Your AI prototype, made production-ready. Two phases, fixed price, clear timeline.
A working prototype is already the hard part. What follows is a structured, fixed-price engagement that adds everything production requires and hands it back to your team to run.
The Prototype Trap: the space between a successful demo and a system you can actually ship
It usually starts the same way. Someone on the team, a developer, an analyst, sometimes a non-technical operator, builds something useful with Cursor, Claude Code, and Codex. It works. People start using it. The right people see it, the budget gets approved, and suddenly there’s pressure to ship it properly. As vibe coding becomes standard practice, the gap between a working prototype and a production system is the next unsolved problem.
That’s when the gaps become visible.
No evaluation pipeline
No way to measure accuracy or catch regressions
No observability
No visibility into failures, latency, or decision paths
No CI/CD
Every deployment is a manual, undocumented risk
No security hardening
Prompts, secrets, and data boundaries left exposed
No cost controls
Token usage ungoverned, invoices unpredictable
No error handling
No recovery logic when the system behaves unexpectedly
| It doesn’t mean the prototype is broken. Rather that it’s not built for the environment it now needs to operate in. |
What We Deliver
Two phases. A clear path from prototype to production
Phase 1: Assessment & Architecture
Before anything gets rebuilt, you need to know what’s actually broken. Phase 1 is a structured audit of your existing prototype across six production-readiness dimensions and an honest answer to the question: is this worth productionising, or is a clean rebuild faster?
What you get:
- A full code and architecture audit of the existing prototype
- A gap analysis across six dimensions: evaluation & testing, observability, security, cost control, CI/CD, and error handling
- A production architecture blueprint – a detailed technical plan with technology choices, infrastructure requirements, and effort estimates
- A go/no-go recommendation, including the honest case for a clean rebuild if that’s the better path
Phase 1 is designed to be low-risk. The deliverable is a blueprint your team can act on whether you proceed with us or internally.
Phase 2: Production Transformation
Six production-readiness layers, added progressively. Every two weeks, the system becomes more robust.
Evaluation Pipeline
Automated regression testing, scenario-based validation, accuracy measurement against defined KPIs. You can prove the system works and know immediately when it breaks.
Observability Layer
LLM observability gives your team visibility into every decision the model makes, so failures become debuggable in minutes, not days.
Security Hardening
Identity propagation, PII masking, prompt injection protection, data boundary enforcement. The kind of setup your CISO can approve.
Cost Controls
Token usage dashboards, hard spending limits per user and feature, cost attribution per workflow, anomaly detection for runaway loops. No surprise invoices.
CI/CD for AI
Prompt version control, automated test gates before deployment, controlled rollouts, one-click rollback. Changes don’t silently break production.
Production Deployment
Deployed to your infrastructure, or DAC-managed, with monitoring, alerting, and documented runbooks.
Delivery Methodology
No rewrites from scratch. No black box. No surprises.
Audit first
Every engagement starts with understanding what the prototype does well, not by replacing it. The business logic and user value stay intact. What gets rebuilt is the production infrastructure around it.
Phased delivery
Phase 2 runs in two-week sprints, each adding a production-readiness layer. The system becomes progressively more robust, and you see tangible progress at every step.
Knowledge transfer built in
Every architectural decision gets documented. Joint code reviews happen throughout. The engagement ends with a handover session so your team can maintain and extend the system independently without coming back to us for every change.
Your people stay involved
You designate the original prototype builder as a point of contact as they know the intent and the edge cases. You designate a product owner to validate that the production system still delivers the intended value. One hour a week is all the time commitment required on your side.
Want to know where your prototype stands before committing to a full assessment?
Use VibeGuard – our free open-source audit tool covering a set of AI-powered subagents that run against your codebase, check it across 50 production-readiness criteria in six domains: data, operations, performance, quality, reliability, and security, and deliver a severity-ranked report with a remediation plan.
A full repository analysis for a medium complex app takes around 30 minutes and costs $8-10 in token usage. The output gives your engineering team a structured list of findings and your decision-makers an executive summary with a production readiness verdict.
