AI app production readiness audit, before your users find the gaps.
A structured review of an AI-built app that ends with a written report ranking every finding Critical, High, Medium, or Low. We combine automated scanning with a senior engineer's review, because scanners cannot judge whether your rules protect the right data.
What is an AI app production readiness audit?
An AI app production readiness audit reviews an AI-built codebase against the failure patterns AI tools repeat most: exposed secrets, authorization that exists only in the interface, unverified payment webhooks, hallucinated or outdated dependencies, and tests that confirm the AI's own assumptions.
The audit combines static application security testing, software composition analysis, and secret scanning with manual review by a senior engineer. Veracode's 2025 GenAI Code Security Report tested over 100 large language models and found 45% of code samples contained an OWASP Top 10 vulnerability.
- Scanners first
- Exposed keys and known vulnerable packages, caught fast.
- Engineer second
- The judgment automation cannot provide on its own.
- Ranked, not listed
- Critical, High, Medium, Low — so you know what to fix first.
- Any tool
- Lovable, Bolt.new, Base44, Cursor, v0, Replit and more.
Three approaches, three depths of problem caught.
Teams check readiness through one of these, and each stops at a different layer.
| Approach | What it delivers | Where it falls short |
|---|---|---|
| DIY scanner + checklist | A free sanity check in 1 to 2 hours: exposed keys, known vulnerable packages. | Cannot judge whether authorization rules protect the right data. |
| Human-led audit | Scanning plus senior engineer review, with a severity-ranked report and roadmap. | Tells you what to fix; your team still does the fixing. |
| Audit + hardening sprint | The audit, plus implementation and verification of every Critical and High finding. | Takes longer and costs more than a standalone report. |
A finished-looking UI doesn't mean production-ready.
Where AI-built apps quietly stop short, and which engagement actually fits you.
AI apps usually implement authentication and assume authorization
Controls get hidden in the interface without being enforced on the server. If the agent cannot point to the rule that enforces ownership, that rule does not exist. We test authorization directly against your APIs and data, not by clicking through screens.
Audit only, or audit plus hardening sprint?
Choose an audit only if your team can implement the roadmap itself. Choose the hardening sprint if you have no engineer to fix Critical findings before launch, or if a customer or investor is waiting on the result.
Why audit before launch, not after the first incident.
Fixing a Critical finding before real users arrive costs a fraction of fixing it after their data is exposed.
Data-leak risks found first
Auth-bypass gaps caught before real customers depend on your app.
Findings ranked, not dumped
So your team fixes the most dangerous three first.
Buyer questions answered
The security answers enterprise procurement and investors ask for.
Silent packages caught
Hallucinated and outdated libraries AI agents install without warning.
Your AI features reviewed
Not only the code that built the app, but the models inside it.
Evidence you can bring
Findings mapped to controls for a SOC 2 or ISO 27001 process.
The 6 areas every AI app audit covers.
We scope depth by codebase size, integrations, and whether money or personal data moves through the app.
Secrets & configuration
API keys, tokens, and environment settings, including keys embedded in client-side bundles.
Authentication & authorization
Session handling, server-side role checks, tenant isolation, and row-level security reviewed as separate tests.
Payments & webhooks
Signature verification, idempotent handlers, and failure paths for Stripe and similar providers.
Data & dependencies
Input validation, database access patterns, and a package review for outdated or hallucinated libraries.
Tests & deployment
Whether tests validate real behavior, plus environment separation, backups, monitoring, and rollback.
AI features inside your app
Prompt injection, secret exposure to the model, tool-call scope, and untrusted model output.
Signs you need an AI app audit.
Four situations send most founders and teams to us.
- 01
Real users arrive soon
Launch is weeks away, and nobody has reviewed the generated code line by line.
- 02
Money or personal data moves through it
Payments, financial records, or personal information raise the cost of every gap.
- 03
A buyer or investor asks about security
Enterprise procurement or technical diligence needs written answers you cannot yet give.
- 04
Nobody on the team reads the code
The AI wrote it, the founder shipped it, and no engineer has reviewed it.
Common readiness mistakes we help you avoid.
These five mistakes account for most of the AI-built apps that fail a first serious review.
Trusting a finished-looking interface
CriticalThe UI can look complete while the backend has no enforcement. We audit the layer users never see.
Treating authentication and authorization as one check
CriticalLogin working says nothing about who can read what. We test each separately.
Testing only by clicking the interface
CriticalDirect API requests skip the interface entirely. We test every privileged action against the API and data layer.
Letting the AI grade its own work
HighAI-written tests often confirm the AI's own assumptions. We check whether tests validate real requirements.
Ignoring packages the agent installed
MediumAgents add outdated or invented dependencies without warning. We review every package against known vulnerabilities.
How we run your audit.
Six stages, from first call to a ranked roadmap. We keep you informed with full visibility throughout.
Scoping call & fixed quote
We confirm repositories, integrations, and data sensitivity, then quote a fixed price.
1 day · ScopingRead-only access & automated scan
We run static analysis, dependency, and secret scans against your repository and configuration.
1–2 days · ScanningSenior engineer review
We test authorization, architecture, payments, and AI features by hand where scanners cannot.
3–7 days · ReviewSeverity-ranked report
We rank every finding Critical, High, Medium, or Low, with a prioritized remediation roadmap.
1–2 days · ReportWalkthrough call
The engineer who reviewed your code walks your team through every finding.
1 hour · WalkthroughOptional hardening sprint
We fix and verify every Critical and High finding, if you want us to.
2–3 weeks · HardeningAI app audit cost and timeline.
Three factors drive the price: how many repositories you have, how many integrations are wired in, and whether payments or sensitive data are involved. You receive a fixed quote before work begins. Hosting and third-party tool costs are billed separately by those providers.
