PRODUCTION READINESS FOR THE AI-ASSISTED CODE
A TECHNICAL CHECKPOINT FOR TEAMS EXPANDING AI-ASSISTED DEVELOPMENT. WE FIND THE GAPS, VALIDATE THE RESULTS, AND FIX WHAT NEEDS FIXING ACROSS EMBEDDED, INDUSTRIAL, MEDICAL, AND OTHER HARDWARE-DEPENDENT PRODUCTS.
AI-assisted changes become part of the existing architecture, build environment, test infrastructure, dependencies, and product lifecycle. Issues can appear outside the changed code: duplicated logic, inconsistent interfaces, missing edge cases, dependency conflicts, or tests that repeat assumptions from the implementation.
The main risks fall into four areas: architecture, verification, production behavior, and traceability.
Architecture & maintainability
AI speeds up local implementation while introducing duplicate logic and inconsistent patterns, increasing maintenance effort and defect risk across the codebase.
Verification & requirements
AI-generated code and tests share implementation assumptions, leaving requirements, edge cases, and failure paths unverified before release.
Runtime & target behavior
AI produces code without full target context, leaving timing, memory, concurrency, and hardware issues to surface during integration and system testing.
Traceability & control
AI increases the pace of code changes while requirements, reviews, and verification records fall behind, leaving gaps in approval and release evidence.
What we offer
Production-readiness assessment
Architecture, code, tests, dependencies, performance, and maintainability reviewed together. The output is a prioritized picture of what can stay, what needs attention, and what stands between the current codebase and production.
Architecture review
Module boundaries, interfaces, dependencies, coupling, state management, and failure propagation. Particularly useful when fast iterations have produced several locally reasonable but globally inconsistent design decisions.
Code review & verification
Correctness, error handling, concurrency, resource management, maintainability, and behavior across component boundaries. Automated analysis where useful; engineering review where tools cannot provide enough context.
Requirements-to-code verification
Implementation checked against product and system requirements. Useful when the code satisfies the task or prompt that produced it, but nobody has yet verified the complete behavior against the actual specification.
Codebase consolidation
Rapid AI-assisted iteration can leave duplicated logic, inconsistent patterns, unnecessary abstractions, and brittle interfaces across otherwise working code. We consolidate the parts that affect continued development, maintenance, and future changes.
Dependency & build stability
Third-party libraries, version compatibility, deprecated APIs, dependency pinning, build reproducibility, and CI behavior. Especially relevant when generated solutions introduce libraries faster than anyone intended.
Performance & scalability validation
Memory use, latency, CPU load, concurrency, allocations, and workload behavior under realistic conditions. For embedded software, this extends to the actual target and its resource constraints.
Codebase stabilization & remediation
For codebases already past the assessment stage: fix the high-risk areas, reduce accumulated debt, restore predictable builds and tests, and leave the code in a state where normal development can continue.
AI development governance & traceability
Review rules, approvals, traceability, documentation, and practical boundaries for AI-assisted development. The goal is not another governance layer, but enough control for the engineering and regulatory environment you already have.
Where production readiness matters most
Embedded & hardware-adjacent software
AI-assisted development works well for isolated implementation tasks, but embedded behavior depends on context that is not always visible in the code being changed. Timing, interrupt handling, memory limits, hardware states, peripheral behavior, and dependencies between components all affect whether an implementation works on the actual device.
Problems often appear during integration: missed timing windows, race conditions, excessive memory or CPU use, incorrect hardware state handling, or code that works on one configuration but fails on another.
We review AI-assisted changes against the system architecture and hardware constraints, then validate the critical behavior on the target under realistic operating and failure conditions.
Medical device software
AI can speed up implementation and test development, but a medical software change has to satisfy more than the immediate coding task. The implementation has to remain consistent with system and software requirements, risk controls, interfaces, and the verification strategy already defined for the product.
The problem is not only incorrect code. A change can work as implemented while leaving a requirement partially covered, a risk control unverified, or the traceability between requirements, code, tests, and results incomplete.
We review the implementation together with its requirements and verification evidence, identify the gaps, and bring the change into the product’s existing review, verification, and change-control process.
Industrial & connected systems
AI-assisted development handles defined interfaces and expected behavior well, but deployed industrial systems rarely operate under clean, predictable conditions. Sensors produce bad or missing data, devices disconnect, protocols behave differently across equipment generations, network links fail, and components restart or recover independently.
These conditions expose state-management, synchronization, buffering, recovery, error-handling, and interoperability problems that are easy to miss when implementation and testing focus on the expected path.
We test the software against real system behavior, including degraded connectivity, invalid data, device failures, restart and recovery scenarios, and interactions with existing equipment and protocols.
Backend & cloud software
AI makes it easy to add an endpoint, service, dependency, query, or integration locally. In an established platform, that change also becomes part of existing data flows, service boundaries, authentication, deployment, monitoring, and failure handling.
Problems accumulate when locally correct changes introduce incompatible interfaces, duplicate existing functionality, increase dependency complexity, change data behavior, or create failure paths elsewhere in the system.
We review AI-assisted changes in the context of the existing platform, validate behavior across service and integration boundaries, and fix architecture, dependency, performance, and reliability issues before they spread into further development.
Why add us to your team
80% of our engineers are senior-level, and our technical leads average 15+ years of experience.