You’ve already proven the concept? Let’s make it production-ready
Phase 1 is the right place to start. In one to two weeks, you’ll have an honest assessment of what’s worth keeping, what needs to be rebuilt, and a concrete technical plan for getting there. No commitment to Phase 2 required.
Examples of Vibe Code to Production services from DAC.digital
FAQ
Is vibe coding production-ready by default?
Vibe coding produces working prototypes fast. But vibe coding and production readiness are two different problems. The tools that make vibe coding possible – Cursor, Claude Code, Codex – are optimised for speed of creation, not for the operational infrastructure a production system requires. Getting from vibe code to production means adding the layer that the tools don’t build for you.
What is vibe coding and why is it a production risk?
Vibe coding refers to AI-assisted development where non-technical or semi-technical team members build functional applications using tools like Cursor, Claude Code, or ChatGPT, often without formal architecture, testing, or security practices. The result works as a demo but lacks the operational infrastructure required for production: no evaluation pipeline, no observability, no access controls, no cost governance. The risk isn’t the code itself, but everything that wasn’t built around it.
What’s the difference between an AI prototype and a production-ready AI system?
A prototype proves that a concept works under controlled conditions. A production system operates reliably at scale, handles edge cases and failures gracefully, protects sensitive data, stays within cost boundaries, and gives the team visibility into how it behaves at runtime. Most AI prototypes built with low-code or AI-assisted tools have the business logic but none of the surrounding infrastructure that makes a system safe to depend on.
How do I know if my AI prototype is ready for production?
If you can’t answer yes to all of the following, it probably isn’t:
Do you have automated tests that measure output accuracy?
Do you have end-to-end tracing that lets you debug failures without reading through logs manually?
Do you have hard spending limits enforced at runtime?
Do you have a rollback mechanism if a deployment breaks something?
Do you have documented access controls and data boundaries?
The absence of any one of these is a production blocker.
How long does it take to move an AI prototype to production?
It depends on the complexity of the prototype and the number of production-readiness gaps. A Phase 1 assessment takes one to two weeks and produces a concrete timeline. Phase 2, the actual transformation, typically runs six to twelve weeks. Simple single-agent prototypes with existing infrastructure land at the lower end. Multi-agent systems with enterprise integrations, complex security requirements, and infrastructure to provision from scratch take longer.
What does a production-readiness audit cover?
A thorough audit covers six dimensions: evaluation and testing (can you prove the system is accurate and catch regressions?), observability (can you trace failures and monitor performance?), security (are access controls, secrets, and data boundaries properly enforced?), cost control (is token usage governed and spending predictable?), CI/CD (can you deploy changes safely and roll back if something breaks?), and error handling (does the system recover gracefully from unexpected behaviour?).
Can an existing AI prototype be transformed, or does it need to be rebuilt from scratch?
In most cases, the prototype can be transformed rather than rebuilt. The business logic and user-facing behaviour are usually worth keeping. What’s missing is the production infrastructure around them. A Phase 1 assessment makes this call explicit: it identifies what’s worth preserving, what needs to be refactored, and whether a clean rebuild from the validated concept would be faster and cheaper than transformation.
What types of companies typically need this service?
Mid-market and enterprise companies in industries that have adopted AI tooling quickly but don’t have deep AI architecture or DevOps expertise in-house. Manufacturing, professional services, and other traditionally non-tech-intensive sectors where internal teams have built useful AI tools but lack the infrastructure knowledge to ship them safely. The common pattern: a useful prototype exists, the business case is proven, but engineering is stuck on the “how do we actually ship this” part.
What is an LLM evaluation pipeline and why does it matter?
An evaluation pipeline is an automated system that tests an AI application’s outputs against defined quality criteria, like accuracy, relevance, safety, consistency, on a continuous basis. Without one, there’s no reliable way to know whether the system is performing correctly, whether a change has introduced a regression, or whether the system has drifted from acceptable behaviour over time. For any AI application handling real business workflows, an evaluation pipeline is a prerequisite for production deployment.
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