AI prototype to production: How agencies turn Lovable, Bolt, Cursor, and v0 builds into client-ready deliverables

Gartner expected 30% of gen AI projects to stall at PoC in 2025. The reality was steeper: over half of AI prototypes never reached production, blocked by gaps in architecture, security, testing, and maintainability. Based on our delivery experience, we’ve compiled practical steps to move AI prototypes into production and extend their lifecycle beyond launch.

What “AI prototype to production” means for agencies and how to get there

When working on an AI concept, agencies  turn a demo that works in a single scenario into a system that holds up under real users, client scrutiny, change requests, and team handoffs. If a prototype proves an idea, a production deliverable proves reliability, security, and maintainability.

Prototype, MVP, and production are not the same

Prototype, MVP, and production are all different delivery modes for agencies, each with certain  expectations,   client obligations, and risks. Agencies are often handed something between a prototype and an MVP and still expected to turn it into a production-ready solution.

Why AI-generated builds feel further along than they are

AI-generated builds feel further along because the UI is assembled in hours, especially in client-facing demos where “something working” is enough to move forward. Happy-path demos reinforce this confidence. They hide edge cases, failures, and data inconsistencies until usage begins. Non-visible systems, like architecture, validation, security, environments, and observability are missing, yet they determine whether the build can ship.

Where Lovable, Bolt, Cursor, and v0 builds break before they reach production

Across GetDevDone projects, we often inherit prototypes built with Lovable, Bolt, Cursor, v0, and similar generators that look ready but cover only part of the delivery scope. When pushed live as-is, they turn into ongoing fixes, manual workarounds, and support overhead that erodes margins and slows every release.

Fragmented architecture and logic

AI-generated builds mix concerns throughout the codebase: UI logic sits next to data fetching, business rules are buried in components, and state handling is brittle. That works in a demo. But it becomes an expensive exercise to track and fix a dozen unrelated documents and files if a client requests a feature change three months post-launch. 

Weak or missing security controls

With AI-generated work, authentication is often demo-grade or missing, input validation is skipped, and dependencies go unmanaged. For agency clients in fintech, healthcare, and enterprise procurement, it leads to failed security reviews and GDPR exposure that delay sign-off or terminate contracts.

Lack of test coverage

AI-based prototypes ship with little to no test coverage. There are oftenunit tests for business logic, integration tests for API contracts, or performance benchmarks missing. The first time a regression surfaces is usually in a client-facing environment and, unfortunately, in most cases, it doesn’t look good.

Unstructured release process

AI-produced builds run locally without CI/CD (Continuous Integration and Continuous Delivery or Deployment) pipeline or staging environment. Deploying them means manual steps that introduce risk every time. 

Limited observability and monitoring

Logging, error tracking, uptime monitoring, and alerting are also often absent when machines produce unsolicited prototyping. When an incident occurs, teams lack the data needed to explain impact, timing, and root cause to the client.

Low maintainability

Undocumented code, missing type safety, inconsistent naming, and unclear module boundaries make onboarding slow and expensive for any incoming developer. For clients whose internal teams will take ownership, this becomes a handoff risk that directly reflects on the agency that delivered the build.

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GetDevDone delivered a production-ready WordPress site from a Lovable prototype in under 3 weeks

An agency needed to take a 12-page Lovable prototype into production. The client had already approved the design and page structure. However, Lovable outputs React apps, not WordPress themes. The production site had to be built separately, with content migrated into editable Gutenberg blocks, forms integrated, SEO configured, and the site deployed on managed hosting that the client’s team could update independently.

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The real cost of delivering AI prototypes as-is

Under delivery pressure, agencies need to move shiny and working AI prototypes into production to meet deadlines and client expectations. In practice, the rush leads to damaged client trust, blown scopes, support overhead, and margin erosion.

Launch failure & reputation damage

Across GetDevDone projects, we’ve seen prototype-grade builds fail under real traffic during client demos or go-live moments. A failed launch in front of stakeholders often leads to lost trust, delayed rollout by 2-4 weeks, and immediate pressure to re-scope delivery.

Hidden rework after approval

Sometimes we inherit builds approved at the demo stage, where the underlying code was never audited or production-ready. It results in 30-50% additional unplanned work after sign-off, eroding margins and forcing budget renegotiation.

Change requests expose fragility

The first client-requested change reveals tightly coupled logic and unstable integrations produced by the AI. Tasks estimated at 1-2 days expand into 1-2 weeks, slowing delivery velocity and increasing internal costs without additional revenue.

Handoff failure & ongoing support drain

Agencies might transfer ownership of AI-based builds that lack structure, documentation, and consistency. This leads to repeated client escalations, 10-20+ hours of monthly support overhead, and prolonged involvement beyond the original contract.

Procurement & enterprise readiness blocks

For enterprise clients, AI-generated builds fail security and compliance reviews due to missing controls and documentation. It delays go-live by several weeks or cancels deals entirely, shifting accountability and recovery effort back to the agency.

Move from AI prototype to production-ready delivery without rework, support overhead, or missed deadlines. Contact us

How to decide what to keep, refactor, or rebuild before moving an AI prototype to production

As the experience shows, the fastest path to production is selective salvage, instead of preserving everything that was generated or running a full rewrite that doubles the timeline. Every component of an AI-generated build requires a triage decision before work begins.

What to keep

UI shells, approved page flows, validated content models, and low-risk components that do not touch sensitive logic are usually worth keeping. If the client has already signed off on screens and layout, preserving that work saves time and avoids revisiting approved decisions.

