AI-Native Engineering

Software,
built AI-native.

Two ways to work with us: we build production software for you with an AI-native team, or we rebuild your engineering team’s delivery around coding agents. Either way, every change is reviewed and every step has a human owner.

Where this fitsThe 4-phase transformationSee the model →
  1. 01Human-led innovation
  2. 02Skill codification
  3. 03Agent-led workflows
  4. 04Agentic departments

What’s not working

The simple tickets work.
The real ones don’t.

We tried three models. Same result.

It’s not the model

The hard part is the system around it: the rules, context and process that shape what goes in and what comes out.

We write code faster. We don’t ship any faster.

Tools without a process

A coding assistant speeds up typing. Review, testing and deploys still work the way they did before AI.

By the time I’ve explained it and fixed what comes back, I could’ve written it myself.

Review eats the savings

Giving each ticket enough context, then reviewing and fixing what the agent returns, costs your senior people more than doing the work themselves.

Two formats

Pick the one
that fits where you are.

Done for you

We build it for you.

An AI-native team plans, builds, tests and ships production software, then hands it over with its evals, observability and runbook.

Best for
A product idea that’s been stuck on the roadmap
Timeline
Varies by project complexity
You keep
The code, evals, observability and runbook
Scope a build →

Done with you

We rebuild how your team builds.

A pilot on one real project. We set up the pipeline, your written standards and the review gates, and your team builds alongside us.

Best for
A delivery process that’s slowing your team down
Timeline
About 12 weeks, one real project
You keep
The starter kit, so your team runs it without us
Plan a pilot →

How it works

We build the machine
that builds the software.

You stop hand-building each feature and start running the system that produces them. Every step has a gate, and a person signs off before anything moves.

  1. Step 01

    Brief

    A stakeholder conversation becomes a clear brief.

  2. Step 02

    Spec and architecture

    The brief becomes specs and a build plan. A person approves both.

  3. Step 03

    Build

    Purpose-built agents write the code, following your team’s written standards.

  4. Step 04

    Review

    Independent judges grade the work first. Then a person approves it, and nothing merges on its own.

How it is vs. how it’ll be

Legacy delivery vs. AI-native delivery.

Legacy delivery AI-native delivery
Who builds it Devs only Every role paired with AI
Product & discovery Months of requirements docs AI-augmented scoping in days
Engineering Hand-writes every line Drafts with agents, then reviews
QA Manual test passes at the end AI-generated tests, continuous evals
Code review Backed up on senior devs AI-assisted, humans own judgment
Time to ship Quarters Weeks

Why it matters

Built to run
in production.

Governed by design

Every AI change is approved by a person and logged, with no auto-commits and no auto-merges.

Observable and debuggable

Complete visibility into every decision, action and outcome.

You own it

The code, the evals and the runbook are yours, with no lock-in to us or to a single model provider.

Measurable ROI

Every engagement anchored to outcomes you can count: throughput, cost, cycle time.

Models we work with

We’re a Claude Partner Network member and a Microsoft partner. Claude and GitHub Copilot follow the same written rules, so you can switch models without starting over.

Models we work with

  • OpenAI
  • Anthropic
  • Mistral
  • Gemini
  • Llama
  • AWS Bedrock