Stability & Reliability Hardening
We find and fix the failure points that only show up under real traffic — race conditions, unhandled errors, memory leaks, and missing retry logic. The product stops going down when it matters most.
You raised a round on an AI-generated MVP. Now real users, real load, and real risk are on the way. TechBar engineers stabilize, secure, and scale vibe-coded products without a full rewrite — our team is ready to jump in ASAP.
AI coding tools got you to a funded MVP faster than a hand-built team ever could — but speed and structure trade off against each other. Vibe-coded codebases usually work fine in a demo and start breaking under real signups, real traffic, and real attackers. At this point, founders realize the product that got them the round can’t carry the company past it, and no one on the team has the bandwidth or the security and scalability background to fix it properly.
TechBar engineers join the codebase directly, read what the AI actually built, and take ownership of the parts that put the business at risk. We work inside your existing stack — Cursor-, Claude Code-, Copilot-, Lovable-, Replit-, or Bolt-generated code included — without demanding a rewrite from scratch. Most importantly, we prioritize by risk: stability first, so the product stops breaking under load; security second, so user data and your funding round don’t turn into a breach headline; scalability third, so growth doesn’t outrun the architecture.
We provide first engineer profiles within 2–3 business days — fast enough to fix issues before your next investor update or your next viral spike.
We find and fix the failure points that only show up under real traffic — race conditions, unhandled errors, memory leaks, and missing retry logic. The product stops going down when it matters most.
We review authentication, authorization, secrets management, and data handling for the gaps AI-generated code typically leaves open, then close them before they become an incident.
We identify the components that won't survive 10x or 100x usage — database queries, synchronous bottlenecks, single points of failure — and re-architect them to scale horizontally.
We untangle duplicated logic, inconsistent patterns, and "god files" left behind by prompt-by-prompt development, and introduce clear boundaries a real team can work inside.
We add automated tests around the riskiest parts of the product first, then wire up CI/CD so releases stop depending on manual QA and crossed fingers.
We add logging, error tracking, and alerting so your team finds out about problems from a dashboard — not from a customer complaint on social media.
We audit schema design, indexing, and backup strategy, and fix the data-modeling shortcuts that cause silent corruption or slow queries as the dataset grows.
We review third-party API integrations, data flows, and privacy posture against GDPR, SOC 2, or whatever your investors and enterprise customers will ask about next.
Five stages with clear deliverables at each step. The framework reduces uncertainty from the first conversation through ongoing delivery.
Schedule a Discovery CallWe stabilize incrementally — the riskiest modules first — so you don't have to pause the product or halt onboarding while we work.
You receive matching profiles within 48–72 hours. Selected specialists start on tasks already prioritized by risk.
We work inside the code your AI tools already generated. Where it can be salvaged, we salvage it; we only replace what genuinely can't be fixed in place.
As we work through the codebase, we document architecture and decisions for the next engineer you hire.
Stability, security, then scalability — in that order — because a fast product that leaks data or falls over under load doesn't survive its next funding round.
When due diligence or a security questionnaire lands on your desk, you have real answers — not guesses.
Anonymized profiles from our current bench. These specialists are available now and have experience taking funded MVPs to production.
Led a security remediation for a Series A fintech MVP, closing 40+ vulnerabilities including exposed API keys and broken auth flows before a Series B due diligence review.
Introduced automated test coverage into an untested AI-generated codebase, catching regressions before release and cutting post-deploy bugs by 70%.
Stabilized a vibe-coded SaaS platform that was crashing daily under real user load, cutting downtime by 90% in the first month through targeted fixes and monitoring.
Untangled a prompt-by-prompt built MVP into a maintainable codebase with clear module boundaries, enabling the founder to hire their first three engineers.
Re-architected a bottlenecked Postgres-backed API for a funded startup, taking it from 200 to 8,000 concurrent users without a rewrite.
Cut cloud spend by 55% for a post-MVP startup by fixing inefficient queries and right-sizing infrastructure that AI tooling had over-provisioned.
Not every engineering challenge requires the same setup. TechBar structures the engagement to match your scope.
Schedule a Discovery CallIt usually means the MVP was built primarily through AI coding assistants — Cursor, Copilot, Replit Agent, Bolt, Lovable, v0 — with minimal architectural planning up front. That’s a legitimate way to get to a funded product fast, but it tends to leave behind inconsistent patterns, missing tests, and security shortcuts that a human team would normally catch in code review. The code isn’t necessarily bad — it’s just unaudited.
No, usually not. We start by identifying what’s genuinely broken versus what’s just unfamiliar, and fix in place wherever the underlying logic is sound. A full rewrite only makes sense for the small number of modules that can’t be safely modernized.
We rank issues by risk, not by codebase area. The typical sequence is:
Yes. Security review runs alongside your existing feature work rather than blocking it — findings get triaged by severity, and critical issues get fixed immediately while lower-risk items go into the backlog your team already manages.
Typically 1–2 weeks. Matching profiles arrive within 48–72 hours, and selected engineers are onboarded into your repository and sprint cycle shortly after.
Yes. Part of the engagement is translating technical risk into business terms — what could break, what it would cost you if it did, and what we’re doing about it — so you can make informed calls without needing to read the code yourself.
Yes, that’s one of the most common reasons founders bring us in. We document what we find and fix, so you walk into diligence or a security questionnaire with real answers about architecture, security posture, and data handling instead of guesses.
Yes. Many founders bring us in for the specific gap their current team doesn’t cover — usually security or scalability — while their engineers keep shipping features. We integrate into your existing workflow rather than replacing it.