“Basedash is the first tool that writes and runs SQL, catches its own errors, self-corrects, and genuinely nails it.”

At Lex, a four-person startup led by co-founder Nathan Baschez, answering product and growth questions used to require hours of manual SQL, debugging, and exporting data into spreadsheets.

The workflow was typical of an early-stage team:

• Blazer, a self-hosted tool for saved queries and basic dashboards
• Hand-written SQL, sometimes exported into Sheets for visualization
• Claude, used to help generate queries, but still requiring heavy prompting and manual debugging

“It was always high effort. Now, with Basedash, it just feels like chatting with the database directly.”

Why Basedash

Nathan had been looking for something that worked more like a colleague than a tool:

• Write and debug SQL automatically
• Handle errors without human intervention
• Deliver results that could be explored further in real time

That is what he found in Basedash.

“It feels like collaborating with a colleague, not a BI tool. It’s performing at the edge of what’s possible with LLMs right now.”

With a chat-first interface, Basedash became his default way of working with data. Instead of preparing dashboards or wrangling queries, Nathan now runs iterative conversations whenever he needs to investigate a problem or validate an idea.

“The natural paradigm for data is chat. Basedash makes that feel real.”

A Critical Incident on the Road

While traveling in San Francisco, Nathan received an alert: Lex’s AI costs were spiking beyond normal.

In the past, resolving the issue would have required writing SQL queries on a laptop, or worse, on his phone.

“I’ve written SQL on my phone before in Blazer. It’s not a pleasant experience. Basedash made it simple.”

Instead of fumbling with scripts, Nathan opened Basedash chat and asked:

“AI costs are spiking - can you figure out what’s going on?”

Within minutes, Basedash guided him through the same thought process he would have taken himself: was the spike coming from a small group of users, a particular model, or a specific feature? The tool asked clarifying questions along the way.

The diagnosis: bot users were abusing Lex’s free trial to run model distillation attempts. Because he could identify the issue quickly, Nathan and his team were able to patch the problem before costs escalated further.

“Basedash turned a very stressful moment into a simple fix.”

Lasting Impact

The incident reinforced Basedash’s role in Lex’s workflow:

• Speed: problems that once took hours now take minutes
• Confidence: conversations provide traceable logic to share with the team
• Productivity: less time wrangling SQL and more time on product and strategy

“Basedash feels like collaborating with a colleague, not a tool. It’s the future of how teams will work with data.”