Find the gaps between a convincing demo and a dependable application. We review AI-built software and AI-enabled products, then scope fixes for access control, data handling, evaluation, and production operation.
Direct access to the builder. Written scope before implementation. Code and deployment handed over.
Practical applications
Review authentication, tenant isolation, secret handling, validation, and critical user journeys. Prioritize issues by exploitability and business impact instead of treating every code-style warning as a release blocker.
Build representative evaluation cases, inspect retrieval and prompt behavior, and define refusal or escalation paths. Check regressions when a prompt, source, or model changes.
Examine tool permissions, approval gates, duplicate actions, retry behavior, and audit history. Turn the highest-risk failure modes into reproducible tests and a remediation plan.
Buying triggers
A scope usually starts with one or more of these situations, not a request for a particular framework.
The app was built quickly with Cursor, Lovable, Bolt, Replit, or another AI coding workflow and nobody has reviewed the complete trust boundary.
Real users, payments, customer data, an enterprise security questionnaire, or investor diligence are approaching.
Authorization appears to work in the interface, but database or server enforcement is unclear.
Secrets, dependency alerts, weak failure paths, missing tests, or unpredictable deployments make the team hesitant to launch.
Best For
Product and enterprise teams preparing an AI system for broader adoption, customer data, regulated workflows, procurement review, or production scale.
Typical tools & methods
Published review approach
Inspect the published review approach before commissioning an audit. It explains engineering checks; it does not claim an independent security certification or disclose private client findings.
Read the scope and approachIntended progress
Findings include affected paths, severity, evidence, likely impact, and a practical remediation order.
The team can choose which findings to fix, what acceptance evidence is required, and what remains explicitly out of scope.
Agreed fixes, regression checks, migrations, deployment verification, and rollback planning reduce avoidable launch risk.
Scope boundaries
Before you commit
Agree the application boundary and review access first. Findings and recommended fixes come before an implementation commitment, with remediation scoped separately when appropriate.
Repository size, deployment environments, roles, integrations, AI behaviors, and the depth of verification drive the estimate. This engineering review is not a certification or a substitute for a separately commissioned specialist penetration test.
Bring a repository overview, architecture notes, a staging environment, known failures, and test accounts with appropriate permissions. Do not send credentials or customer data in the public contact form.
Timing is agreed after the scope and dependencies are understood. Access to source systems, review availability, procurement, and migration requirements can change the schedule.
Questions before scope
It is a human-led review of an AI-built application's code, data access, authentication, authorization, secrets, dependencies, integrations, failure paths, tests, and deployment configuration. Findings are tied to evidence and prioritized by risk rather than presented as a generic scanner report.
Yes. The review focuses on the generated and edited code that actually runs, plus the database, environment, integrations, and deployment around it. The builder used does not replace verification of the resulting system.
The initial assessment can usually begin with read-only repository access, architecture context, and a controlled view of relevant configuration. Write access is requested only if a remediation scope is approved.
Scanners are useful inputs for known patterns and dependency issues. A human codebase audit also follows business workflows, role boundaries, data access, deployment assumptions, and chained failure paths that a generic tool may not understand.
No. A codebase audit examines source, configuration, and trusted boundaries. A penetration test actively probes a running system under a separately authorized scope. Some products need both, but they answer different questions.
Yes. ZamDev AI is based in Lahore, Pakistan and works remotely with international teams. We agree meeting overlap, the decision-maker, a written review cadence, repository access, and deployment ownership during scoping. Tell us your time zone and any procurement, hosting, or data-location requirements so we can confirm the fit before a commitment. We do not represent a US or UK office.
Review the implementation trade-offs before deciding whether this engagement fits your product.
Share the current product, workflow, or repository and the decision you need to make. We will identify the useful next step, required access, deliverables, exclusions, and acceptance criteria.