"AI-native" gets thrown around as if it means "we added a chatbot." It doesn't.
AI-native development is a way of working that uses LLMs at every stage of the software development lifecycle — and treats that integration as a first-class engineering concern.
Where AI shows up in our stack
- Planning: structured brainstorming, requirement decomposition, edge case discovery
- Codegen: scaffolding, boilerplate, refactors, test generation
- Reviews: first-pass PR review before a human ever sees the diff
- Evals: regression suites for LLM-powered features, run on every commit
- Docs: auto-generated and human-curated together
None of this replaces senior engineers. It compounds their leverage.
The honest tradeoffs
LLMs are non-deterministic. They hallucinate. They get worse on long contexts. Treating them like a junior engineer who needs supervision — not an oracle — is the only thing that works.
What we recommend for product teams
- Start with evals before features. If you can't measure it, you can't ship it.
- Use codegen for breadth, not depth. Tests, types, migrations — yes. Core business logic — review carefully.
- Build a prompt registry the same way you'd build a SQL migrations folder.
- Budget for inference cost from day one. It's a real line item.
We help teams adopt AI-native workflows in their existing codebases. Let's talk.