- Investment
- $1,500–$3,000
- Timeline
- About 5 business days
- Scope
- Single app, one repository
- Method
- Scans plus engineer review
- Output
- Severity-ranked report
- Investment
- $3,000–$7,500
- Timeline
- 1–2 weeks
- Scope
- Multiple repos & integrations
- Depth
- Payments, AI & tenant isolation
- Compliance
- Control mapping included
- Investment
- $7,500–$15,000+
- Timeline
- 3–5 weeks
- Scope
- Full audit, then implementation
- Fixes
- Every Critical & High finding
- Proof
- Each fix verified & documented
The methods behind your audit.
Automated coverage first, then human judgment where automation stops.
OWASP Top 10
SupabaseWays to work with us.
Pick the model that fits where your app stands today. All are fixed-scope with no long-term lock-in.
Standalone Audit
A written report and roadmap your own team implements.
Best with an in-house engineerAudit + Hardening Sprint
The audit, followed by fixes and verification of every Critical and High finding.
Best with no engineer to fixPre-Launch Re-Audit
A focused second pass after a major regeneration or feature release.
Best before launch dayCompliance Evidence Pack
Findings mapped to SOC 2 and ISO 27001 controls for your certification process.
Best for a certification runEvery audit comes complete.
No hidden gaps. Each audit includes everything you need to act on the findings.
- Fixed quote
- A price agreed before work begins, unchanged mid-review.
- Automated scans
- Static analysis, dependency, and secret scanning across your repositories.
- Engineer review
- Manual testing of authorization, payments, and architecture.
- Severity-ranked report
- Every finding rated Critical, High, Medium, or Low.
- Remediation roadmap
- A prioritized order for fixing, with effort noted per item.
- Walkthrough call
- The reviewing engineer explains every finding to your team.
- Read-only access
- We never need write access to your production environment.
- NDA
- Signed before you share any code or configuration.
AI app audits across every kind of product.
The method stays the same. The depth changes with what your app handles.
Pre-Launch Founders
First-time launches that need an independent check before real users arrive.
B2B SaaS Startups
Teams facing security questionnaires from enterprise buyers.
Fintech & Payments
Apps where webhook and authorization gaps carry direct financial risk.
Health-Adjacent Products
Products handling sensitive personal data that need a gap assessment first.
Marketplaces
Multi-sided apps where tenant isolation decides who sees what.
Agencies Delivering Client Apps
A second pair of eyes before a client-facing AI build ships.
Investors & Acquirers
Technical diligence on AI-built products before a deal closes.
Internal Tools Teams
Employee-data apps built fast that nobody has reviewed for access control.
Explore more Software Development services.
An audit pairs naturally with these services from Hoop Interactive.
Vibe Coding Agency
The full cluster of AI app build, rescue, and migration services.
ExploreLovable Development
Building on Lovable with review from the first prompt.
ExploreLovable App Rescue
When the findings are already breaking the app.
ExploreBolt.new Development
Framework-flexible builds that export cleanly to GitHub.
ExploreBolt.new App Rescue
Rescue for an app stuck between preview and production.
ExploreBase44 Development
Zero-config speed, with the lock-in stated upfront.
ExploreBase44 App Rescue
Stopping credit burn and closing server-side auth gaps.
ExploreDevOps Services
The monitoring and rollback an audit checks for.
ExploreAI app audit questions
The questions founders and teams ask us most before booking an audit.
An AI app production readiness audit is a structured review of an AI-built or AI-assisted codebase that combines automated scanning with senior engineer review. It ends with a written report that ranks every finding as Critical, High, Medium, or Low and includes a prioritized remediation roadmap.
We audit apps built with Lovable, Bolt.new, Base44, Cursor, v0, Replit, Claude Code, and GitHub Copilot. The audit follows the code and configuration, not the tool, so mixed stacks and hand-edited projects are in scope.
45% of AI-generated code samples contained a vulnerability from the OWASP Top 10, according to Veracode's 2025 GenAI Code Security Report, which tested over 100 large language models.
An AI app production readiness audit costs $1,500 to $15,000 or more, depending on scope. A readiness scan for a single app costs $1,500 to $3,000, a full production audit with integrations and payments costs $3,000 to $7,500, and an audit with a hardening sprint costs $7,500 to $15,000 or more.
A readiness scan delivers its report in about 5 business days, a full production audit takes 1 to 2 weeks, and an audit with a hardening sprint takes 3 to 5 weeks depending on how many findings need fixing.
You receive a written report listing every finding by severity, a prioritized remediation roadmap, and a walkthrough call with the engineer who reviewed your code. The quote is fixed before work begins and does not change mid-review.
Yes, as an optional hardening sprint. The audit stands alone, so you can hand the roadmap to your own team, or we implement the Critical and High findings and verify each fix.
No. Scanners catch exposed keys and known vulnerable packages, but they cannot judge whether authorization rules protect the right data or whether the architecture holds under real load. We run scanners first, then a senior engineer reviews what they cannot see.
Yes. We check whether users can inject instructions into prompts, whether the model sees secrets or private data, whether tool calls are scoped, and whether model output is treated as untrusted text.
No. Our audit is not a SOC 2 or ISO 27001 certification, which requires an accredited auditor. We map our findings to relevant controls, such as SOC 2 change management and ISO 27001 secure coding, so you arrive at that process with evidence and known gaps.
We need read-only access to your repository and configuration, and we never need write access to production. We sign an NDA before you share anything.
An audit tells you what is wrong and in what order to fix it, before or after launch. An app rescue repairs an app that is already broken, stuck in a fix loop, or exposed. Many rescues start with an audit.