# Text to Data Products: Kaarvi’s End-to-End AI for Ingestion, Quality, and Dashboards

Page: https://stenobird.com/podcast/data-engineering-podcast/text-to-data-products-kaarvi-s-end-to-end-ai-for-ingestion-quality-and-dashboards
Text version: https://stenobird.com/podcast/data-engineering-podcast/text-to-data-products-kaarvi-s-end-to-end-ai-for-ingestion-quality-and-dashboards.md
Podcast: [Data Engineering Podcast](https://stenobird.com/podcast/data-engineering-podcast)
Published: 2026-06-08T01:14:52+00:00
Episode link: https://www.dataengineeringpodcast.com/kaarvi-agentic-data-engineering-episode-511
Audio file: https://op3.dev/e/dts.podtrac.com/redirect.mp3/serve.podhome.fm/episode/f6ff0caa-931b-4c08-bfdd-08dc7f5cd336/6391647681280765633f2d2645-06b3-49a6-90f2-3722747b3664.mp3
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JSON: https://stenobird.com/v1/public/podcasts/data-engineering-podcast/episodes/text-to-data-products-kaarvi-s-end-to-end-ai-for-ingestion-quality-and-dashboards
Duration seconds: 3172

## Resource

Summary  In this episode Shravan Gunda, founder and CEO of Kaarvi AI, talks about building an AI-native, agent-driven data platform designed to eliminate the janitorial work that consumes most data teams. He explores Kaarvi’s multi-agent architecture that runs queries across seven LLMs in parallel for reliability, its synthetic data generator that mirrors source schemas for quick testing, and “Hey Kaarvi” chat for text-to-SQL, text-to-transformations, and text-to-dashboard workflows. He also digs into on-prem versus SaaS deployments, domain-specialized agents for privacy and accuracy, code blocks for custom Python/SQL, and the roadmap for a marketplace and desktop assistant. Shravan highlights how Kaarvi compresses weeks of work into hours and bridges the gap between business users and data engineers by turning AI into a dependable force multiplier.  Announcements  Hello and welcome to the Data Engineering Podcast, the show about modern data management This episode is sponsored by DataDriven.io, the free data engineering interview prep platform built by data engineers for data engineers. Ever walked into a data engineering interview and gotten a question that has nothing to do with real data engineering work? Interviewing is its own skill, separate from the job. Watch your code execute live, inspect Spark internals, and whiteboard your data models and pipelines and defend your decisions. Unlike SQL-only or Python-only practice, DataDriven.io covers the full interview loop: star schemas, slowly changing dimensions, grain and fact table design, idempotency, watermarks, dead letter queues, change data capture, and backpressure. Every question comes from real Data Engineer interview loops at Google, Amazon, Meta, Stripe, Databricks, Netflix, and Airbnb. Go t…

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