Case study · AI & Automation

190+ production n8n workflows

Bots, API syncs and scheduled jobs that keep running while I sleep

196workflows, self-hosted

~940production runs / week

Client: Self-hosted - client and internal automations
Role: Designed, built and operate the suite
Stack: n8nDockerWebhooksSSHLLM APIs

The context

A self-hosted n8n instance running close to two hundred workflows: WhatsApp and Telegram bots, API synchronisation between systems, scheduled jobs, and operational tooling such as deploy and log management.

What I did

1
Self-hosted on Docker
Runs on infrastructure I control, triggered by webhooks and schedules, so cost and data stay predictable.
2
Automation for real operations
Examples include a git-push to production deploy with secret and branch validation, a form-driven server log manager over SSH, and multi-client social publishing pipelines.

Workflows in the wild

**Git-push → production deploy**Bitbucket webhook → secret & branch validation → SSH deploy → Gmail notification; unauthorized calls rejected.

**Server Log Manager v2.1**Form-driven ops tool: fetch / fetch-and-delete / delete logs over SSH, parse output, error branches, email logs as attachments.

**Complete Social Automation System**100+ node pipeline: Google Sheets / form input → AI content generation → content recycling → multi-platform publishing → analytics.

Daily AI Avatar Video Pipeline9 AM trigger → locks a topic row → AI agent (Claude via OpenRouter) writes script + captions as validated JSON → HeyGen renders long & short videos in parallel with polling/timeouts → publishes to YouTube, Shorts, TikTok, Instagram, Facebook, X → updates sheet & emails; dedicated error path.

**Unified Multi-Client Social Posting**One engine serving many brands: per-client config, AI image/video, 10+ platform branches.

Need something like this built?Full-stack platforms, cloud infrastructure and AI agent automation — available for freelance and consulting work.

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190+ production n8n workflows

The context

What I did

Self-hosted on Docker

Automation for real operations

Workflows in the wild

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