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Build the Future with
AI & Data

ThinkingDBx builds varCHAR, an agentic data engineering platform — and ThinkingLanguage, the world's first compiled language purpose-built for data and AI.

About ThinkingDBx

ThinkingDBx is a technology startup based in Hyderabad, India and British Columbia, Canada, building intelligent tools for data engineering and AI. We created ThinkingLanguage — a compiled programming language where data pipelines, ML, and streaming are first-class features. And varCHAR — an agentic platform where data pipelines are built visually or through AI chat, not YAML files.

We build for builders. From solo developers to enterprise teams, our tools are designed to eliminate complexity and make working with data 100x faster.

Our Platform

Agentic Data Engineering with varCHAR

Flagship Product

varCHAR

An AI-driven data engineering and streaming platform that helps businesses build, manage, and scale data infrastructure 100x faster — with Agentic, visual, no-code pipelines.

Visual pipeline designer powered by Apache Spark, Kafka & Camel. Agentic data engineering through AI chat — build pipelines with Spark, SQL, PySpark, MCPs, Kafka & JDBC.

Agentic Pipelines
Build data pipelines 100x faster with AI agents via chat
Visual Designer
Drag-and-drop pipeline designer powered by Spark, Kafka & Camel
Real-Time Streaming
SQL, PySpark, MCPs, Kafka, JDBC & more
Enterprise Grade
Spring Boot backbone with production reliability
Introducing

A New Programming Language — Data Deserves Its Own Language

Open Source · MIT + Apache 2.0

ThinkingLanguage

The world's first compiled language where data pipelines, SQL-like queries, ML training, and real-time streaming are all first-class language features — not libraries bolted on after the fact.

Compiled to native code via LLVM & Cranelift. Built entirely in Rust. Python-like readability meets Rust-like safety and performance.

// Complete ETL + AI in one language source users = postgres("db").table("users") -> User transform active_users(src: table<User>) -> table<User> { src |> filter(is_active == true) |> clean(nulls: { name: "unknown" }) |> with { tenure = today() - signup_date } } model churn = train xgboost { data: active_users(users) target: "is_active" features: [tenure, monthly_spend] metrics: [accuracy, f1, auc] } pipeline daily_churn { schedule: cron("0 6 * * *") steps { raw = extract users predicted = transform predict_churn(raw) load predicted -> postgres("analytics") } }
Also from ThinkingDBx

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ThinkingNews

AI & tech news distilled to 60 words or less. No fluff, no clickbait — just the stories that matter for builders, founders, and engineers.

Covering AI breakthroughs, startup funding, open source, and the Indian tech ecosystem. Updated in real-time.

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