Founders with a working AI-built prototype
Users understand the concept, but authentication, payments, data, security, reliability, or maintainability need professional engineering.
Vibe-Coded to Production
Keep the speed of AI-built software. Remove the production risk.
Automiq evaluates the application you already created, preserves the useful product learning, and hardens the code, data, identity, security, testing, deployment, and operating controls that real customers require.
AI-generated code is treated as inherited software: inspected and tested before anyone promises a rewrite or launch.
Business outcome
Users & workflow
Systems & constraints
Vibe-Coded to Production
Working capability
Production controls
Owned handover
- Engineering judgment
- Product-led
- Decisions account for adoption, support, and maintenance
- Delivery ownership
- Senior
- Product and architecture stay close to implementation
- Production behavior
- Observable
- Failures and quality signals remain visible
- Handover objective
- Portable
- Agreed code, access, documentation, and runbooks
Best fit
Who this service is for.
Fit depends on the business problem, access to decision-makers and representative data, and willingness to own the resulting product or workflow.
Teams inheriting generated code
A product exists without trustworthy architecture, documentation, tests, dependency ownership, or deployment controls.
Businesses preparing customer or investor release
The prototype must support real accounts, sensitive data, integrations, support, and predictable change.
The problem
Why otherwise promising initiatives stall.
These failure modes are resolved before scale amplifies them.
The happy path hides the architecture
Screens work, but state, permissions, error paths, data integrity, and background behavior are unclear.
Secrets and access are unsafe
Credentials reach the browser, authorization relies on UI state, or tenant boundaries were never tested.
Generated dependencies own the product
Packages, hosted backends, prompts, and copied code introduce licensing, update, or portability risk.
Every prompt changes something else
Without tests and boundaries, AI-assisted iteration makes regressions faster than feature delivery.
What we build
A complete production capability, not an isolated technical demo.
The exact scope is discovered with the customer; these are representative systems within this service.
Production-readiness audit
Code, dependencies, ownership, identity, authorization, data, APIs, security, performance, deployment, and recovery.
Targeted re-engineering
Replace unsafe or unmaintainable boundaries while preserving proven experience and functionality.
Quality and delivery foundation
Types, validation, tests, environments, CI/CD, observability, backup, release, and rollback.
Launch and ownership package
Production deployment, operational monitoring, documentation, access transfer, runbooks, and support path.
Practical use cases
Where this service creates useful leverage.
Use cases are selected by measurable workflow or product value—not by how fashionable the technology sounds.
Lovable or AI-builder MVP
Prepare a validated generated application for customer accounts, payments, data, and dependable iteration.
AI-generated internal tool
Add identity, permissions, audit, integrations, and recovery before the tool becomes operationally critical.
Prototype due diligence
Assess technical risk before funding, acquisition, customer rollout, or hiring an internal team.
Generated feature inside a live product
Isolate, test, and integrate AI-produced code without weakening the existing architecture.
Deliverables and ownership
What a production engagement should leave behind.
The engagement agreement defines exact ownership, but the delivery objective is an operable system and a practical path forward.
Prioritized audit report
Evidence, severity, business consequence, recommended treatment, and launch-blocking findings.
Remediated application
Agreed security, architecture, data, performance, accessibility, and maintainability improvements.
Test and release system
Automated checks, environments, deployment, monitoring, backup, rollback, and incident signals.
Maintainable handover
Repositories, dependency and licensing notes, architecture decisions, access, documentation, and walkthroughs.
Example architecture
A representative flow buyers can reason about.
This is an explanatory pattern, not a promise to force every project into the same components.
- Stage 01
Inspect
- Code and dependency provenance
- Identity, data, and authorization
- Deployment and failure behavior
- Stage 02
Stabilize
- Types, validation, and boundaries
- Secrets and permission enforcement
- Critical regression tests
- Stage 03
Re-engineer
- Unsafe services or data paths
- Performance and background jobs
- Portable infrastructure interfaces
- Stage 04
Operate
- CI/CD and environments
- Monitoring, backup, and rollback
- Documentation and change discipline
Build, buy, or integrate
When custom engineering makes sense—and when it does not.
A useful partner should help reject unnecessary custom work as clearly as it scopes justified work.
| Option | Best when | Main tradeoff |
|---|---|---|
| Continue with the AI builder | The product is still exploratory, data is low-risk, and platform limits do not block validation. | Maximum iteration speed with growing production and portability constraints. |
| Harden the existing application | The product works and most architecture can be secured, tested, and maintained. | Preserves learning, but inherited inconsistencies may remain. |
| Re-engineer critical boundaries | Identity, data, integrations, performance, or deployment cannot meet production requirements. | More effort, focused on the risk rather than a cosmetic rewrite. |
Automiq is probably not the right fit when:
- The product is still a disposable experiment with no validated user need.
- The buyer wants a security guarantee without audit access or remediation scope.
- Source code, dependencies, data, or platform access cannot be obtained.
- A complete rewrite is demanded for prestige rather than evidence.
Delivery method
From evidence to production in reviewable increments.
The method scales to the work. A bounded integration uses a lighter version than a multi-workflow platform, but the control points remain visible.
- 01
Scope & discovery
Map users, workflows, constraints, success measures, and the smallest valuable production milestone.
Outcome: Prioritized scope and delivery plan
- 02
Data & architecture
Audit systems, integrations, data quality, security boundaries, and the architecture the future team can maintain.
