Most are still shipping “AI add-ons.” The real shift happens when the whole workflow disappears into one action — that’s when users actually feel the value.

mhhh… yeah dude, I totally get where you’re coming from. FinTech isn’t something you wanna gamble with, especially after getting burned once. Honestly, you’re right Clutch ratings and fancy portfolios don’t tell the full story. A lot of companies look great until you actually start working with them. What helped me personally was changing how I evaluated teams: First, I asked them to walk me through a real project they built, not just show it. Like what went wrong, how they handled compliance, delays, bugs, etc. That tells you way more than polished case studies. Second, I checked if they actually understand regulations (KYC, AML, etc.) if they can’t explain this clearly, that’s a red flag in fintech. And yeah, communication matters a LOT, if they’re slow or vague in early conversations, it usually gets worse later. For me, I ended up working with a company called Suffescom when I was building a neobank app. Not saying “go hire them” or anything, but my experience was solid mainly because they were pretty transparent and knew the fintech side well. That made a big difference. At the end of the day, I’d say don’t rush it. Treat it more like hiring a long-term partner than just outsourcing dev work.

The OWASP LLM risks become even more critical when you consider that AI coding agents now have shell access and can modify files directly. Prompt injection isn't just a chatbot problem anymore — it's a supply chain risk when an agent reads untrusted input (like a GitHub issue body) and executes code based on it. Two practical mitigations I've found effective: 1) Sandboxing agent execution so it can't access credentials or production systems, and 2) Using pre-commit hooks that scan for common patterns like hardcoded secrets or suspicious shell commands in AI-generated code. Claude Code's hook system supports this natively, which helps enforce security gates in the CI pipeline automatically.

API docs get attention. The frontend/API contract usually doesn't. TypeScript helps, but types lie without runtime validation. The API returns an unexpected null, a renamed field, an edge case you never tested and your types had no idea. Zod fixes this. Parse at the boundary. If the API changes shape, you catch it at the schema. Not in a Sentry alert a week later. We do this with Next.js Server Actions too. The server/client boundary is the natural place to validate. Keep the schema next to the call. Documentation problem and type-safety problem are usually the same problem.

This really clicked for me. It kind of reminds me of how backend systems moved away from one big service into smaller pieces that each do one thing well. Also the idea that context is something you have to manage instead of just keep adding to it… that changes how you approach the whole thing. Feels like the hard part isn’t prompting anymore, but how you structure everything around it.

You’re definitely not alone that “Step 5 bottleneck” is where most AI-assisted teams hit reality. Right now, most teams aren’t fully automating reviews yet. The common pattern I’m seeing is a hybrid approach, not purely human or purely automated. What others are doing AI generates code → Automated checks (linting, tests, security, architecture rules) → Targeted human review (not full manual review) 👉 The key shift: humans review intent + architecture, not every line.