Why we are building the AISEN
We are a cross-functional team of manufacturing engineers, software developers, process experts, and sales engineers.
We’re building the AISEN to solve a core problem we’ve encountered across every layer of industry:
"In theory, engineering is repeatable and reproducible.
In practice, it often isn’t."
Despite all the systems in place, critical process knowledge is often undocumented, inconsistently applied, or simply lost. As a result, successful processes cannot be reliably repeated—especially under pressure.
We've Seen the Cost
- Deep-tech startups collapsing—not because the core technology failed, but because stable production couldn’t be achieved in time.
- Process teams spending months rebuilding results they already had—because no one could find the exact setup again.
- Production launches cancelled because validation wasn’t traceable.
- A process that worked in one facility but failed in another—without anyone able to pinpoint why.
- Critical audits failed due to inconsistent documentation and incomplete data.
The Status Quo Is Broken
- Experiments are tracked in Excel or ad hoc tools—often with missing context.
- Cp/Cpk calculations are skipped or retrofitted.
- Trial settings are recorded once, then overwritten or forgotten.
- Engineers waste time redoing instead of scaling.
This isn’t just inefficient—it’s a systemic barrier to process reliability and scale.
Our Perspective
- Manufacturing engineers on our team have run the same experiments five times just to get clean data.
- Software engineers build tools that fit directly into real engineering workflows—with minimal overhead.
- Process experts define what matters: structure, traceability, robustness.
- Sales and support stay in feedback loops with real users to validate every design decision.
What We’re Building
- Structured experiment workflows with version control and traceable settings.
- Built-in statistical evaluation — Cp, regression, correlation — so analysis happens where the data lives.
- Automatic documentation of parameters, timestamps, batch numbers, operator inputs.
- Transferable process knowledge, ready for scaling across products, shifts, and facilities.
Our goal is clear: Make reproducible engineering the default—not the exception. And we’re building the AISEN to get us there.
