Wonder Analytics Autonomous Analytics Lakehouse

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Gaming · BigQuery bill

$22k/m → $180/m

Marketing · BigQuery bill · 5× data

$5k/m → $120/m

Gov · POC · Elasticsearch

$327,000/m → $3,000/m

Finance · POC · serverless BI

$2,000/m for 1M users

Until today

Pay the engineers, or pay the cloud. Or neither.

Until today, BI or a data product meant hiring a team of data engineers, maintaining a lake or warehouse, and hoping the result would be fast and cheap. Wonder takes you from raw data to full BI systems and data products: zero engineering, low cost, ultra-low latency.

MONTHLY RUN COSTMONTHS OF DATA ENGINEERING (ETL · PIPELINES · MODELING)01234PAY THE CLOUDfast to start, expensive foreverPAY THE ENGINEERScheap to run, months to buildTHE TRADE-OFF CURVEBigQuerySnowflakeElasticsearchRedshiftClickHouseDatabricksAthenaTrinoWonderUNREACHABLE UNTIL NOW MONTHLY RUN COSTMONTHS OF DATA ENGINEERING01234PAY THE CLOUDfast to start, expensive foreverPAY THE ENGINEERScheap to run, months to buildUNREACHABLE UNTIL NOWBigQuerySnowflakeElasticsearchRedshiftClickHouseDatabricksAthenaTrinoWonder

Every point is the same goal: a working data product at interactive speed.

No engineering on your side

You define

01

Raw Data Source

Connect your raw data wherever it is

02

Semantic Layer

03

Dashboards

Set your requirements

All Engineering on Us

CUSTOMER DATAWONDER BACKEND ENGINEBACKEND SERVERSWONDER ANALYTICS PRODUCTCDCdeployPostgrestransactions · OLTPMaster Datausers · products · accountsETL ServerQuery ServerReflow ServerOptimized Parquetspartitioned by the query logOptimized ProjectionsRAM · NVMe · bucketWonder CubeData Model and RequirementsDashboardsrun in the browserClient EngineDuckDB WASM · browser cacheAIverified answers
Customer data
  • Postgres
  • Master Data
Wonder backend engine
  • ETL Server
  • Query Server
  • Reflow Server
  • Optimized Parquets
  • Optimized Projections
Wonder Cube

Data Model and Requirements

Wonder analytics product
  • Dashboards
  • Client Engine
  • AI
10x

Send us last month's bill

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or email sales@indivi.ai

How we do it

Wonder Data DSLs: an AI-native programming language built for data

Fewer degrees of freedom. Fewer ways to be wrong.

The tooling AI needs
  • Semantic
  • Autocomplete
  • Compiler
  • Feedback loop
  • Debugger
  • Smart data lineage
  • Connectors
The problem

Everyone uses AI to create software that quickly turns into unmaintainable slop

IntentGeneral-purpose codeResult
Our approach

A language that limits AI to the right moves, with the tooling it needs to get them right

IntentDomain-specific languageResult