AI-Generated Code: From Demo to Production
Last verified: June 2026· engagement
You built the prototype in a weekend with Lovable, Bolt, v0, or Replit Agent. We take it to production — auth (SSO + RBAC), data (RLS + migrations + backups), APIs (versioning + rate limits), observability (cost + latency + errors), and the security + compliance the demo doesn't have. Same engineers we use for the AI-coding rollout and LLM cost work.
- Outcome
- A documented, production-grade codebase your team owns
- Timeline
- 3–8 weeks
- Pricing
- $25–80k fixed, or 10% of first-year production revenue uplift
- Buyer
- CTO, VP Eng, Head of Platform (any team that shipped a Lovable / Bolt / v0 / Replit prototype)
The problem
You built a working prototype in a weekend with Lovable, Bolt, v0, or Replit Agent. The demo is impressive. Production is a different problem: SSO + RBAC + RLS + migrations + backups + rate limits + idempotency + monitoring + alerts + incident response + SOC 2. None of that ships by default — and most of the AI-generated code assumes a single trusted user on localhost.
What we do
- Audit the prototype against a 12-axis production-readiness rubric (auth, data, APIs, observability, security, cost, compliance, devops, reliability, performance, accessibility, error handling).
- Harden auth: SSO, MFA, RBAC, session handling, password reset, account recovery, audit logs.
- Harden the data layer: RLS policies (or self-hosted Postgres + your own auth), migrations, backups, scaling, PII redaction.
- Wire observability: per-route cost, latency, error rate, and SLO dashboards — wired into the team's existing stack, not a separate tool.
- Stand up the security + compliance the demo doesn't have: prompt-data gateway, AI-aware SAST, SOC 2 evidence trail.
How it fits together
What you get
Built on our open source
fast-litellm — Rust acceleration for LiteLLM — faster connection pooling, rate limiting, and memory-intensive workloads.
Common questions about this engagement
Do you work under NDA?
Yes — we sign your mutual NDA before any data or repo access. For audits we prefer read-only access to start; for builds we work in a clean repo under your ownership.
Will we own what you ship?
Always. You own the code, the runbooks, the dashboards. We are explicitly set up to hand off and transition out, not to create dependency.
Vendor-agnostic — what does that mean in practice?
We integrate with what you already run — OpenAI, Anthropic, open-weight models on your cloud, your CI/CD, your observability stack. If a hosted vendor solves it, we will not reinvent it; if a self-hosted tool is the right answer, we will not pretend the hosted one is.
How is this different from a Big Four consulting deck?
We are the engineers doing the work, not analysts handing recommendations to a different team. The deliverable is working software in your repo, not a slide deck.
Can you work with our in-house AI team instead of replacing them?
Yes — most of our engagements pair with an internal owner and ramp them up to run the system after we leave. Many of our best engagements start with "we hired an AI team, help us get them productive."
Guides for this work
Is your codebase ready for AI agents?
Agents don't fail randomly on a repo — they fail predictably on the same things that slow down human engineers: fuzzy boundaries, missing tests, undocumented conventions. Readiness is just those weaknesses paid down deliberately.
Agentic codingWhat is agentic coding?
Agentic coding is letting an AI coding agent plan, edit across files, run tools and tests, and iterate in a loop toward a goal — powerful for the first 80% of a change, and quietly expensive on the last 20% if you skip standards and review.
Let’s scope it on a call
Thirty minutes with an engineer. We’ll tell you straight whether this is the right first move for your team.