AI-assisted engineering
Use AI for speed.
Keep judgment human.
AI supports requirement analysis, product iteration, engineering documentation and quality checks. Every result is reviewed against the real system, the intended user and explicit acceptance criteria.
Live product
FitBack Coach
A deployed coaching application shaped through AI-assisted product iteration. The workflow covered requirements, interface decisions, responsive behavior, implementation review and deployment verification.
DefineClarify the user, constraints and desired coaching flow.
DesignCompare interface options and choose behavior intentionally.
BuildImplement the selected flow and maintain product consistency.
VerifyTest the deployed application in a real browser.
Open FitBack Coach →
Engineering documentation
Private implementation to public case study
AI helps inspect project material, organize the architecture and identify missing context. The public result is then manually checked so source code, repository history and private identifiers remain excluded.
- Review the real systemRead documentation, diagrams and implementation structure.
- Extract engineering decisionsIdentify responsibilities, dependencies, constraints and operational trade-offs.
- Sanitize the narrativeRemove private names, source history and environment-specific identifiers.
- Verify every claimCompare the published case study with the actual project material.
View the resulting case studies →
Browser quality assurance
Design, implementation and QA loop
AI-assisted frontend work is not complete when code is generated. The implementation is rendered, inspected and corrected at the viewports a recruiter or hiring manager is likely to use.
DesktopHierarchy, spacing, architecture visibility and navigation.
MobileText fit, menu behavior, touch targets and horizontal overflow.
ContentNames, certification status, project claims and private information.
TechnicalConsole errors, links, metadata, reduced motion and image behavior.
Operating principles
A controlled AI workflow
01Start with a concrete objective, known constraints and acceptance criteria.
02Use AI to explore alternatives, not to replace engineering ownership.
03Verify behavior, wording, links and layouts against the real implementation.
04Publish decisions and outcomes while keeping private project details private.
