Project Info

Winner

Conversion: Best Use of Conversion

Onboardly

Devpost

Inspiration

We were inspired by the massive efficiency gap in technical onboarding. Companies often spend days manually provisioning accounts, setting up security, and explaining the same basic cloud concepts repeatedly. This process is intimidating, prone to human error, and lacks real-time guidance for new hires. Our goal was to eliminate this friction entirely, creating an AI that doesn't just create tickets, but actively guarantees a smooth, secure, and educational Day 1 experience by automating both the company-side security setup and the user's hands-on learning process.

What it does

Onboardly is a Full-Stack, AI-Powered Onboarding Orchestrator that seamlessly bridges IT provisioning and user coaching. For the Company (Automation): From a single form trigger, Onboardly uses the Groq AI to dynamically generate an 8-step, role-specific curriculum (e.g., GCloud training for an SWE Intern). It then uses Jira's API to instantly create a master Epic and all sub-tasks, and SendGrid to deliver a personalized welcome email with a downloadable calendar invite, all assigned directly to the new hire. For the Intern (AI Coach Extension): Once the intern clicks the Jira link and navigates to the Google Cloud Console, our custom Chrome Extension injects an AI Coach panel. This panel uses Gemini Vision AI to capture and analyze the screen, providing real-time, step-by-step guidance on how to complete each taskβ€”like a senior developer looking over their shoulder. The entire workflow is tracked, and when the final task is complete, the system automatically transitions the main Jira Epic to DONE.

How we built it

We built Onboardly using a clean, three-part architecture. 1. The Provisioning Engine (Node.js/Express): This handles the pre-onboarding setup, orchestrating API calls to Groq for curriculum generation, and using the Jira API to create and assign the entire task hierarchy. 2. The AI Brain (Python/Flask with Gemini): This server exposes the vision endpoints. It utilizes Gemini 2.0 Flash for low-latency visual analysis of the screenshot and Gemini 2.5 Pro for sophisticated reasoning and generating clear coaching instructions. 3. The AI Coach Frontend (Chrome Extension): This utilizes a Background Service Worker to securely capture the visible tab screenshot, which the Content Script then sends to the Python backend. The Content Script then renders the real-time coaching UI directly onto the Google Cloud Console interface, providing the interactive guidance needed to complete the Jira tasks.

Challenges we ran into

The primary challenges involved navigating complex and often fragile enterprise APIs. Jira Provisioning was the biggest obstacle: We faced persistent issues finding the correct internal issue type IDs (10001, 10004) and dealing with the obscure "Epic Name" field ID, which required removal for our simple project. Furthermore, the Jira API user invite process consistently failed on the free tier. We bypassed this by implementing a feature to automatically assign the Epic to the manager's account, ensuring the demo user had instant, authorized access to the tasks. Secondly, AI Key Quotas blocked our progress with an insufficient_quota error, which we resolved by performing a real-time migration to Groq AI's compatible API, maintaining our dynamic curriculum feature.

Accomplishments we're proud of

We are most proud of achieving true, end-to-end automation of a complex business process within a short hackathon window. This includes: 1. Zero-Touch Provisioning: Successfully creating a Jira Epic, 8 sub-tasks, and sending a personalized welcome email with a downloadable calendar inviteβ€”all from a single Node.js trigger. 2. Dynamic Curriculum: Using Groq AI to generate a highly detailed, accurate 8-step GCloud curriculum in under one second. 3. The Wow Factor: Seamlessly integrating real-time Gemini Vision coaching that actually understands what the user is seeing on a complex external site (GCP Console) and guiding them to complete the automated tasks.

What we learned

We learned three crucial lessons: 1. AI Compatibility is Key: Utilizing the OpenAI-compatible API structure (as provided by Groq) is vital for rapid prototyping and maintaining provider flexibility when quotas are an issue. 2. API Workarounds are Essential: Complex enterprise APIs (like Jira's) often require deep inspection of error messages and unconventional workarounds (like removing required fields or using specific transition IDs) to integrate successfully in a fast-paced environment. 3. The "Isolation Problem" Requires a Stack: A helpful AI coach cannot be built in one script; it requires a stack of communication (Content Script $\leftrightarrow$ Background Script $\leftrightarrow$ Flask Backend) to securely and effectively capture the user's screen and leverage powerful vision models.

What's next

for onboardly We plan to implement three key features: 1. Jira Webhook Integration: We will eliminate the final "Mark Complete" button by having Jira trigger a webhook back to our server when all 8 sub-tasks are manually marked "Done," achieving truly touchless final Epic completion. 2. Advanced Security Checks: Integrate the GitHub API (which we built a stub for) to check if the intern has enabled required branch protection rules on their new starter repository before marking that task as complete. 3. Gemini Pro Reasoning: We will leverage the powerful reasoning capabilities of Gemini 2.5 Pro to provide non-visual feedback, such as analyzing the security logs in the next step and providing a summary of the threats found directly to the intern.

Analysis

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Figures cover GitHub contributors during the hackathon window. A co-authored commit counts in full for each author, so per-member totals add up to more than the whole-team figures.

Technology

Found in codeClaimed only
  • CSSIn code
  • ExpressIn code
  • FlaskIn code
  • Google GeminiIn code
  • HTMLIn code
  • JavaScriptIn code
  • OpenAIIn code
  • PythonIn code
  • ReactIn code
  • Tailwind CSSIn code
  • Node.jsClaimed

10 of 11 appear in the indexed code. 1 claimed on Devpost could not be matched to code, which may simply mean the tool leaves no trace in the repository.

AI coding agents

  • Claude CodeConfig Β· Commits

Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.

Codebase size

Source size

265 KB

Source files

35

Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.

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