What to refactor

Business logic, API integrations, prompt orchestration, schema design, and permission structures typically need to be refactored rather than thrown out. The underlying intent is sound; the implementation needs to be made testable, isolated, and robust enough to handle failure states.

What to rebuild

Authentication, payments, sensitive data handling, deployment infrastructure, monitoring setup, and any unstable core architectural layer require a proper rebuild from the ground up. Patching them post-launch is always slower and more expensive than doing them properly once.

Prototype triage matrix

Area	Typical AI-build state	Risk to your delivery	Agency decision
UI/screens	Often solid, approved by the client	Low if logic is cleanly separated	Keep: migrate or restyle as needed
User flows/navigation	Mostly usable, some gaps	Low or medium	Keep with the QA pass
Business logic	Intertwined with UI, poorly separated	High; breaks under edge cases	Refactor: isolate and test
API integrations	Happy-path only, no error handling	High	Refactor: add retries, auth, error states
Authentication	Often demo-grade or missing	Critical	Rebuild
Data schema/DB	Unscaled, no migrations strategy	High	Refactor or rebuild depending on complexity
Payments/sensitive data	Placeholder or absent	Critical	Rebuild: use audited libraries
CI/CD and environments	None, runs locally only	Critical for delivery	Rebuild: set up from scratch
Security (secrets, validation)	Hardcoded or skipped	Critical for enterprise clients	Rebuild: full audit required
Test coverage	Absent or minimal	High, invisible until post-launch	Build: unit, integration, E2E
Monitoring/observability	Absent	High post-launch	Build: logging, alerting, uptime
Documentation	Absent	High for client handoff	Build: inline docs, handoff pack

When an agency needs help taking an AI prototype to production for client delivery

Agencies bring in a specialist partner when deadlines are fixed, and internal teams lack full production engineering coverage. Clients often arrive with Lovable, Bolt, Cursor, or v0 demos that look complete but cannot support a reliable launch.

While agencies own the client relationship, backend, DevOps, or security requirements extend beyond their core delivery capability. As a result, prototypes fail technical or procurement reviews, requiring fast remediation to avoid delays and client escalation.

GetDevDone provides  AI engineering services for agencies, covering AI prototypes to production, embedded AI features, and unstable AI build rescue. We operate as a white-label engineering partner, delivering the production layer under the agency’s brand and timelines.

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GetDevDone turned AI-assisted ideation into a production-ready Next.js site

A startup came in with Lovable and v0 experiments that showed direction but lacked design clarity and system consistency. We used Stitch and Figma Make to generate layout options, then turned the selected direction into a structured Figma design system. From there, we built and shipped a production Next.js site with Sanity CMS, with full documentation and a deployment pipeline in place.

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6 steps for agencies: From AI prototype to production-ready delivery

At GetDevDone, we structure engagements as a production-readiness sprint followed by hardening, validation, launch, and handoff. We follow this sequence consistently, with each phase delivering defined outputs so agencies know exactly what they get and when.

Step 1. Production-readiness assessment

This is where the real state of the build becomes visible. By reviewing architecture, security, testing, infrastructure, code quality, and documentation, the expert team identifies what needs fixing and lays out a prioritized plan to get the project production-ready.

Step 2. Architecture and code hardening

Our engineering team refactors or rebuilds the components flagged in the assessment. Business logic gets isolated and tested. API integrations get error handling and retry logic. Component boundaries get cleaned up. The codebase moves from something that works in demo conditions to something that performs under production load and edge cases.

Step 3. Infrastructure, environments, and CI/CD

It begins with staging and production alignment, so what’s tested behaves the same in the real world. With that foundation in place, CI/CD pipelines, infrastructure-as-code, and rollback procedures ensure every release is controlled, stable, and reversible. The client’s build stops existing only on a local machine and starts existing in a real, deployable system.

Step 4. QA and acceptance criteria

With the foundation in place, testing is introduced where it matters most. Business logic, APIs, and critical user journeys are covered, and performance is baselined. Only once clear acceptance criteria are met does anything move into a client-facing environment. 

Step 5. Launch support and stabilization

The real test begins after launch. As real users start interacting with the system, the first 72 hours become the highest-risk window. With close monitoring, rapid response, and continuous environment checks, issues are caught early and kept under control.       

Step 6. Documentation and client handoff

The final stage focuses on a clean handoff. A complete documentation package is delivered, covering repository standards, deployment runbooks, environment variables, integrations, and onboarding guidance for the client’s team. Code ownership transfers smoothly, allowing the agency to close the project while the client’s developers can maintain and extend the product independently.           

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GetDevDone converted a Bolt eCommerce prototype into a production-ready Shopify storefront

A client came in with a Bolt-generated prototype that captured layout and user flows but was not connected to real commerce systems. The AI-based prototype did not support catalog, payments, or inventory. We built a custom Shopify Liquid theme matching the approved design, migrated the catalog, and configured payments and shipping.

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What agencies should expect in the final deliverable

A client-ready deliverable is a codebase with clear standards and predictable structure. It comes with a usable deployment pipeline, test coverage that catches regressions early, and monitoring that surfaces issues in real time. Documentation ensures a clean handoff and removes dependency on the original team. 

At GetDevDone, that means:

• Repository: typed codebase, consistent naming conventions, documented module boundaries
• Deployment: staging and production environments, CI/CD pipeline, rollback procedure
• Testing: unit, integration, and E2E test suite with coverage baseline
• Monitoring: error tracking, uptime alerting, performance baseline
• Documentation: inline code docs, deployment runbook, third-party integration map, developer onboarding guide
• Handoff: code ownership fully transferred, access credentials documented, no loose ends

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