AI Build Help: AI Code, Apps and Software Pitfalls
AI has made the transformation of ideas into working software possible. With the right prompt and AI tools, users can create unique apps, features, or proofs of concept quickly and effectively.
For many teams, this is a game-changer that enables rapid progress. However, it often comes at the expense of security vulnerabilities, scalability issues and lack the technical architecture needed for production environments.
Speed helps you get moving, but it doesn’t always hold up once you need real users and data.
At I-Finity, we support businesses navigating the challenges of early AI-built software and websites. In this blog, we explore common pitfalls, why they happen, and how an AI-built app can be used to support your production-ready application.
The rise of AI-built apps
There are good reasons AI app builders are popular. They lower the barrier to entry, reduce upfront costs, and help teams explore ideas without committing to a full development cycle. For internal tools, prototypes, or early validation, they can be useful.
The challenge appears when those early builds are pushed further than they were designed for. Or when a simple AI tool needs to scale. Furthermore, if issues around GDPR and secure data storage aren’t addressed, then your quick fix AI app can become a significant risk.
Something that “worked well enough” in a demo environment can quickly unravel when real users, real data, and real security requirements are needed.
Common problems with AI-built apps
When AI-generated software starts to struggle, the issues are usually predictable:
- Fragile codebases: difficult to understand, extend, or safely change without unintended side effects.
- Lack of context: AI models will lose track of broader project capability, this can lead to inconsistencies.
- Unclear architecture: little separation between logic, data, and presentation, increasing the risk of breaking existing functionality.
- Limited scalability: AI-generated builds can hit a scaling ceiling, meaning you don’t have the capacity in users, data, or feature complexity.
- Performance bottlenecks: often AI-generated code is not optimised for performance and can contain code bloat resulting in high latency and inefficiencies.
- Weak security foundations: authentication and authorisation are often incomplete or poorly implemented.
- Data protection gaps: sensitive information is not always handled in line with regulatory expectations.
- Poor maintainability: limited documentation or ownership makes long-term support harder than it should be.
- Accessibility issues: not meeting national or international accessibility standards can hinder an app’s usability.
- Lack of ownership: with AI-generated code, who owns the IP? Currently, ambiguity around ownership remains. This may be important for a start-up or a business who needs to own its intellectual property (IP) to add value.
Over time, teams often realise that an AI-coded app requires support from a web agency where software developers can deliver on making the app production ready. This approach ultimately reduces business risk, avoids disruption, and protects user trust.
Why does this happen?
AI-generated code can create useful outputs, prototypes and demos quickly, but it doesn’t have the full picture. These tools are designed to produce something that works immediately, not necessarily something that will scale, remain maintainable, or meet complex compliance requirements over time.
Because of this, edge cases, regulatory needs, and integrations with other systems are often overlooked.
That doesn’t mean the AI or the user has “failed” — it simply reflects the limitations of the approach.
Understanding these constraints is the first step to addressing the issues. By recognising what the software can and cannot do on its own, organisations can make informed decisions about whether to repair, refactor, or rebuild, reducing risk and protecting long-term stability.
Can AI-built apps be fixed?
The short answer is yes, sometimes.
The more important answer is that sometimes rebuilding is the safer, more cost-effective option. The key is knowing which route makes sense before investing more time and money.
A structured AI-code fix begins with understanding what already exists. Some elements may be perfectly usable. Others may need reworking or replacing entirely.
The goal is never to throw everything away by default, but to make informed decisions that protect long-term viability.
How I-Finity approaches AI-generated builds
Our approach is structured to give clarity and confidence:
- Technical audit: we go beyond surface-level functionality to examine architecture, code quality, security, scalability, and integration points.
- Tailored recommendations: based on the audit, we advise whether to repair, refactor, or rebuild.
- Preserve what works: focus on keeping functional elements while replacing or improving what causes problems.
- Reduce wasted effort: minimise sunk costs by avoiding unnecessary rewrites and prioritising changes that deliver long-term stability.
- Practical guidance: we help customers understand the trade-offs and next steps for a reliable, maintainable solution.
Where I-Finity fits in
We often work with teams and individuals who know what they want their app to do, what business logic and workflows are needed. Using AI tools enables them to showcase their app concept.
However, they know they have reached the limits within AI or need specific code/logic to make it production ready. They may not have in-house specialists, or they may be dealing with a legacy platform alongside newer AI-generated components.
Our role is to translate technical realities into practical options, helping stakeholders understand risks, timelines, and trade-offs without overwhelming them.
We are AI-aware, but engineering-first. That balance matters when systems need to be secure, performant, compliant, supportable, and ready for future change.
Contact us today to see how we can get your project on track.
AI is a tool, not a shortcut
AI can accelerate development, but it does not remove the need for sound architecture, secure design, and long-term thinking. Treating it as a shortcut often leads to frustration later, when the cost of correction becomes unavoidable.
A well-planned AI-built app can lead to a project for a production-ready solution, project early technical validation is always the better option.
If you’ve used AI to build an app and it’s not doing what you need, we can help you understand why — and what your best next step is.
