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Owner-facing AI operations assistant for small businesses
README 전체를 불러오는 중입니다.
README 전문 보기 ↗An AI marketing agency that lives in the small business owner's WhatsApp. The owner answers a few questions about their business. A team of specialized AI agents then plans, designs and publishes their Instagram content, and nothing goes out without the owner's "Aprobar".
Status: MVP in progress (24h hackathon, 2 builders). Working end to end: onboarding (started from WhatsApp, finished in the web wizard), post and carousel creation from the chat with Gemini text and images, and approve-and-publish to Instagram through Zernio. Still to build: the dashboard, the WhatsApp simulator and the weekly plan. Start in
docs/.
| What | Where |
|---|---|
| Frontend (Vercel) | https://neitorai.vercel.app |
| Backend (Railway) | https://backend-production-6e67.up.railway.app |
| WhatsApp bot (Kapso sandbox) | https://wa.me/56920403095 |
| Path | What lives there |
|---|---|
docs/ | Product, architecture, design and implementation docs (reading order) |
backend/ | FastAPI: Kapso WhatsApp channel, conversation orchestrator, agents, in-process jobs, Gemini text and image generation, carousels, onboarding, Zernio publishing, Alembic migrations |
frontend/ | Next.js: Neitor AI landing page (/), onboarding wizard (/o/[token]); dashboard and WhatsApp simulator are planned |
.github/ | Issue and PR templates, CI and Tenki review workflows, Dependabot |
scripts/ | Repo tooling: the docs index check CI runs, Orca worktree setup |
.claude/skills/ | Shared agent skills: workflow (grilling, domain modeling, tickets, PR review, docs), frontend, and infrastructure (Supabase, Railway, Vercel) |
.claude/rules/ | Path-scoped agent rules (Python, database, Docker, CI) that load when matching files are touched |
.claude/routines/ | Nightly cloud agents (issue fixer, product research, bug hunt) that open draft PRs |
AGENTS.md | How we work: the shared workflow and conventions, for humans and any AI tool |
| Layer | Choice |
|---|---|
| Backend | Python 3.13 · FastAPI · SQLAlchemy 2 + asyncpg · Alembic · uv, on Railway (ADR 0017) |
| Frontend | Next.js 16 · React 19 · Tailwind 4 · shadcn/ui, on Vercel (ADR 0004) |
| Text LLM | Gemini gemini-3.8-flash (ADR 0023) |
| Images | Gemini gemini-nano-banana-2.1 (ADR 0018, ADR 0019) |
| Kapso (ADR 0020); voice notes via Kapso's transcript with Gemini as fallback (ADR 0021) | |
| Instagram publishing | Zernio (ADR 0008) |
| Database and files | Supabase Postgres + Storage (ADR 0003) |
Every decision and its status is in the ADR index.
Full guide, env vars and accounts: docs/implementation/setup.md.
# backend
cd backend && cp .env.example .env
uv sync && uv run alembic upgrade head
uv run uvicorn app.main:app --reload --port 8000
# frontend
cd frontend && cp .env.example .env.local
npm install && npm run dev
Checks before a PR: uv run ruff check . && uv run pyright && uv run pytest in backend/, npm run typecheck && npm run lint && npm run build in frontend/.
Read AGENTS.md: one workflow (capture, research, decide, plan, build, review, close the loop) that keeps what we learn in docs/. It applies whether you use Claude Code, another AI tool, or none.
For the MVP build, work from docs/implementation/tasks.md (one task ≈ one small PR) and follow the development workflow.
AGPL-3.0. Contributions are licensed under the same terms (ADR 0036).