01
Instant generation
A whole market of tools turns natural-language prompts into working code.
- Cursor
- Claude Code
- Lovable
- Bolt
- Windsurf
- Replit
10 · Vibe to Production
nehlum Vibe to Production
You built it with AI. We make it production-grade.
We take the code your team already vibe-coded and transform it — hardening, refactoring and engineering it into a secure, scalable, compliant system ready for real deployment. Your product, kept and improved — not thrown away and rebuilt.
- Keep your codebase
- Transform, don't restart
- Deployment-ready
11
Validation layers
7
Layers reviewed
50+
Failure modes
5–7
Days to report
01THE NEW REALITY
Everyone is shipping AI-built software
In months, AI coding tools went from novelty to default. Anyone can turn a prompt into a running application — and the output looks finished.
02
It looks finished
The demo runs, clicks through, and screenshots beautifully — in hours, not sprints.
03
Expectations jump
Leadership sees the demo and assumes it is ready to ship to real users.
What they see
A demo in minutes
What it actually takes
Months of enterprise engineering
03THE HIDDEN COST
The problems most vibe coders never see
Seven layers, more than fifty failure modes — all invisible in a working demo, until they reach production.
7
Layers
50+
Failure modes
Architecture
06 risks- Tight coupling
- No modular design
- No scalability planning
- Single points of failure
- Monolithic apps
- Missing design patterns
Security
08 risks- Broken authentication
- Authorization flaws
- JWT & session hijacking
- XSS · CSRF · SQLi · SSRF
- Remote code execution
- Prompt injection & jailbreaks
- Secret leakage
- Weak encryption
Database
07 risks- Missing indexes
- No migrations
- No rollback strategy
- Slow queries & locking
- Missing backups
- Poor transactional integrity
- Data corruption
APIs
07 risks- No versioning
- No rate limiting
- Missing retries
- Poor error handling
- Insecure endpoints
- Breaking changes
- No observability
Infrastructure
07 risks- Single-server deploys
- No load balancing
- No high availability / DR
- No monitoring or logging
- Manual deployments
- Misconfigured K8s / Docker
- Networking & firewall gaps
AI systems
08 risks- No RAG evaluation
- Prompt injection attacks
- Hallucinations
- Model drift
- No guardrails
- Poor vector DB design
- Uncontrolled token costs
- No AI governance
Enterprise ops
07 risks- No CI/CD
- No automated testing
- No code reviews
- No release management
- No SLA monitoring
- No incident response
- No change management
Every layer is a path to an incident or outage.
04WHAT WE FIX
From common risk to production-ready
Twelve failure patterns we find in almost every AI-built codebase, and the engineering that resolves each one.
| # | Common risk of vibe coding | How nehlum solves it |
|---|---|---|
| 01 | Broken authentication & authorization | Enterprise identity — RBAC, MFA, SSO, OAuth2, Azure AD |
| 02 | Security vulnerabilities (OWASP Top 10) | Secure-by-design architecture plus penetration testing |
| 03 | Exposed API keys & secrets | Secrets management — Vault, Key Vault, AWS Secrets Manager |
| 04 | Doesn't scale beyond a few users | Cloud-native, containerized, horizontally scalable architecture |
| 05 | No CI/CD, little or no testing | Automated testing, deployment, rollback & release management |
| 06 | Hallucinated or incorrect AI logic | AI evaluation frameworks, guardrails & human oversight |
| 07 | Compliance risks | GDPR, ISO 27001, SOC 2, NCA, SAMA & PDPL support |
| 08 | Poor database design | Optimized schemas, indexing, normalization & disaster recovery |
| 09 | Insecure APIs | Secure REST/GraphQL, API gateways, rate limiting & versioning |
| 10 | Performance bottlenecks & high cloud costs | Load testing, caching, observability & FinOps optimization |
| 11 | Missing audit trails & technical debt | Full audit logs, governance & architecture modernization |
| 12 | No monitoring, backup or disaster recovery | Centralized logging, metrics, tracing, backup & recovery |
05THE NEHLUM METHOD
Eleven validation layers
Eleven validation layers that turn AI-generated code into enterprise-ready software. Every layer is reviewed, remediated and signed off before anything ships.
