Pros
Nothing advanced or beyond basic/average points
Cons
Outdated Tech Stack: The engineering culture leans heavily on a legacy software stack that slows down feature delivery and stifles innovation.
Over-Engineered Architecture: Simple problems are frequently met with overly complicated solutions, creating unnecessary technical debt and making onboarding difficult.
Cultural bottleneck: Running an ML business simply because we are 'nice guys' has led to unmanaged cloud spend, resulting in a highly inefficient infrastructure footprint.
With $95M raised and nearly a decade in operation, the real-world impact is missing.