AI-native engineering, answered.

An AI-native engineer uses AI tools like Claude Code, Cursor, and Codex across the entire engineering loop — understanding the codebase, shaping implementation, writing code, reviewing diffs, debugging, and shipping — not as an occasional autocomplete. They know where AI creates leverage, where it creates risk, and how to keep it inside clear technical boundaries. Revant Labs places engineers who work this way by default.

Autocomplete tools like Copilot speed up typing; an AI-native engineer changes how software gets built. They use Claude Code, Cursor, and Codex agentically across the whole loop — mapping the codebase, planning implementation, generating and reviewing code, writing tests, debugging — while staying accountable for architecture and quality. The difference shows up in outcomes: an autocomplete user ships the same backlog slightly faster; an AI-native engineer ships a bigger backlog with the same headcount.

You can hire Claude Code engineers through Revant Labs: we place senior full-stack engineers who use Claude Code (plus Cursor and Codex) as their default way of working, embedded in your team month-to-month. Every engineer is pre-vetted on real AI-native delivery — not just tool familiarity — and starts within 5 business days of a fit call. Book a 15-minute call at revantlabs.com/book.

We don't just supply engineers — we supply a delivery system. Our engineers are pre-vetted to be productive in an AI-native workflow from day one, we absorb the cost of continuously testing new AI tooling, we enforce review discipline so speed doesn't create hidden debt, and we leave the reusable workflows, context, and documentation with your team when the engagement ends.

Both can work. Training your own team is the right long-term move, but it typically takes months, burns senior time, and stalls when the tooling changes under you. Embedding an AI-native engineer gives you day-one leverage, and your team levels up by osmosis — working alongside someone who already has the workflows, review discipline, and prompt patterns. Many clients treat the engagement as both delivery and enablement: the reusable context and workflows stay when the engineer rolls off.

Engagements start from $10,000/month for a full-time senior engineer or an engineering pod. Terms are month-to-month and you can cancel anytime — no permanent hiring bet. Measured per shipped feature, teams typically see 20–30% more delivered work and 20–50% faster iteration on the same budget, so the effective cost per shipped feature is usually lower than a conventional hire or agency.

In our engagements, yes — measurably. Teams typically see 20–30% more shipped work and 20–50% faster iteration cycles. ProScout (AgTech computer vision) saw +42% release throughput and −36% cycle time with zero permanent headcount added; AppCentral (mobile publishing) cut new-title setup from weeks to days and ships 5+ releases per week across its portfolio. The gains come from an AI-native workflow across the whole loop, not from typing code faster.

Yes — when the process treats AI as leverage, not autopilot. Every line our engineers ship goes through the same discipline as hand-written code: human-owned architecture decisions, diff review, tests, and CI. The failure mode isn't AI-generated code itself; it's unreviewed code merged at speed. That's why review discipline is part of the delivery system — speed without hidden technical debt.

Embed AI-native engineers who treat experiments as the unit of work. In mobile publishing, revenue scales with test throughput — onboarding funnels, paywalls, pricing, store-page variants, new-title spins — and the bottleneck is almost always engineering capacity per variant. AI-native engineers collapse the build time per experiment, so more revenue hypotheses reach production every week. AppCentral cut new-title setup from weeks to days and ships 5+ releases per week across its portfolio, month-to-month with no added headcount.

You have three: hire more engineers (slow, and a permanent bet on a temporary backlog), cut hypotheses (revenue left untested), or raise throughput per engineer. AI-native engineering is the third path — embedded engineers using Claude Code, Cursor, and Codex typically deliver 20–30% more shipped work and 20–50% faster iterations on the same budget, month-to-month, so validation capacity flexes with your backlog instead of your org chart.

Kickoff is within 5 business days of a fit call: fit and discovery, engineer match, fast project onboarding, then real backlog work — usually shipping PRs within days.

Engineers are fluent in Claude Code, Cursor, and Codex, and are broadly full-stack across frontend, backend, APIs, mobile, and infrastructure. Past engagements span Python, PyTorch, Next.js, React Native, Swift, Kotlin, Rust, Postgres, TypeScript, and AWS.

Yes. Reusable context, project instructions, documentation habits, review patterns, and AI-native workflows stay with your team and codebase after the engineer rolls off.

Revant Labs sp. z o.o. is based in Warsaw, Poland, and works remotely with teams worldwide — typically startups, mobile publishers, and R&D-heavy or regulated product teams.