Commerce data platform

Ecommerce dataready forAI and agents.

Triple Whale turns fragmented commerce signals into one governed, real-time data foundation: from proprietary fetchers and canonical schemas to SQL, dashboards, Moby and agentic work.

How the data works

$100B+ Client GMV represented annually

Real-time Attribution as commerce signals arrive

Commerce sources

SSHOPIFY

MMETA

GGOOGLE

KKLAVIYO

AAMAZON

Proprietary integration system

Sensory fetchers

Unified canonical schema

Normalize every source.

orderscanonical

customerspersistent

marketingcross-platform

attributionexplanatory

Saber organization layer

Commerce data anthology.

orders products sessions journeys attribution

SQL

Dashboards

Moby

Agents

$100B+ Annual client GMV running through the intelligence layer

One schema First-party and third-party commerce data standardized for analysis

Many surfaces Dashboards, SQL, APIs, Moby and agents on the same governed foundation

The Triple Whale platform

From raw signal to usable intelligence.

A complete commerce data stack, already connected: ingest the ecosystem, organize the meaning, query the truth and activate the result.

01 / SENSORY

Bring the commerce ecosystem into one current.

Sensory is Triple Whale’s proprietary integration system. It fetches data from first-party and third-party sources, keeps the connections moving and gives the rest of the platform a consistent starting point.

SourcesTriple Pixel, post-purchase survey, seller inputs and connected platforms

CoverageStorefront, ads, lifecycle, marketplace, fulfillment and more

PurposeRemove integration plumbing from the path to analysis

Sensory / live ingest Waiting for source records

Shopifyorder

#10482 · $184.00

received

Metaclick

fbclid / campaign 218

received

Klaviyoprofile

customer_718 · known

received

Triple Pixelsession

3 touches · 12m journey

received

01 collect 02 validate 03 match 04 write

Canonical orderorders.order_id

order_id: #10482
customer_id: customer_718
revenue: $184.00
journey: 3 matched touches

02 / SABER

Give every table a business meaning.

Triple Whale standardizes diverse source schemas into one canonical model. Saber organizes that model into a commerce data anthology: shared language, explicit object boundaries and a dictionary that tells every human and agent what the data means.

ObjectsOrders, customers, products, marketing entities, sessions and journeys

ClarityCanonical truth stays distinct from modeled and explanatory layers

OutcomeFewer category errors, faster analysis and agent-ready context

Saber / schema map Raw fields loaded

Raw fields

shopify.customer.emailstring

shopify.order.idstring

meta.campaign_idstring

pixel.session_idstring

Canonical objects

Customercustomer_id

Orderorder_id

Campaigncampaign_id

Journeysession_id

Customer 1 → many Orders Campaign 1 → many Journeys Grain and keys validated

03 / SQL + API

Make commerce data inspectable.

Use the visual query builder or write ClickHouse-flavored SQL directly. Move from top-line business questions into tables, joins, exports and custom logic without leaving the same governed data model.

SQLBeautiful editor for no-code and advanced query workflows

APIProgrammatic access for custom integrations and automation

ExportMove transformed data into downstream warehouses when needed

commerce_performance.sql Ready

SELECT day, revenue, spend, revenue / spend AS mer
FROM commerce_performance
WHERE day BETWEEN @start_date AND @end_date
GROUP BY day
ORDER BY day;

Rows scanned 0 governed grain / day

04 / INTERFACES

Turn the data foundation into work.

The same transformed data becomes dashboards, reports, APIs, Moby answers and agent workflows. Every interface starts from the same organized commerce context, so analysis can become an action without rebuilding the stack.

DashboardsFlexible operational and analytical views

MobyNatural-language analysis across the governed data layer

AgentsRules, recurring work and destination delivery

Governed result / ready to use Preparing destinations

Signal MER fell 18% yesterday grain: day · source: governed

DashboardWaiting

Operating view refreshed

MobyWaiting

Meta prospecting drove 71% of the decline.

