it with every answer.
01· Connect · Pushdown

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

02· AI Prep · Kaiya Architect

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
Active Session
Build a Brand Performance Business View for pharmaceutical analytics with TRx, NBRx by territory, market share analysis, and field force effectiveness metrics.
Discovery complete. Identified the key metrics: prescription volume (TRx/NBRx), territory-level market share, and sales-rep performance indicators. Connected to Snowflake
Schema scan complete. Found 6 relevant tables in PHARMA_ANALYTICS:
IQVIA_CLAIMS 8.2MHCP_ROSTER 45KTERRITORY_MASTER 890CALL_ACTIVITY 1.2MFORMULARY_STATUS 12KPRODUCT_HIERARCHY 340
Data enrichment & preparation.
Grain alignment: daily calls aggregated to weekly
Deduplication: removed 1,247 duplicate claim entries
Null handling: 342 NPI values → fuzzy match on HCP name
Derived columns: fiscal_quarter, rolling_4wk_avg, yoy_growth_pct
Objective
Brand Performance Business View — prescription tracking, market-share analysis, and field-force metrics.
Data sources
IQVIA_CLAIMS — prescription transactions 8.2M · weekly
HCP_ROSTER — provider master 45K rows
TERRITORY_MASTER — geographic hierarchy 890 terr.
CALL_ACTIVITY — sales-rep interactions 1.2M rows
FORMULARY_STATUS — payer coverage 12K rows
PRODUCT_HIERARCHY — product master 340 prod.
Join strategy
IQVIA_CLAIMS.npi → HCP_ROSTER.npi_number N:1 LEFT
HCP_ROSTER.territory_id → TERRITORY.id N:1 LEFT
IQVIA_CLAIMS.product_id → PRODUCT.id N:1 INNER
Calculated metrics
market_share = brand_trx / total_market_trx * 100
nbrx_ratio = nbrx_units / total_rx * 100
call_effectiveness = conversions / total_calls * 100
HCP_ROSTER
npi_number PK
specialty VARCHAR
TERRITORY
territory_id PK
region VARCHAR
IQVIA_CLAIMS
npi VARCHAR
product_id INT
trx_units INT
PRODUCT
product_id PK
brand VARCHAR
FactDimension
# 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
03· Semantic Layer · Business Views · Memory

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.

Learn more
Semantic Layer · Live

"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

How It Works

Your data stays put. Every answer traces back.

Connect a source and three things are true from day one — it stays where it lives, it's unified into one governed model, and everything it produces traces straight back to it. No migration, no second copy, no black box.

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

Governance & Security

Your data. Your perimeter.
Your governance.

Tellius runs inside the security model you already have. The warehouse, the IdP, the audit trail — all yours. We connect the layer that reads across them.
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

DISCOVER MORE

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