Import into n8n. Configure credentials. Activate. Agents work 24/7 from there.
Genspark Super Agent + Lindy AI connected via n8n. Content generation → lead processing → calendar scheduling → email — one pipeline.
Personal AI assistant in Telegram. Handles voice messages, checks your calendar, sends emails. GPT-5.6 with memory.
Type a message → AI drafts an email → sends it. Chat → LLM → Structured Output Parser → Gmail. Perfect for reminders, follow-ups, or any templated email.
Send a message via webhook → AI drafts a personalised reply → sends it instantly. 3 nodes. Under 5 minutes to set up.
List topics in a Google Sheet → AI writes the full article, generates a cover image, publishes with Yoast SEO meta tags. Zero manual work after setup.
Adapted for aisuitehq.org. One topic → AI writes article + cover → publishes instantly. No WordPress needed. Pushes directly to GitHub, Vercel deploys in seconds.
Auto-generates promotional videos from content ideas daily. Uses HTTP Request nodes to call RunwayML and ElevenLabs APIs.
24/7 AI phone agent. Handles calls naturally, checks availability, books, confirms. No human needed.
Every workflow uses the same pattern: Trigger → HTTP Request → Action. Once you understand the HTTP node, you can connect n8n to any API.
The same pattern from Lesson 4. Use it to build lead processors, notification systems, report generators.
The exact workflow the Boardroom coaches walked through — n8n + Google Sheets + Gmail:
Import the JSON, connect credentials, test. Customise: add more lead data columns, or drop in an OpenAI node to generate the email copy per lead.
The lesson from the session: "vibe coding" with an LLM to build n8n nodes hits walls fast (one member spent hours on a Dropbox integration). Use a structured approach instead: understand the node types first, prompt with the exact node names, and troubleshoot step by step. The 2,000+ workflow GitHub repo and the Boardroom templates are better starting points than asking AI to invent the flow.
The session's star: a Claude-powered system that turns a plain-English requirement into an 80-90% complete n8n workflow, ready to iterate on.
Examples from the session: a meeting-transcript summarizer (input Quill transcript → summary, decisions, action items) and a marketing spend analyzer (data in → email/Slack reports) — both working prototypes.
The tricks that make it work:
1. Keep a deprecated-node blacklist. Old templates contain nodes that no longer exist — maintain a list to exclude, or Claude will happily build with them.
2. Ask for single-node JSON when iterating. Don't regenerate the whole workflow for one fix — have Claude output just the node you're changing.
3. Fix invalid JSON by having Claude review its own structure. Paste the error, let it correct itself.
4. Use n8n's built-in AI analysis, feed that to Claude. For troubleshooting, n8n's own error analysis + Claude's fix = the fastest path.
5. Embrace iteration. 80-90% on the first pass is the goal, not 100%. The last 10% is where you learn the platform.
It's also a brainstorming tool: describe a high-level flow (e.g. GoHighLevel → n8n → GoHighLevel) and it maps out the structure — perfect for overcoming technical knowledge gaps.
n8n shipped its own natural-language builder. Describe the workflow you want in plain English and it generates it — the session reports it lands 85-95% of the way to a functional workflow.
The rule that makes it work: you need a clear concept of what you want before you start. Prompt-to-workflow isn't a thinking tool — it's a building tool. Know the goal, the trigger, and the output, and it fills in the nodes.
For best results: combine it with the wizard tricks above — adapt an existing template rather than describing from scratch, and iterate on the last 10-15% by hand.
The live demo built a blog-post generator from RSS feeds in ~10 minutes of active work — normally hours. The playbook:
1. Pre-plan with an LLM. Claude/GPT defines the concept and writes the prompt first. The prompt must fit n8n's ~1,000-character limit and guide the agent properly. Example: "I want to create a blog post generator... use two RSS nodes... multiple agents (outline, content, review)... save to Google Sheets."
2. Expect the agent to fail on logic and external services. The demo burned ~11 credits on three real bugs: Google Sheets "no columns found" (fix: create the columns manually), a merge error (fix: tell the agent to simply combine all items), and a scraper rate limit (fix: replace VentureBeat — which blocks scraping — with the Google AI blog).
3. Watch for filter logic traps. The generated filter used OR when it needed AND — "must be from the last 24 hours AND contain one of these keywords." Check generated logic, don't assume it.
4. Build POC → MVP. Start with a simple proof of concept to validate the core logic, then iterate — add more RSS feeds, SEO agents, extra outputs. Never ask for the finished system in one prompt.
The staged generation pattern (from the Dec 18 book-builder demo): for any large content project, split it into stages that pass forward: concept → chapter ideas → per-chapter outlines → content per outline point. Each stage checks consistency before the next. The demo used this for a non-fiction book ("Physics for Teens") — same pattern works for courses, guides, and long reports. One giant prompt produces a mess; staged prompts produce a book.
