Finding the Right Partner to Help You Fix Your AI MVP
If you’re considering having your AI MVP reviewed, it’s important to recognize that there are different types of code reviews – especially when it comes to AI products.
Whether you’re in the midst of running demos for your AI MVP or about to give a pitch to an investor, you may feel uncertain about its security, scalability or trustworthiness in a production environment. This is common with a lot of projects being put together using “NoCode” or “LowCode” tools (i.e., with Copilot/ChatGPT).
The unfortunate truth is that AI MVPs built from NoCode/LowCode tools tend to be very precarious because they typically have several underlying issues like leaking abstractions, brittle logic, lack of unit tests and a significant amount of technical debt. Because of this, you don’t just need a code review – you need a partner to help you address these types of issues and do a thorough job of repairing broken code.
Let’s look at how we at Appricotsoft perform AI MVP rescue missions and what you need to consider in order to make sure you choose the right partner for your MVP.
What Makes AI MVPs Fragile (and Solutions)
AI tools allow developers to write code extremely quickly without providing any context or continuity or maintainable code. So what happens? These AI MVPs will:
- Work in demo scenarios, but when faced with edge conditions the project/product will fail.
- Be implemented without tests and/or documentation.
- Incorporate mixed architectural patterns in an unpredictable way.
- Contain silent bug/security holes ethical issues.
It isn’t your fault as that is how AI is utilized in development today; however in order to fix this issue you will need to have a code audit team that is focused on identifying issues unique to AI and does not just perform general QA activities.
Things To Consider When Evaluating Your Code Review Partner
When you are choosing an individual or organization to review/refactor your AI MVP, it is important to not simply select a generic development shop or contractor. You should search for a team that:
✅ Has Had An Audit (or experiences) of AI Code
They has seen the some of the patterns that can occur in many forms of AI-generated code including examples of ChatGPT hallucinations & Copilot boilerplate. The team should have been exposed and have previous experience in spotting these types of issues with speed and expertise (because they have seen them) and can help you avoid them while providing an audit of your AI-generated code.
✅ A Process That Is More Than Just Reviewing Code.
There are additional audits in reviewing code, such as:
- Security (vs. dependencies)
- Infrastructure/Deployment Readiness
- Quality Assurance/Test Coverage
- Traceable/Detailed Decision Logs
These are the things that can make the difference between delivering demo software (vs. production-ready software).
✅ Communications That Are Understandable (if You Do Not Have Any Knowledge of Development)
If you don’t have a technical background (and/or are a founder), the reviewing team needs to have the ability to clearly articulate on the Risk and Options associated with your code and the project, in non-technical terms (e.g. plain English vs. GitHub comments).
The Appricotsoft Method: Evaluate + Repair rather than simply report
At Appricotsoft, we collaborate with countless startups to assist them in moving through the development phase of their AI minimum viable product to the deployment of a scalable product. We create a process of discovery to find broken things, to disclose why they matter, and to repair them without impeding your momentum.
Our Unison Framework combines real human expertise with AI-first delivery, creating a process where code, product thinking, and client alignment all fit together.
In simple terms, the process is:
1. Your AI Minimum Viable Product (AI MVP) Audit
We perform an AI-generated code audit and not simply a static linter run, which includes:
- Checking for functional and security-related risks
- Making suggestions for improved clarity and maintainability of code
- Determining how well-tested the code is
- Finding edge cases
- Confirming the architectural consistency of the code
2. Report and Prioritize
Our reports contain actionable summaries and do not take up 80+ pages. Your report will have:
- Screenshots as context for each actionable item
- Suggested corrective actions grouped by priority
- Estimate of the effort to complete each corrective action
3. Fix and Refactor
After completing your AI MVP report and identifying problems, our team will fix the problems found. From broken logic to missed test cases to mystery code generated by AI, we will eliminate your guesswork and hold ourselves accountable to be sure your problems are fixed.
4. Restore Your Trust through Demos
You’ll never have to guess if things are getting better, as we provide you with demo sessions every week at your convenience, along with regular transparent reviews of your work backlog. You can see what is being done correctly or not, confirm damaged items have been repaired, and modify timelines.
Founders: Don’t Wait to Experience Production Pain
If you’ve built your MVP using AI tools, it’s not a question of whether or not issues will arise, but instead when they will come to the surface. Early fixes for startup app development occur at a completely different cost than when something experiences a critical failure in production.
So how can you tell if it’s time to conduct an audit?
- You have no idea how your application will handle its “edge” cases (unintended or unique user interactions that may occur).
- You are currently coding new features or functions on top of “temporary” hacks or temporary solutions.
- Other developers cannot explain why certain aspects of the code were written the way they were.
- There are no unit tests or integration tests for the current app.
If these points resonate with you, it may be time for a technical audit specific to startups developing applications using AI technology.
Questions to Ask Before Selecting a Partner:
- Have they successfully completed machine learning and AI-based code review & audit projects in the last 3 years?
- Will they present their report to you in layman’s terms?
- Do they also provide code refactoring services?
- How do they communicate what has been accomplished? (Their answer should be “our weekly demo”)
- How do they address security, documentation, and test coverage?
If a potential partner cannot meet any of these criteria, you may want to reconsider.
What You Will Gain from the Right Partnership
✅ Confidence in your fundraising efforts
✅ A well-defined technical roadmap
✅ Reduced technical debt
✅ An actual scalable product
The mission at Appricotsoft is simple: we want to create software that we are proud of – and make sure you can scale that software with confidence. With experience developing AI platforms, mobile applications, and web-based solutions for clients throughout the United States and Europe, we are fully prepared to help you achieve similar results.
Review Your AI MVP Together
Whether you’re under pressure from your investors, growing rapidly, or just wondering what the future holds, we’re happy to facilitate your work. Contact us for a complimentary consultation, or skip ahead to our code audit quote form to begin.
If you would like to obtain additional insights, read these resources: