Nathan Baschez
Founder
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,” Nathan explained. “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’s what he found in Basedash. “It feels like collaborating with a colleague, not a BI tool,” he said. “It’s performing at the edge of what’s possible with LLMs right now.”
Basedash is the first tool that writes and runs SQL, catches its own errors, self-corrects, and genuinely nails it.
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,” he said. “Basedash makes that feel real.”
A Critical Incident on the Road
The power of this approach became clear during a potentially catastrophic moment. While traveling in San Francisco, Nathan received an alert: Lex’s AI costs were spiking far 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,” he said. “It’s not a pleasant experience. Basedash made it simple.”
Instead of fumbling with scripts, Nathan opened Basedash chat and asked a direct question: “AI costs are spiking — can you figure out what’s going on?”
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? A specific feature? The tool even asked clarifying questions along the way.
“It was amazing to just chat with our database and get to the root of the problem,” Nathan said.
The diagnosis: bot users were abusing Lex’s free trial to run model distillation attempts. Because he could identify the issue so quickly, Nathan and his team were able to patch the problem before costs spiraled further.
“Basedash turned a very stressful moment into a simple fix,” he explained.
Lasting Impact
The incident proved more than just Basedash’s utility in a crisis—it reinforced its place in Lex’s workflow.
Speed: Problems that once took hours now take minutes.
Confidence: Conversations with Basedash provide a traceable logic he can share with his team.
Productivity: Instead of wrangling SQL, Nathan can focus on product and strategy.
“Basedash feels like collaborating with a colleague, not a tool,” he said. “It’s the future of how teams will work with data.”
