AIDLC

AI-Powered SDLC Platform

Ship faster with intelligent quality — from requirements to release, in one workspace.

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Overview

AIDLC is a full-stack platform that brings AI into every stage of the software delivery lifecycle. It helps teams analyze requirements, generate tests, review code, predict defects, gate releases, and monitor production — with a polished React dashboard and a FastAPI backend powered by Ollama.

Built by WayamAI.

┌─────────────────────────────────────────────────────────────┐
│  Frontend (Vite + React + shadcn/ui)          :8080         │
│  Dashboard · Pipeline · AI Workspace · Code Review          │
└──────────────────────────┬──────────────────────────────────┘
                           │  /api/*
┌──────────────────────────▼──────────────────────────────────┐
│  Backend (FastAPI)                            :8000         │
│  Requirements · Test Gen · GitHub · Jira · CI Intel         │
└──────────────────────────┬──────────────────────────────────┘
                           │
         ┌─────────────────┼─────────────────┐
         ▼                 ▼                 ▼
    MongoDB            Ollama AI         GitHub / Jira

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Features

Build & Code

Module	Description
AI App Builder	Generate React apps from natural-language prompts
AI Workspace	Monaco editor with Copilot, Git ops, and impact analysis
Code Reviewer	AI-powered inline PR review via GitHub integration
Code Impact	Dependency graph and affected-test mapping
PRD Generator	Turn ideas into structured product requirements

Testing & Quality

Module	Description
Repo Test Baseline	Scan repos and generate categorized Playwright tests
Doc-Driven Tests	Extract test scenarios from documentation
Live Test Runner	AI-driven browser test execution
Defect Prediction	File-level risk scoring from commit history
Requirements → Tests	Analyze requirements and auto-generate test cases

Release & Ops

Module	Description
SDLC Pipeline	End-to-end delivery workflow across stages
Deployments	Vercel deployment tracking
Release Gate	AI-assisted go / no-go release decisions
CI Intelligence	Workflow health, flaky test detection, failure explanation
Monitoring	Anomaly detection on time-series metrics

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Quick Start

Prerequisites

Tool	Version
Python	3.12+
Node.js	18+
MongoDB	7+ (Docker recommended)
Ollama	Local or Ollama Cloud

1. Clone & configure

git clone https://github.com/WayamAI/AIDLC.git
cd AIDLC

2. Start MongoDB

docker run -d --name aidlc-mongo -p 27017:27017 -v aidlc-mongo-data:/data/db mongo:7

3. Backend

cd backend
cp .env.example .env
# Edit .env — set MONGODB_URI, OLLAMA_BASE_URL, OLLAMA_API_KEY, OLLAMA_MODEL

python -m venv .venv
# Windows
.venv\Scripts\activate
# macOS / Linux
source .venv/bin/activate

pip install -r requirements.txt
uvicorn main:app --host 127.0.0.1 --port 8000 --reload

API docs: http://127.0.0.1:8000/docs

4. Frontend

cd frontend
cp .env.example .env
npm install
npm run dev

Open http://localhost:8080 — the Vite dev server proxies /api to the backend.

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Environment Variables

Backend (backend/.env)

Variable	Required	Description
MONGODB_URI	Yes	MongoDB connection string
MONGODB_DB	Yes	Database name (default: aidlc)
OLLAMA_BASE_URL	For AI	http://localhost:11434 or https://ollama.com
OLLAMA_API_KEY	For AI	API key for Ollama Cloud
OLLAMA_MODEL	For AI	e.g. gpt-oss:120b or llama3.1
GITHUB_TOKEN	Optional	Private repos & higher rate limits
JIRA_DOMAIN / JIRA_EMAIL / JIRA_TOKEN	Optional	Jira sprint intelligence
VERCEL_TOKEN	Optional	Deployment tracking

See backend/.env.example for the full list.

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Project Structure

AIDLC/
├── backend/                 # FastAPI API server
│   ├── app/
│   │   ├── routes/          # REST endpoints
│   │   ├── services/        # Business logic & integrations
│   │   ├── engines/         # AI test generation pipeline
│   │   └── models/          # Pydantic / DB models
│   └── main.py
├── frontend/                # Vite + React SPA
│   ├── src/
│   │   ├── pages/           # Route-level views
│   │   ├── components/      # UI components (shadcn/ui)
│   │   └── lib/             # API client, design system, brand
│   └── public/
│       └── videos/          # Login hero video assets
├── VERCEL_DEPLOY.md         # Step-by-step Vercel deployment guide
├── vercel.json              # Full-stack Vercel config (frontend + API)
├── api/index.py             # Serverless FastAPI entrypoint
├── requirements.txt         # Python deps for Vercel (slim, no Playwright)
├── SETUP.md                 # Detailed local setup guide
└── DEV_CHECKLIST.md         # Development checklist

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Tech Stack

Frontend

• React 18 · TypeScript · Vite
• Tailwind CSS · shadcn/ui · Framer Motion
• TanStack Query · React Router · Recharts
• Monaco Editor · Sigma.js (dependency graphs)

Backend

• FastAPI · Motor (async MongoDB) · Pydantic v2
• Ollama / OpenAI-compatible API for LLM calls
• Playwright · GitPython · httpx

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Scripts

# Frontend
cd frontend && npm run dev      # Dev server (:8080)
cd frontend && npm run build    # Production build
cd frontend && npm test         # Vitest

# Backend
cd backend && uvicorn main:app --reload --port 8000

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Deployment

Vercel (recommended)

One-click full stack — import the repo on Vercel. The root vercel.json builds the frontend and runs the FastAPI API at /api on the same domain.

# Or deploy via CLI
npm i -g vercel && vercel login && vercel --prod

Required Vercel env vars: MONGODB_URI (Atlas), OLLAMA_BASE_URL, OLLAMA_API_KEY, OLLAMA_MODEL

Full guide: VERCEL_DEPLOY.md

Deploy mode	Root directory	Config
Full stack (recommended)	.	vercel.json
Frontend only	frontend	frontend/vercel.json + VITE_API_URL
Backend only	backend	backend/vercel.json

Note: Live Playwright execution and AI IDE WebSockets require a VM — see frontend/DEPLOY_AZURE_VM.md.

Other platforms

Both frontend/ and backend/ can also deploy independently. Point VITE_API_URL at your API URL for split deployments.

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Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/my-feature)
3. Commit your changes
4. Open a pull request against main

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License

Proprietary — © WayamAI. All rights reserved.

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_{Built with care by the WayamAI team}