The token-limit workaround (from the Jan 15 newsletter build): generating a digest directly from raw article data hits LLM token limits and produces weaker output. The fix: loop over each article and summarize it individually, then generate the final newsletter from the summaries. Same principle anywhere — summarize the parts first, synthesize the whole after. The live build failed at the "select best article" step, which is exactly why the multi-step strategy exists.
Watch for the trigger trap: the AI-generated workflow used an unexpected webhook trigger and failed on test. Rebuild with a simple manual trigger first, then swap in the real trigger once it works. Same rule as the "Workflow Configuration" node — strip the AI's clever defaults until the core runs.
Code nodes — the middle ground (from the Dec 11 demo): when no standard node exists, drop in a Code node for custom JavaScript: data validation/cleaning, prompt engineering, complex logic. It bridges the visual interface and full coding power without leaving n8n. The demo workflow: Google Sheets reads briefs → Code node validates → Code node builds prompts → OpenAI node generates text + DALL·E images → Code node formats → Sheets writes back.
The credit strategy (from the Dec 11 demo): use "Build with AI" (1 credit per prompt) for the initial setup, then the FREE "Ask" feature for troubleshooting and refinement. That conserves the 50 monthly credits for what actually needs them.
Bottom line: it's a powerful proof-of-concept tool that needs guidance. LLM pre-planning + incremental builds + hands-on debugging = the workflow that works.
The platform choice rule (from the Jan 24 session): agentic coding tools (AnyGravity, GitHub Copilot) have overtaken n8n for COMPLEX projects — they generate Python, which is more flexible and universal. n8n stays right for simpler automations or when a client specifically wants it. The decision tree: simple or client-required → n8n. Complex or long-term → agentic coding straight to Python. And MCP servers: promising concept, but execution is often flawed — few are practical for real-world use.
Choose the tool by scale (from the Jan 8 session): Make/Zapier for small internal projects · n8n for scalable enterprise-grade solutions (containerizable, hostable for large audiences) · custom Python for maximum performance and cost-efficiency at very high volume — at extreme scale, n8n's cost can exceed hiring a developer to convert it to native code. Same escalation ladder as the 80/20 rule applied to tools.
The session's sharpest point: n8n is for prototyping, not enterprise scale. Once a workflow is proven, "vibe-code" it into a native language like Python to avoid platform fees and gain full control.
The new bottleneck is API integration. Idea generation and coding are solved by LLMs — the hard part now is poor API documentation and dependencies. Expect friction at every external service: the live demo stalled on a fal.ai auth error, and the sheet-reading step failed because n8n read a single A1 value as a header (fix: add a header row, content from row 2).
The workflow doctrine:
1. Meta-prompt. Have an LLM generate the Prompt-to-Workflow prompt for you — well-formatted, effective, and it saves n8n credits.
2. Build in stages. Validate each step before the next. Isolates errors, prevents wasted credits.
3. Anticipate API friction. Documentation lies, dependencies break, auth fails. Budget for it.
4. Prove it, then port it. POC in n8n → validate → rebuild the winner in Python for production.
That's the exact path our kits take: workflow proven in the visual tool, then hardened into a script that runs without platform fees.
n8n itself is free and self-hosted — npm install -g n8n on any machine or server. The ongoing costs are the API credits for the AI models each workflow calls: GPT-5.6 for drafting and reasoning, DeepSeek V4 Flash for high-volume writing at $0.14/M input tokens, ElevenLabs for voiceover, and so on. A typical small-business workflow set runs £50-£200/month in API credits — versus £1,500-£3,000/month for a VA doing the same tasks by hand.
n8n is a visual workflow builder — you connect triggers, logic nodes and APIs into fixed pipelines that run the same way every time. An AI agent (like the ones in the AI Suite store) decides its own steps toward a goal. The practical pattern: use n8n for deterministic processes (lead routing, email sending, data sync) and AI agents for open-ended work (research, writing, analysis). Most businesses need both.
Almost. Each workflow imports as JSON into n8n, then you connect your own credentials — Gmail, Google Calendar, OpenAI/GPT-5.6 keys, ElevenLabs, Twilio. That takes about 5-10 minutes per workflow. If you'd rather not set it up yourself, AI Suite deploys these for you from £149, tested and verified against your own accounts.
For a static site like aisuitehq.org, yes — n8n handles the server-side work (webhooks, email, data processing) without a traditional backend. The "SEO Blog — Static Site" workflow above pushes articles to GitHub and Vercel auto-deploys, no WordPress needed. For heavy, high-traffic applications, prototype in n8n then port the proven workflow to Python (see "Prototype in n8n, Ship in Python" below).
The same things we deploy as agents: missed-call follow-up, lead triage and email sorting, invoice chasing, appointment booking, weekly client reporting, and SEO content pipelines. The difference is n8n suits clients who want fixed, predictable flows; agents suit clients who want autonomy. Browse the store to see what's ready to deploy, or request a proposal for a custom build.