Others now rely on it
The tool has users and affects daily operations.
AI software assurance and production engineering
From an AI-built app to software your business can trust.
We review code, access, data and deployment for internal tools, business-critical systems and SaaS products. When we find risk, we can help fix it.
From prototype to production
Faster software creation does not remove responsibility for how it runs or what it can access.
The turning point
A prototype became part of the business.
It starts as a tool for one person. Then come more users, customer data and access to production systems. That is the time to review trust boundaries.
It handles important data
Customer or employee data, or a production database, is now involved.
Access is too broad
The app or agent can do more than its job requires.
Ownership is unclear
No one owns deployments or incident response.
What we review
The code is only part of the system.
Automated scans find some problems. The review also covers data flows, business logic and deployment decisions.
Production readiness audit01Architecture and trust boundaries
02Authentication and authorization
03Secrets and agent permissions
04Data and database roles
05Dependencies and integrations
06Deployment and rollback
07Backups and recovery
08Tests, logs and ownership
One path forward
From first review to ongoing change control.
We match the scope to the application’s risk and maturity. Not every team needs a full audit on day one.
01
Secure AI development workshop
Access rules, agent configuration and review habits for the team.
02AI code health check
A focused review of the biggest risks and launch blockers.
03Production readiness audit
A deeper review of architecture, security and operations.
04Architecture rescue
Fix the risks without rewriting the application by default.
05Continuous assurance
Review critical changes and architecture decisions after launch.
06A plan for change and growth
We identify necessary fixes and refactoring, then prepare a plan your team can carry out independently or with our architects.
After the audit
Keep the code that works. Fix what should not be left to chance.
The audit report comes before a remediation proposal. Every recommendation has a reason. Your team, ours or another supplier can make the changes. We then retest the findings.
How we rescue architectureWho this is for
- Internal tools supporting important operations
- SaaS applications before customer release
- Systems built quickly with Claude Code, Codex or other agents
- People and teams who want to put AI-assisted code into production responsibly
- Existing apps without clear access control, tests or deployment procedures
Portfolio
Explore our work on university systems and medical software.
Before we work together
Questions before an audit
Do you only review AI-generated code?
No. We also audit conventionally written applications and systems developed by several teams. We examine architecture, data flows, access controls, testing and deployment. The origin of the code helps explain the development process, but it does not replace a review of the running system.
Will you rewrite our application?
Usually not. We identify parts that work and areas that need repair or refactoring. The report separates launch blockers from changes that can be planned later, so your team can decide which fixes to implement independently.
Is this a penetration test?
The application audit reviews code, architecture, data access, configuration and production readiness. We do not present it as a penetration test. If specialist security testing is needed, we scope that separately.
Can we keep using Claude Code and Codex?
Yes. We review what agents can access, which data and services they can reach, and how the team checks their changes. You can continue using AI tools while adding code review, tests and access controls suitable for a production system.
Do you need production access?
We agree on the minimum access before starting. A repository, architecture documentation and a test environment are usually enough. If we need to inspect production configuration, we agree on the scope and method of access first.
Will our code be sent to an external AI model?
We do not send client code to external AI services without explicit contractual approval. Before the audit, we agree on how materials are processed, who can access the repository and how data is handled. We can also define which code must not be used with AI tools.
Let’s talk
Tell us what your application depends on today.
The type of system, its users and your main concern are enough. Do not send passwords, API keys or source code.