Outcome: Architecture and risk register
- 03
Prototype & evaluate
Test the riskiest assumptions against representative data, measurable acceptance criteria, and real user feedback.
Outcome: Evidence-based go or adjust decision
- 04
Build & integrate
Ship in reviewable increments with testing, access controls, observability, documentation, and clear ownership.
Outcome: Production-ready software
- 05
Deploy & hand over
Release progressively, monitor real usage, train operators, and transfer repositories, infrastructure, and runbooks.
Outcome: Controlled launch and clean handover
- 06
Support & grow
Maintain reliability, refine workflows, manage dependencies, and keep shipping as the product and business evolve.
Outcome: A stable platform that keeps improving
Production safeguards
Failure handling is part of the feature.
Safeguards are selected by consequence and operating environment, then tested before broad release.
Authorization verification
Server-side permissions and tenant boundaries are tested independently of the interface.
Dependency and secret control
Licenses, updates, credentials, generated artifacts, and third-party ownership are inventoried and corrected.
Regression protection
Critical journeys, APIs, data rules, and security checks protect future AI-assisted changes.
Recoverable releases
Separate environments, versioned deployment, backup, monitoring, and rollback support controlled iteration.
Technology
Tools selected for this workload—not a mandatory agency stack.
These technologies are relevant to the service. Final architecture depends on the customer’s existing environment, risk, team, and handover needs.
business platform
Lovable
Audit, secure, and scale applications built with Lovable
Explore Lovableweb mobile
React
Custom React application development
Explore Reactweb mobile
Next.js
Production Next.js application development
Explore Next.jsweb mobile
TypeScript
TypeScript product and platform engineering
Explore TypeScriptcloud data
Supabase
Supabase application development and production hardening
Explore Supabasecloud data
PostgreSQL
PostgreSQL architecture, migration, and application development
Explore PostgreSQLdelivery
Docker
Docker application containerization and production delivery
Explore DockerInternational delivery
Vibe-Coded to Production across regions and operating markets.
Remote delivery is scoped around the customer's jurisdiction and operating language rather than assuming one global configuration.
Regional system terms
Align the names used by founders with ai-built products and teams with unstable prototypes for roles, records, states, dates, addresses, currencies, taxes, units, and exceptions.
Data and provider geography
Confirm hosting and model regions, data residency and transfers, subprocessors, customer access, retention, deletion, and recovery objectives.
Working model
Agree time-zone overlap, decision owners, language, procurement, release windows, incident escalation, support responsibility, and handover location.
Timeline
A sequence defined by evidence, dependencies, and risk.
Automiq does not publish one universal duration. Discovery establishes a bounded milestone and confirms the decisions required to reach it.
- Audit · 01
Establish evidence
Inspect code, access, data, dependencies, runtime, deployment, and critical user journeys.
- Triage · 02
Separate launch blockers from improvements
Prioritize findings by consequence and define retain, remediate, or replace decisions.
- Harden · 03
Engineer the production boundaries
Fix access, data, tests, integrations, performance, deployment, and operations.
- Launch · 04
Release with visibility
Deploy progressively, monitor behavior, close documentation, and agree continuing ownership.
Investment context
What changes the size of the engagement.
A credible estimate follows workflow, architecture, integration, data, risk, and release discovery—not a generic page-based package.
Audit before estimate
Generated applications vary widely; credible scope follows access and evidence.
Risk matters more than line count
Identity, payments, sensitive data, integrations, and migration increase remediation depth.
Preservation reduces waste
Keeping sound product and code decisions can cost less than an unexamined rewrite.
No price or timeline on this page is a quote. Commercial scope is documented after discovery and depends on the agreed milestone and responsibilities.
Relevant experience
Product context behind the engineering approach.
ATZ CRM is adjacent founder experience operating and continuously changing production SaaS. It is not claimed as a vibe-code rescue project.
Product visual
founded
ATZ CRM
Recruitment · B2B SaaS experience involving AI, Web app, Workflow automation, CRM integrations.
- Recruitment
- B2B SaaS
Questions, answered
Vibe-Coded to Production questions, answered
Direct answers about fit, architecture, ownership, risk, and delivery.
What is a vibe-coded application?
It is software created largely through natural-language prompts and AI coding or app-builder tools. The term describes the creation method, not whether the resulting code is safe or unsafe.
Does AI-generated code need to be rewritten?
Not automatically. Code should be inspected and tested. Sound components can remain; unsafe, opaque, or unmaintainable boundaries should be remediated or replaced.
What does a production-readiness audit cover?
Ownership, dependencies, licenses, identity, authorization, data integrity, APIs, secrets, security, tests, performance, accessibility, environments, deployment, monitoring, backup, and rollback.
Can Automiq work with Lovable and Supabase applications?
Yes. The catalog includes Lovable and Supabase experience for auditing, hardening, extending, or migrating applications when access and project fit are confirmed.
Can we keep using AI coding tools afterward?
Yes, with engineering controls. Small reviewable changes, tests, typed boundaries, code review, dependency policy, and monitored releases let AI assistance improve speed without owning product decisions.
Talk to the engineering team
Discuss a vibe-coded to production requirement with the team.
Bring the current workflow, product, systems, constraints, and desired outcome. We will help define the first useful production milestone.