What we learned from building a no-code data stack. And why we changed course. How AI copilots solve what no-code apps simply cannot.
At first, we set out to solve a problem that seemed, at first glance, straightforward. Businesses have data locked in different systems: CRMs, product analytics platforms, billing tools, and more. To answer even basic business questions, they needed to extract and combine data from these silos.
Our approach was ambitious. We wanted to build a tool that handled every step of the data pipeline — data ingestion, modeling, schema mapping, metric definitions, and visualization — all through an intuitive and simple no-code SaaS interface. Our vision was to empower analysts, ops teams, and business users to do what once required a full data engineering team.
But, as we built and iterated and onboarded use-cases, we uncovered deep structural flaws in this approach. These learnings have been pivotal in shaping our current belief that low-code/no-code solutions are fundamentally misaligned with the challenges of real-world data complexity.
We learned that Code-first tools, with the right scaffolding, allow you to embrace complexity without being overwhelmed by it. With AI copilots like Preswald, code-first architectures, and unified control planes, we can finally create data stacks that are both powerful and approachable.
At first, we set out to solve a problem that seemed, at first glance, straightforward. Businesses have data locked in different systems: CRMs, product analytics platforms, billing tools, and more. To answer even basic business questions, they needed to extract and combine data from these silos.
Our approach was ambitious. We wanted to build a tool that handled every step of the data pipeline — data ingestion, modeling, schema mapping, metric definitions, and visualization — all through an intuitive and simple no-code SaaS interface. Our vision was to empower analysts, ops teams, and business users to do what once required a full data engineering team.
But, as we built and iterated and onboarded use-cases, we uncovered deep structural flaws in this approach. These learnings have been pivotal in shaping our current belief that low-code/no-code solutions are fundamentally misaligned with the challenges of real-world data complexity.
We learned that Code-first tools, with the right scaffolding, allow you to embrace complexity without being overwhelmed by it. With AI copilots like Preswald, code-first architectures, and unified control planes, we can finally create data stacks that are both powerful and approachable.