Speed
2-week sprints, incremental delivery, and continuous prioritization.
Development with
Agentic AI.
2-week sprints with AI assistants for code generation, testing, and documentation, without replacing human review or quality governance.
A hybrid Scrum + Kanban model where AI adds value in planning, backlog, and productivity, without replacing human validation.
- AI assistants for code generation, refactoring, testing, and documentation
- Specialized team: Tech Lead, AI-Native Software Engineers, QA, DevSecOps
- Every AI-generated asset is reviewed and approved before acceptance
- Per-sprint performance metrics: coverage, defects, release frequency
01Discovery and Inception: vision, architecture, and backlog
02AI-assisted build, with human code review
03QA + DevSecOps, demo, and retrospective
AI built into the development cycle, with human governance.
AI as a controlled accelerator
It doesn’t replace engineering: every generated asset goes through human review.
Specialized team
Tech Lead, AI-Native Software Engineers, QA Automation, and DevSecOps Engineer.
Key features
AI-powered productivity
Present across requirements, design, code, testing, security, and documentation.
Security by design
DevSecOps, plus dependency and vulnerability analysis in every increment.
Governance and control
Backlog, evidence, metrics, and acceptance criteria for each increment.
30-day onboarding plan
Kick-off, discovery, first sprint, and pilot release in one month.
When does this model make sense?
When you need to build from scratch with speed and control.
New applications and modules
When your architecture needs new services and integrations.
APIs and microservices
When you want to add artificial intelligence capabilities to your products.
AI/ML integration
The results behind this solution.
-40%development time
+35%team productivity
100%human review of critical deliverables
Your questions, answered
At which stages of development is AI used?
In requirements, design, code, testing, security, and documentation, always with human control at every step.
What about the quality of generated code?
It goes through human code review and testing before being merged into any critical deliverable.
How does the team model work?
Through a specialized pod or a block of hours, adjustable to your project’s demand.
Which metrics measure the service?
Change lead time, sprint completion, test coverage, critical post-release defects, and release frequency, among others.