Explanation grounded in the same result

APIWaiting

POST /signals/performance

Payload delivered to endpoint

AgentWaiting

Inspect campaign mix
Draft budget action
Queue for review

Architecture

The whole data stack. Already assembled.

Most ecommerce teams stitch together fetchers, transformation jobs, definitions, warehouses, dashboards and AI tools. Triple Whale makes that stack one native system.

Commerce sources

First-party buyer signals, seller inputs and the external platforms that run the business.

Connected

Sensory fetchers

A proprietary integration system that continuously brings diverse source data into the platform.

Ingested

Canonical + Saber

Standardized schemas, enriched context and an industry-first commerce data dictionary organized around business objects.

Understood

SQL + developer access

Inspect and manipulate transformed commerce data with SQL, APIs and exportable data products.

Queryable

Dashboards + Moby + agents

Put governed context directly into analytical interfaces, AI reasoning and recurring work.

Activated

Real-time attribution

The business moves. The model moves with it.

Attribution should not be a delayed reconciliation project. Triple Whale connects incoming commerce and marketing signals to an explanatory layer that updates as the journey unfolds.

Live commerce stream ingesting

10:42:08METAad interaction linked to active journeyMATCHED

10:42:11PIXELproduct view appended to session pathLIVE

10:42:17SHOPorder outcome joined to canonical transactionVERIFIED

10:42:18MODELattribution context refreshed for analysisREADY

Data anthology

A dictionary built around commerce, not infrastructure.

Saber gives data teams, operators and AI systems the same language for how a business works: the grain of every object, the tables that represent it and the boundaries between transaction truth and analytical explanation.

Shared languageOne definition layer for humans, SQL and agents

Object boundariesOrders are not sessions. Platform delivery is not attribution.

Agent contextQuestions route to the right data surface before reasoning begins

Triple Whale data ontologyConceptual explorer

Core object / topline

Store / business

The top-level business entity representing the store as a whole, where performance, operations and blended metrics come together.

Typical questions

How is the business doing overall? Is performance up or down?

Expected grain

One store per day.

Primary data surface

Blended performance and external revenue datasets.

Boundary

Use for topline performance, not order-level or customer-level detail.

Beautiful SQL, governed data

Ask exact questions. Inspect exact answers.

Go from a business question to the underlying data without losing the definition, source or grain. Build visually or write ClickHouse-flavored SQL directly.

NO-CODE QUERY BUILDER + TRADITIONAL SQL

CANONICAL COMMERCE TABLES + MODELED ATTRIBUTION

CUSTOM WINDOWS, JOINS, EXPORTS AND RECONCILIATION

SELECT
  day,
  sum(new_customer_revenue) AS nc_revenue,
  sum(ad_spend) AS spend,
  nc_revenue / spend AS nc_roas
FROM governed_commerce_metrics
WHERE day BETWEEN @start_date
  AND @end_date
GROUP BY day
ORDER BY day;

QUERY COMPLETE
MODEL / GOVERNED
GRAIN / DAY
OUTPUT / 10 ROWS

Native for AI + agents

The warehouse can finally do the work.

Moby can query the entire governed commerce layer. Agents can apply rules, generate reports, deliver findings and connect analysis to recurring workflows without reconstructing context every time.

Illustrative prompt

What changed in new customer efficiency yesterday, and which marketing entities explain it?

Evidence loaded Store / business
topline context Order
transaction truth Marketing entity
delivery context Attribution
outcome linkage

Structured answer

01 / Start with the topline change.

Compare the exact current window against the required baseline and preserve the metric definition.

02 / Trace the explanatory layer.

Move from store-level movement into campaigns, journeys and attributed outcomes without substituting one source for another.

03 / Turn the finding into work.

Send the report, update a destination or queue a governed action with the evidence attached.

A commerce ecosystem, not a closed database

Data infrastructure for commerce

Your AI strategy should start with a data foundation that already understands ecommerce.

Connect the ecosystem. Organize the meaning. Query the truth. Put Moby and agents to work on top of it.

Explore the data model Visit Triple Whale