AI-Assisted Development

Our Philosophy: Experience-Driven AI Development

UnaMentis is built with AI-assisted development from the ground up, but this is not "vibecoding." The project founder brings over 30 years of experience in technology, the majority spent as a developer, with years contributing to open source projects. Every architectural decision, every tool selection, and every quality standard is informed by decades of real-world software engineering experience.

The goal is ambitious: use AI not just to move faster, but to approach the quality and review standards achieved by a thoughtful, attentive human developer. We have deep respect for that standard. A skilled developer bringing full attention to code review, architecture decisions, and quality assurance is not easily replicated by any single tool. However, when six or seven layers of AI-driven tools and processes work together, each with overlapping review and complementary perspectives, the cumulative effect can begin to approximate that level of rigor. We are documenting an ongoing experiment in what becomes possible when deep experience guides this layered approach with intention.

AI handles the repetitive, error-prone aspects of software development while human experience guides architecture, quality standards, and the creative problem-solving that makes UnaMentis unique. The combination enables a small team to build and maintain a sophisticated, multi-platform voice AI tutoring system.

AI Tools We Use

Our AI-assisted development workflow combines multiple specialized tools, each chosen for its strength in a specific domain. Together, they form a comprehensive system that touches every aspect of our development process.

The Code Quality Initiative

To achieve enterprise-grade quality with a small team, we implemented a systematic 5-phase Code Quality Initiative. Each phase builds on the previous, creating layers of automated protection that catch issues progressively earlier in the development cycle.

Key Achievements

Phase 1: Foundation

The foundation phase automates existing manual quality gates across iOS, Server, and Web components. The goal: make quality enforcement invisible and unavoidable.

Pre-Commit Hooks

A unified hook system runs automatically before every commit, catching issues before they enter the repository:

Dependency Automation (Renovate)

Manual dependency tracking is eliminated. Renovate handles everything automatically:

• Schedule: Updates run Mondays before 6am, minimizing disruption
• Grouping: iOS, Python, and npm dependencies grouped separately for focused review
• Auto-merge: Security patches, patch updates, and dev dependencies merge automatically
• Manual review: Major version updates and breaking changes require human approval

Coverage Enforcement

Code coverage is not a suggestion. It is a gate. The iOS build fails if coverage drops below 80%. This is enforced automatically in CI, with coverage extracted from Xcode result bundles.

Phase 2: Enhanced Quality Gates

Phase 2 extends quality enforcement with nightly testing, performance regression detection, and comprehensive security scanning.

Nightly End-to-End Testing

Every night at 2am UTC, comprehensive end-to-end tests run against the full system:

• iOS E2E tests with real API keys (from GitHub Secrets)
• Latency regression tests using the provider comparison suite
• Full voice pipeline validation
• Automatic GitHub issue creation on failure with "nightly-failure" label

Performance Regression Detection

Voice applications live and die by latency. Our latency test harness ensures we never ship a slower release:

Multi-Layer Security Scanning

Security is not a single check. It is a layered defense:

Phase 3: Feature Flag System

Feature flags enable safe experimentation. New features can be developed, deployed, and tested in production without affecting all users. If something goes wrong, we flip a switch instead of rolling back a deployment.

Self-Hosted Unleash Infrastructure

We run our own feature flag system using Unleash, giving us full control over flag management without subscription costs:

Flag Lifecycle Management

Feature flags have a lifecycle. Forgotten flags become technical debt. Our automated audit system tracks every flag from creation to cleanup:

• Flags have target removal dates and designated owners
• Weekly automated scans detect overdue flags (90 day maximum age)
• CI creates GitHub issues for flags approaching expiration
• PR comments highlight any flag changes for review

Phase 4: Observability & DORA Metrics

You cannot improve what you do not measure. Phase 4 provides visibility into quality trends and engineering health through industry-standard DORA metrics.

DORA Metrics (Apache DevLake)

We track the four key metrics that distinguish elite engineering teams:

Quality Dashboard

Daily automated metrics collection provides ongoing visibility:

• CI/CD success rates across iOS, Server, and Web pipelines
• Pull request metrics (count, average size, review time)
• Bug metrics (open count, closed in 30 days, age distribution)
• Trend analysis with 90-day retention for pattern detection

Phase 5: AI-Powered Code Review

The final phase brings AI into the review process. Every pull request receives automated analysis from CodeRabbit, configured for maximum issue detection with language-specific rules for our tech stack.

Review Configuration

CodeRabbit is configured in "assertive" mode for comprehensive coverage:

Results & The Road Ahead

The Code Quality Initiative is an ongoing journey. Phases 1-4 are complete, with Phase 5 actively in progress. Here is where we stand and where we are heading:

Current Quality Gates

Planned Advanced Features