Hey everyone,

While AI has sped up writing BigQuery SQL, the actual workflow around it is still heavily fragmented.

For most data teams, the process currently looks like this: prompt an external LLM, copy the SQL, paste it into the BQ console, fix the schema errors, run the query, and then export the results to a BI tool like Looker Studio or Tableau just to visualize it.

We built Dataki.ai to eliminate that context switching. It’s a unified workspace designed specifically to bridge the gap between AI, BigQuery, and your dashboards.

How it works:

• Schema-Aware Generation: Dataki connects directly to your BigQuery environment. The AI understands your actual tables and schemas, which drastically reduces hallucinations.
• Auto-Visualization: When a query runs, the output is automatically mapped to interactive visualizations. No manual axis mapping required.
• Full Code Control: The platform doesn't hide the code. The generated SQL is fully exposed in the editor for your team to tweak, optimize, and review.
• Instant Dashboards: You can pin any chart or table directly into a live dashboard without leaving the platform. Then share with your team

Why we're posting:

Dataki is currently in beta and completely free to use.

We are looking for unvarnished feedback from data engineers and analysts who live in BigQuery (or any supported data soruceS). We want to know how the platform handles your real-world workflows, and more importantly, where it breaks down when you throw complex schemas or nested arrays at it.

If your team is looking to streamline the AI-to-BI pipeline, you can try it out here: dataki.ai

We'll be in the comments to answer any technical questions or hear your feedback.