The engineers assigned to the project work directly with the code, architecture, tests, and product constraints. For an assessment like this, we don’t separate the review from the engineering expertise needed to understand what the code is actually doing.
Our teams have worked in embedded and system-level software for 30+ years, from firmware, BSPs, drivers, and operating systems to applications, integrations, and backend software.
That matters when the problem attributed to a piece of code actually sits at a hardware boundary, in another component, or in the way several parts of the system interact.
We maintain four embedded test labs and can take validation beyond source review, static analysis, and CI.
Our engineers work with target hardware, HIL setups, simulators, peripherals, and system-level test environments when the product requires it. Hardware-based development and testing has been part of our delivery model for years, including dedicated customer labs for long-running product programs.
Our QMS is compliant with ISO 13485 and IEC 62304, and our teams work with requirements, traceability, verification, change control, and customer quality processes.
This is established practice, not a process created for AI-assisted code: our medical-device engineering history includes software development and testing for products used in critical clinical environments.
We are a software engineering company, not an audit practice. With 200+ engineers, we can keep the same engagement moving from assessment into refactoring, testing, performance work, hardware integration, or continued development.
Long engagements are normal for us: engineering teams behind PerformaCode have worked with some customers for decades and stayed with products through multiple releases and technology generations.
Relevant Engineering Experience
Engineering toolset
Languages
- C
- C++
- Python
- Java
- C#
- Rust
- Go
- JavaScript
- TypeScript
- Bash
- Assembly
Operating Systems & RTOS
- Embedded Linux
- Yocto
- Buildroot
- FreeRTOS
- Zephyr
- QNX
- VxWorks
- ThreadX
- Windows
- Android
- Bare Metal
Build & Toolchains
- CMake
- Make
- GCC
- Clang/LLVM
- MSVC
- GDB
- Git
AI-Assisted Development
- GitHub Copilot
- Claude Code
- Cursor
- OpenAI APIs
- Azure OpenAI
Testing & Verification
- GoogleTest
- pytest
- Robot Framework
- CTest
- HIL
- SIL
- Unit Testing
- Integration Testing
- System Testing
- Regression Testing
- Code Coverage
- Fuzz Testing
Code Analysis & Debugging
- Coverity
- SonarQube
- Clang-Tidy
- Cppcheck
- Valgrind
- AddressSanitizer
- ThreadSanitizer
- GDB
- Static Analysis
- Dynamic Analysis
Performance & Profiling
- perf
- Valgrind
- Profilers
- Memory Analysis
- CPU Profiling
- Leak Detection
- Thread Analysis
Hardware & Target Validation
- ARM
- x86/x64
- RISC-V
- JTAG
- SWD
- I2C
- SPI
- UART
- CAN
- USB
- PCIe
- Ethernet
Simulation & Virtualization
- QEMU
- Simics
- Virtual Platforms
- Device Modeling
- Hardware Simulation
Requirements & Traceability
- DOORS
- Polarion
- Jira
- Azure DevOps
- Requirements Traceability
- Change Control
CI/CD & Dependencies
- Jenkins
- GitLab CI/CD
- GitHub Actions
- Azure DevOps
- Docker
- Artifactory
- Conan
- vcpkg
LET’S LOOK AT YOUR CODE
Have AI-assisted changes already made their way into your codebase? Tell us what you’re working with and where you need help. We can start with a focused assessment or join your team for remediation and further development.
Got Questions?
What do you mean by AI-assisted code?
Software where AI coding tools have been used to generate, modify, refactor, debug, or test parts of an existing codebase. We are not assuming the entire product was AI-generated.
Do you need to know which code was AI-generated?
No. We can assess the codebase without a clean record of which changes came from AI tools. Where that history is available, it provides additional context, but the review is based on the software and its expected behavior.
Our tests pass. What else are you looking for?
Passing tests tell us that the tested behavior passes. We also look at architecture, interfaces, error handling, concurrency, resource use, dependencies, build stability, requirements coverage, integration behavior, and the assumptions built into the tests themselves.
Do you only assess the code, or can you fix it?
Both. We can take the findings into remediation: refactoring, fixing defects, strengthening tests, resolving build and dependency issues, improving performance, and validating the resulting changes.
Do you only work with embedded and medical software?
No. We work across embedded and system-level software, applications, integrations, backend services, and other software around connected products. The depth of the review depends on where the code sits in the system. Our experience in embedded, hardware-dependent, and medical software gives us particular depth in complex systems, but the same engineering approach applies to platform and enterprise software.
Can you work with our existing engineering team?
Yes. We can take a defined assessment or remediation scope, or work alongside your engineers within the existing codebase, development environment, review process, and release workflow.
Can you review AI-generated tests?
Yes. We review whether tests exercise the required behavior, relevant edge and failure cases, and integration boundaries rather than simply confirming the assumptions already present in the implementation.
Can you validate on target hardware?
Yes. For embedded and hardware-dependent products, validation can include target hardware, HIL, simulators, peripherals, and system-level test environments where required.
Do you need access to our AI tools or prompts?
Not necessarily. For an AI-assisted code review, we need as much of the existing SDLC context as you can provide: requirements, architecture and design documentation, source code, test cases and results, build and CI/CD configuration, change history, and other relevant development artifacts. Access to AI tools or prompts can provide additional context, but it is not a prerequisite. If important SDLC artifacts are missing, we can reconstruct them where needed, but this adds effort to the assessment.