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Is your vibe-coded MVP production-ready?

You built it fast with AI coding tools, and it works on your machine. Before real users, real data and real payments arrive, a senior engineer reads the code and tells you what breaks first: auth and secrets, the data model, deploys and rollbacks, and the parts the agent quietly routed around. You get a short, ordered fix list, not a rewrite pitch.

05 · experts matched 68 cases across their profiles Contracted through Invental Request a review ↗
Lead matches
Code review · Arch. review · Arch. design · Due diligence · Vibe-code rescue · Mentoring

Continuous-delivery fractional CTO

Best for: solo founders with a vibe-coded MVP · seed startups without a CTO · scale-ups after a funding round · investors needing a quick technical audit · CTOs who want an outside second opinion.

CTO-level · Europe, working with North American and European clients View profile →
Lead match · 01 CTO-level
Code review · Arch. review · Arch. design · Due diligence · Vibe-code rescue · Mentoring

0→1 founder-CTO for data-heavy SaaS and agent-ready tooling

Best for: solo founders and seed startups building a data or AI product · SaaS teams adding an API or MCP server · developer-tool companies whose CLI or API will be called by AI agents · founders who want product and engineering advice in one person.

founder-CTO / founding product engineer · Europe View profile →
Lead match · 02 founder-CTO / founding product engineer
— Also matched for this
staff / lead-level full-stack engineer

AI-native staff engineer

Best for: solo founders and seed startups adopting AI coding tools; small SaaS teams preparing for SOC 2 / ISO 27001; teams migrating a Vue front end to Next.js; companies that want juniors coached by someone who teaches..

Code review, Arch. review +4View profile →
 senior data / ML engineer with more than a decade in data engineering

Founding data engineer: DWH, ML and LLM systems from zero

Best for: solo founder · seed startup · scale-up (especially the ~50-person "our numbers don't match" stage).

Code review, Arch. review +3View profile →
 senior / lead backend engineer

High-load backend lead for payments and consumer platforms

Best for: seed startup · scale-up · corporate innovation team (especially teams where non-engineers ship with AI tools).

Code review, Arch. review +4View profile →

From the network

Selected cases from these experts’ profiles.

Turning a vibe-coded MVP into a production product

A non-technical founder vibe-coded an MVP that got early interest, but features break each other, some pages are slow, and nobody can answer "is it secure?".

A short audit of code, data model, security and infrastructure; then set up continuous delivery, tests where they give the most confidence, and error plus uptime monitoring, and decide what to keep.

You get

An audit report and a stabilization plan, plus advice on whether to look for a technical cofounder, a full-time CTO or a fractional one.

Bookable · Vibe-code rescue · Architecture reviewwhatever the MVP uses (often Next.js / React / Python)
Continuous-delivery fractional CTO →

Review of an AI-built SaaS before it takes payments

A founder has a Python or TypeScript SaaS built mostly with AI tools and is about to add billing, an API or an MCP server. The expert reviews data model and pipeline design, which store serves which query, metering and pricing hooks, retry and idempotency on anything that writes, and whether the interfaces are agent-safe.

You get

A ranked fix list, a target architecture sketch and the first fixes made together with the team.

Bookable · Vibe-code rescue · Architecture reviewPython / FastAPI or TypeScript, PostgreSQL / MySQL, ClickHouse or Elasticsearch when relevant, MCP
0→1 founder-CTO for data-heavy SaaS and agent-ready tooling →

Review and rescue of an AI-generated codebase

A founder built fast with AI tools and now has a Next.js / TypeScript app nobody fully understands.

Read the code against what the product should do, add types and tests on the risky paths, and set up a spec-first workflow so later AI changes stay reviewable.

You get

A findings report, a stabilization plan, and a working spec / CI setup.

Bookable · Vibe-code rescue · PR/MR code reviewNext.js, TypeScript, Node, Python, CI
AI-native staff engineer →

Making a vibe-coded FastAPI + Next.js MVP production-ready

A founder built an MVP with AI tools; it works on a laptop but not reliably in production.

Database schema and migrations, async and background work in FastAPI, auth and secrets, Next.js data fetching, deployment and error monitoring.

You get

A prioritised fix list, reviewed PRs for the critical items, and a simple deploy-and-monitor setup.

Bookable · Vibe-code rescue · PR/MR code reviewFastAPI, SQLAlchemy, PostgreSQL, Next.js, Docker, Sentry
Founding data engineer: DWH, ML and LLM systems from zero →

A safe "sandbox + guardrails" setup for a founder who ships with AI

A founder or a non-engineering team generates features with AI tools and deploys straight to production.

Set up a separate sandbox environment and pipeline for experiments, protect the main branch with automated checks, and define how an experiment gets promoted to production after review.

You get

A working sandbox, a written promotion path from experiment to production, and a short list of the riskiest areas in the current backend.

Bookable · Architecture design · Vibe-code rescueGitLab CI or equivalent, containers / Kubernetes, the product's existing backend
High-load backend lead for payments and consumer platforms →

Questions buyers ask

What does a review of a vibe-coded MVP actually check?+
The places AI-built code most often fails in production: authentication and secret handling, input validation, the data model and migrations, error handling, how deploys and rollbacks work, and whether anything is monitored. The output is a prioritized list ordered by risk, so you fix what can hurt users or data first.
Do I need to rewrite an MVP that was built with AI tools?+
Usually not. Most AI-built MVPs need a handful of structural fixes and some guardrails, not a rewrite. A reviewer marks which parts are safe to keep, which need fixing before launch, and which can wait until there is traction.
How do you change a production database schema without downtime?+
Split the change into phases so the application version still running in production always works with the new schema: for example, add a new column first, deploy code that writes to both, backfill, switch reads, and only then drop the old column. Large-table operations also need care because they can lock tables. An expert in the Invental network applies this phased approach as standard practice on continuously deployed projects.
Why do AI agents fail when they call command-line tools?+
Most CLIs were designed for humans. They page output and wait for a keypress, return the same exit code for "nothing happened" and "half-done", print free text instead of structured data, or crash silently on odd input. An agent then stalls, wastes tokens, or retries a non-idempotent action. A review checks exit codes, output format, timeouts and retry safety against a list of known failure modes.

How it works

  1. Tell us what you need — the repo or system, the question, and the deadline.
  2. We match an expert from the network, with a second reviewer where it helps.
  3. Scoped work, contracted through Invental — review per pull request, a fixed-scope audit or architecture review, or ongoing capacity.

— Invental · software studio · Montevideo, UY

Tell us what needs a look.

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