Boosting engineering cycles with AI-native SDLC
- Category: Private Equity
- Industry: Education
- Service: Startup
- Client: Civitas Learning
A Francisco Partners-backed Civitas Learning partnered with Symphony for a 10-day AI SDLC sprint that transformed delivery speed and confidence. By installing agentic coding workflows, automated PR reviews, and a hardened test setup, the in-house team closed a ticket previously estimated at 4 weeks in just 2 hours and reduced manual coding by ~80%. Feature work that used to take 2–3 days now lands in a few hours, with faster testing and debugging to match.
Problem
Delivery was dragging at 2–3 days per feature; QA bottlenecks and long compliance cycles slowed releases, and the team was skeptical about applying AI to "real" dev tasks.
- Feature delivery cycles averaging 2-3 days per ticket
- QA bottlenecks causing delays in release pipeline
- Long compliance cycles slowing down deployments
- Team skepticism about AI effectiveness in development workflows
- Manual coding processes consuming excessive development time
Solution
Symphony ran a focused AI-in-SDLC sprint and embedded workflows the team could own:
- Cursor rules tailored for the PHP codebase to standardize high-quality changes
- GitHub/Jira integrations that auto-link work and streamline reviews
- Automated PR reviews to keep standards high without adding wait time
- A Dockerized engineering environment and test harness (PHPUnit, Playwright, API mocks) for rapid validation and safer merges
- Elements drawn from the client workshop + delivery plan
Technology: Implemented using AI-native development tools, automated testing frameworks, and containerized environments to ensure rapid, reliable delivery cycles.
Under the hood
The technical implementation focused on sustainable AI-powered workflows:
- Core track: AI development workflows (Cursor rules for PHP; GitHub/Jira; automated PR checks)
- Multiplier track: Platform fundamentals (Docker environment including database/webhooks/containers; testing framework with PHPUnit, Playwright, API mocks)
- Change management: Lightweight enablement so the client's team could continue independently after Day 10
- Automated code quality checks and standardized development patterns
- Integrated CI/CD pipeline with comprehensive test coverage
- Real-time collaboration tools for seamless team coordination
Impact
- 4-week ticket reduced to 2 hours during the sprint's peak (Day 8)
- Feature cycles compressed from 2–3 days to ~3 hours, with ~80% less manual coding
- Faster infra work: complete env setup in minutes, test setup in minutes (not days)
- ~50% faster debugging and issue resolution
- Team gained confidence in AI-powered development workflows
- Sustainable processes that the team could continue independently
- 4 weeks → 2 hours - Ticket completion time during sprint peak
- 80% - Reduction in manual coding effort
- 2-3 days → 3 hours - Feature cycle compression
- 50% - Faster debugging and issue resolution