CEO at Altimate AI
Co-founder & CEO at Altimate AI
Open-source data engineering tooling with 1M+ downloads across thousands of companies in 100+ countries
Pradnesh Patil is the Co-founder & CEO of Altimate AI, whose open-source data engineering tooling has grown 7X in a single year, from 110,000 to 780,000 installs across hundreds of enterprise users in 100+ countries.
Altimate builds the infrastructure layer for safe AI adoption in data engineering. Their open-source harness, Altimate Code, gives teams a practical way to evaluate, benchmark, and deploy AI agents against real data workflows running on dbt, Snowflake, Databricks, Airflow, and any other modern data tooling. On ADE-bench, the industry's standard evaluation for agentic data engineering, Altimate Code scores 74.4%. It currently is outperforming Snowflake's Cortex Code CLI (65%), dbt Labs' own tooling (59%), and the Claude Code baseline (40%).
The problem Pradnesh is solving is specific: When it comes to data work, AI works in demos but isn't reliable enough in production. Data systems aren't like traditional software. They're more context-heavy, more fragile, carry higher downstream risk, and have messier dependency chains. Most AI tooling ignores this. It generates code that looks right but silently breaks pipelines, produces wrong numbers, or creates technical debt that compounds. Pradnesh and his team experienced this firsthand and built Altimate to solve it.
The platform enables safer, more reliable AI usage across the data development lifecycle, automating pipeline creation, testing, documentation, and cost optimization with agents that understand the actual context of your data infrastructure, not just the syntax. Today, enterprise users average $840K in annual hard savings; one customer recorded $8M in annual Snowflake savings from query and warehouse optimization alone.
Before Altimate, Pradnesh spent years as an engineering and product leader in the Bay Area, shipping enterprise products used by thousands globally. He holds a degree from USC and is based in Sunnyvale, CA.
AI & Data Engineering
Agents, pipelines, automation
Product Strategy
GTM, vision, enterprise
Cloud Computing
Snowflake, Databricks, dbt
FinOps & Cost Governance
Optimization at scale
Open Source Tooling
Community-driven innovation
Infrastructure for safe AI adoption in data engineering
Altimate AI builds the infrastructure layer for applying AI safely in data engineering. Most AI tools generate code that looks correct but silently breaks pipelines, produces wrong numbers, or creates technical debt that compounds. Data systems aren't like traditional software — they're more context-heavy, more fragile, and carry higher downstream risk.
At the core of the platform is a shift from traditional tooling to intelligent, agent-driven systems that actively participate in the data development lifecycle. Rather than simply assisting engineers, Altimate's AI agents are built to write, test, document, and optimize data pipelines with deep context awareness — across dbt, Snowflake, Databricks, and Airflow. The result: faster development cycles, lower infrastructure costs, and higher data reliability at scale.
Autonomous agents that write, test, and document data pipelines
Context-aware generation across dbt, Snowflake, Databricks, Airflow
FinOps-driven governance that catches waste before it compounds
OPEN SOURCE
Altimate Code is an open-source harness for applying AI in real data engineering environments. It focuses on handling production complexity — where context is fragmented, dependencies are layered, and simple prompt-based approaches fail.
Used by thousands of companies across 100+ countries with over 1M downloads, it provides a structured way to test, evaluate, and run AI systems across modern data stacks like dbt, Snowflake, Databricks, and Airflow.
Deep technical perspectives from building AI systems that work in production
Ready-to-use questions for your interview
Recent appearances and features
TECH FIELD DAY
YOUTUBE
Ranked first on independent AI data engineering benchmarks — a cheaper model with the right harness outperformed expensive alternatives.
Open-source tooling adopted by thousands of data teams globally, proving demand for production-grade AI in data engineering.
Shipped the open-source harness that drove adoption — giving teams a structured way to evaluate and deploy AI agents against real data workflows.
Interested in having Pradnesh on your podcast? Let's connect.
or contact directly: troy@kitcaster.com
CONNECT WITH PRADNESH
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