| Traditional dev shop | AI-native team | |
|---|---|---|
| Where AI sits | Individual engineers' licenses | In the process: scaffolding, tests, refactors, migrations |
| Review of AI code | Varies by engineer | A senior engineer reviews every line before merge |
| Pricing incentive | Often hourly, so speed cuts revenue | Fixed scope or retainer, so speed is shared |
| Typical MVP timeline | Months | Navia: 4 AI modules in 4 months. Vimsy: live in 5 weeks |
| AI in your product | Often bolted on after launch | Designed into the architecture, with evals and tracing |
| Wrong AI answer in production | Investigation starts from scratch | Traced in Langfuse to the prompt and retrieval step |
| Team size | Often large | Small and senior |
When a traditional dev shop is the better choice
- •You need 20+ engineers on one program for years.
- •Your procurement requires a large vendor with formal certifications.
- •The product has no AI and changes slowly, so speed isn't the constraint.
When an AI-native team is the better choice
- •You need production software in weeks, on a startup budget.
- •AI is part of the product and has to survive real users.
- •You want to read every pull request as it lands.
Where SociiLabs fits
We're an AI-native team, so we sell one side of this. What we can show: ZapBot reached 71% booking conversion live, three months after kickoff. Every line our AI writes is reviewed by a senior engineer before merge.
ZapBot: booking by conversation
71% booking conversion, under 90 seconds from message to confirmed booking.
Read the case study~1.7×
more major issues in AI-co-authored pull requests without a senior review gate (~2.7× more security defects).
CodeRabbit, across 470 open-source pull requests
FAQ
Questions about ai-native team vs traditional dev shop
Ask to see a merged pull request and who reviewed it. Ask how they test an LLM feature before release. See /blog/how-to-evaluate-an-ai-native-dev-partner for the full list of questions.