DevPilot — AI Developer Workflow Assistant

DevPilot is an AI-powered Developer Workflow Assistant built on IBM Bob 2.0 that unifies debugging, code review, testing, onboarding, and release preparation into one connected workflow instead of five disconnected tools. The Problem: Development teams lose enormous time context-switching between separate tools for each phase of the software lifecycle. Debugging requires manual code archaeology, reviews are inconsistent, tests get skipped under deadline pressure, new hires take weeks to ramp up on unfamiliar codebases, and release decisions rely on gut feeling rather than a trustworthy signal. The Solution: DevPilot connects a repository once and builds a single deep understanding of the codebase using Bob 2.0's Agent mode, parallel tasks, and subagents. From that one scan, it automatically detects bugs and security issues with plain-language reasoning, generates missing unit tests matched to the project's existing framework, explains unfamiliar code on demand, produces a living onboarding guide that never goes stale, and compiles a Release Readiness Score combining test coverage, open issues, and code quality into one clear number. Target Users: Solo developers, engineering teams without dedicated QA, senior engineers tired of repetitive review work, new hires ramping up on legacy code, and engineering managers who need release confidence backed by evidence, not instinct. How It Works: Users import a repo via GitHub or upload, then navigate a dashboard with live-updating panels for Issues, Tests, Code Explainer, Onboarding, and Release Report — plus a chat copilot grounded in the actual indexed codebase. A real-time 3D visualization shows the scan happening as a connected network of files, reacting visibly as problems are found. What Makes It Unique: Unlike single-purpose AI coding tools, DevPilot treats the entire developer lifecycle as one shared intelligence layer — insights from bug detection strengthen the release score, and code understanding feeds both onboarding docs and chat answers. This eliminates redundant context-rebuilding and turns fragmented workflows into one continuous, trustworthy system — cutting debugging time, raising test coverage, shrinking onboarding from weeks to days, and giving teams one clear signal before every release.