From Startup Speed to Enterprise Reality: Lessons from Contour Software

Last Monday, I led an industrial visit to Contour Software with a simple agenda: I’ve been building in startup environments, but I wanted a closer look at how a large corporate software house actually runs, and I wanted the juniors and sophomores with us to see what “industry ready” looks like in 2026 once you strip away the buzzwords.

What excited me most is that big software houses are finally hiring for judgment, and not just tech stacks and LeetCode, especially now that AI has made stacks more volatile than ever. Tools can be swapped, but strong systems thinking is what saves you when a rollout slips, and the data is sensitive, the blast radius is real, but you still have to ship.

What I noted is that big corporates have stopped treating AI like an add-on. It’s now showing up inside workflows where decisions actually happen, but the real work has shifted to building guardrails, observability, control, audit trails, and safe failure modes, otherwise this setup is synonymous with just automating mistakes at scale. This is similarly applicable to cybersecurity, AI does speed up detection and response, but it also widens the attack surface if you treat it like a plug-in instead of a constraint you have to govern.

My conversation around QA with Sir Haris Irfan informed me Level 3 agentic systems have taken on a big chunk of the load, generating test cases, widening edge coverage, running regressions, summarizing failures, and speeding up triage, while manual QA has mostly narrowed down to physical testing, hardware-dependent scenarios, and validation with actual users.

A core challenge highlighted in the session was about legacy systems. A lot of the core systems companies like Contour run are built on 15 to 20 year old tech, not because teams don’t want to modernize, but because that code holds the domain logic that literally pays salaries, and it’s so tightly coupled that you can’t just bolt AI onto it. So while AI fits neatly into new products, the older stack usually turns into a modernization problem first, fixable only by phased migrations or rebuilds while protecting what matters most, the data, the domain logic, and business continuity, because nobody wants innovation that breaks payroll.

If you’re early in your career, here’s what I’d genuinely bet on after this visit: instead of optimizing only for frameworks, build systems understanding, because once you’re inside real software, it’s architecture, tradeoffs, and change management that decide whether your work survives, which is why learning how legacy gets wrapped before it gets replaced, how observability gets added before you touch core flows, how test gates get designed to catch silent failures, how data gravity quietly shapes decisions, and how migrations ship in phases so the business keeps breathing, ends up being the difference between someone who can build and someone who can be trusted.

Thanks for the read. I’m collecting perspectives on this, what have you seen work?