Where it saves time
- Discovery. Call notes and documents become a first draft of requirements and user stories, which an analyst then corrects.
- Scaffolding. The repetitive layer - models, serializers, admin screens, API clients - is generated from the spec, so engineers start from the interesting part.
- Tests. Unit and integration tests are drafted for existing code; engineers review and fix them instead of writing from zero.
- Review. A first pass catches missing error handling, naming drift and obvious security mistakes before a human reviewer spends time.
- Documentation. READMEs and API docs stay in sync with the code.
Where we keep it on a short leash
- Architecture. A model will confidently suggest a design. A senior engineer decides.
- Security-sensitive code. Authentication, payments, medical data: AI may draft, humans review line by line.
- Data that cannot leave. For regulated projects models run on the client's infrastructure - our AI call center for the UAE processes every call inside the country.
What it means for your budget
AI-assisted delivery moves cost from typing to thinking. The effect is largest on MVPs and internal tools, where much of the code is standard. In complex domains - trading engines, compliance logic, real-time systems - the savings are smaller, because the hard part is the decision, not the keystrokes.
AI inside your product
The same experience goes into client products: voice agents that answer calls around the clock, copilots that fill forms for the user, generative tools inside professional software. The pattern holds - AI takes the routine, people keep the decisions.
Ask us
Describe your product and we will tell you where AI would help - and where it would add risk without saving money.