-
01
Architecture
Modular, scalable, resilient system design
-
02
Security
Secure-by-design & penetration tested
-
03
Infrastructure
Cloud-native, high availability & DR
-
04
Database
Schemas, indexing, migrations & backups
-
05
APIs
Gateways, versioning & rate limiting
-
06
AI models
Guardrails, evaluation & governance
-
07
Performance
Load testing, caching & FinOps
-
08
Compliance
SOC 2, ISO 27001, SAMA & PDPL
-
09
Deployment
CI/CD, rollback & release management
-
10
Monitoring
Logging, metrics & distributed tracing
-
11
Support
SLAs, incident response & governance
Production-ready. Every layer validated and signed off before release.
06HOW WE ENGAGE
A four-phase path to production
One sequence, from first look at the codebase to running it in production with you.
-
STEP 01
Assess
Audit the AI-built codebase across all layers. Deliver a prioritized risk report.
-
STEP 02
Engineer
Remediate architecture, security, data and infrastructure to enterprise standard.
-
STEP 03
Harden
Testing, compliance, load & penetration testing, backup and disaster recovery.
-
STEP 04
Operate
CI/CD, monitoring, incident response, support and ongoing governance.
07AUDIT-ONLY SERVICE
READ-ONLY · NO CODE CHANGES
Just need an expert opinion? Start with an audit
A read-only assessment of your AI-built codebase. We assess, score and recommend — we never change your code. You keep full control of what to fix and when.
What we audit
- Architecture, security & data layers
- Vulnerability & dependency scan (OWASP)
- Scalability & performance review
- Compliance gap check — NCA, SAMA, PDPL
- AI model, guardrail & cost review
What you get
- Prioritized risk report with severity scores
- Actionable remediation recommendations
- Effort & impact estimate per issue
- Executive summary & readiness scorecard
- Findings walkthrough call with our engineers
How it works
-
01 Fixed scope & timeline
A defined, time-boxed engagement.
-
02 Zero disruption
Read-only access — your code stays untouched.
-
03 You decide next steps
Fix in-house, or engage us to remediate.
Not ready to refactor? Start here.
An independent, no-commitment health check — then decide with the full picture in hand.
Ideal for
- Pre-launch reviews
- Investor due diligence
- Board assurance
- Post-incident checks
09QUESTIONS
Frequently asked
The questions engineering and product leaders ask us before an assessment.
What is Nehlum Vibe to Production?
It is an engineering service that takes an application your team built with AI coding tools and makes it production-ready. We audit it across eleven validation layers, then remediate architecture, security, data, infrastructure and AI guardrails. You keep your codebase — we transform it rather than rebuild it.
Do you rewrite our code from scratch?
No. The starting position is always that your product is kept and improved. We refactor and harden what already exists, and only replace a component when keeping it would cost more than rebuilding it — a decision we bring to you with the evidence, never one we make quietly.
Can we start with just an audit?
Yes. The audit-only engagement is read-only: fixed scope, no code changes, and a prioritized risk report with severity scores within five to seven business days. If you then choose to remediate with us, 100% of the audit fee is credited against that work.
How do you protect our intellectual property?
A mutual NDA is signed before you share a single file. Access is least-privilege and time-limited, granted only to the assigned engineers inside an isolated environment. We never train models on your code and never share it with third parties, and every copy is securely destroyed at close — confirmed in writing. You retain all IP and ownership.
Which compliance frameworks do you cover?
We assess and remediate against GDPR, ISO 27001 and SOC 2, and against the Saudi frameworks that apply locally — NCA controls, SAMA requirements and PDPL data handling. Every compliance gap is reported with an effort and impact estimate so you can sequence the work.
What does an AI-specific review cover that a normal code audit does not?
Prompt injection and jailbreak exposure, hallucination and model drift, missing guardrails, RAG and vector-database evaluation, uncontrolled token spend, and the absence of AI governance — the failure modes that only surface once a model is serving real users.
Book a production-readiness assessment
AI can write code in minutes. Enterprise software takes engineering. Let's make your AI-built application secure, scalable and compliant — before it becomes your next incident or outage.