Does AI mean juniors and templates?

The opposite. Small pods of senior engineers direct agents that do a material share of the work. You get senior judgment on architecture and review — and agent speed on the mechanical parts. There is no junior-to-senior headcount pyramid to rent.

Isn’t AI-generated code risky?

Uncontrolled, yes — which is exactly why the control system is the point. Every build runs inside automated tests, golden-set evals, review gates, and audit logs. Google’s 2025 DORA research is clear: AI improves throughput but only stays stable with strong testing and fast feedback loops. That layer is what we build into every project.

Do you replace my developers with AI?

No. We don’t over-promise magical speedups or a hands-off, no-human factory — a rigorous 2025 study found naive AI use can actually slow experienced engineers down while they feel faster. We position honestly at Level 4: agents work in parallel, senior humans review before anything ships.

Who owns the code and the AI setup?

You do — from the first commit. Code and IP live in your repository, we keep the stack model-independent and portable, and there’s no lock-in to us or to any single AI provider.

How We Build

AI speed. Senior-engineering discipline.

Codenovai builds software with an agentic delivery process: senior engineers direct AI agents that write, test, and review code inside a control system of automated evals, review gates, and audit logs. The result is production software, shipped in weeks, at a fixed price — with your code in your repo from day one.

Blueprint → Build → Run

Blueprint

We pressure-test the riskiest part of your idea and hand back a reference architecture, a working prototype, and a fixed-price quote. You start the build knowing the number.

Build

A senior pod directs agents that write, test, and review code in parallel — inside automated evals, review gates, and a commit history you watch from week one.

Run

We keep it stable: evals re-run weekly, drift caught before customers see it, costs watched, models upgraded. Or we hand it fully to your team. Your call.

The Stack, In Plain Terms

Every part is real, documented, and adopted — that’s the point.

MCP (Model Context Protocol)

An open standard from Anthropic, now adopted across OpenAI, Microsoft, Google, and AWS.

A secure universal adapter — like USB-C — that lets our agents work inside your CRM, database, repo, and cloud without brittle one-off integrations. Your data stays in your systems.

Claude Agent Skills

Published as an open, cross-platform standard.

Our proven delivery playbooks — auth, payments, RAG, compliance checks — packaged as reusable modules our agents load automatically. You get our best work baked in from day one, not a blank page.

AWS Strands Agents

Built and used internally by AWS for its own services.

We build on the same open-source agent framework AWS itself runs in production — battle-tested infrastructure that deploys to Lambda, Fargate, and EC2, not a science experiment.

Subagents & the harness

Level-4 harness-driven development, with humans reviewing before anything ships.

A senior-led team of specialist agents building, testing, and reviewing in parallel — inside guardrails: automated tests, eval scores, review gates, and audit logs. AI speed, senior-engineering discipline.

Where We Sit — Honestly

Level 4: multiple AI specialists work in parallel, with senior humans reviewing before anything ships.

We don’t sell a “dark factory” with no human in the loop, and we don’t claim magical developer multipliers — a rigorous 2025 study found naive AI use can slow experienced engineers down even as they feel faster. So we’re precise about it: agents accelerate the work; senior engineers own the architecture and the review gate. Speed you can trust, because a human signed off.

Why This Beats a Traditional Dev Shop

Everyone has AI now. The difference is the control system.

Google’s 2025 DORA research — spanning teams at roughly 90% AI adoption — found AI lifts throughput but only holds delivery stable where teams have strong automated testing, mature version control, and fast feedback loops. “AI doesn’t fix a team; it amplifies what’s already there.” We build that amplifier — evals, review gates, audit logs — into every project. That’s the difference between an agent that demos and software that ships and keeps working.

See what we build

FAQ

Does AI mean juniors and templates?: The opposite. Small pods of senior engineers direct agents that do a material share of the work. You get senior judgment on architecture and review — and agent speed on the mechanical parts. There is no junior-to-senior headcount pyramid to rent.
Isn’t AI-generated code risky?: Uncontrolled, yes — which is exactly why the control system is the point. Every build runs inside automated tests, golden-set evals, review gates, and audit logs. Google’s 2025 DORA research is clear: AI improves throughput but only stays stable with strong testing and fast feedback loops. That layer is what we build into every project.
Do you replace my developers with AI?: No. We don’t over-promise magical speedups or a hands-off, no-human factory — a rigorous 2025 study found naive AI use can actually slow experienced engineers down while they feel faster. We position honestly at Level 4: agents work in parallel, senior humans review before anything ships.
Who owns the code and the AI setup?: You do — from the first commit. Code and IP live in your repository, we keep the stack model-independent and portable, and there’s no lock-in to us or to any single AI provider.