r/n8nbusinessautomation

I am building the outreach stage of a larger AI sales prospecting system, but I did not want the model sending messages directly to prospects without review.

This workflow starts by pulling prospects that have already been researched, qualified, and synced. An outreach agent uses the available prospect and company context to generate a structured draft.

Instead of sending that draft externally, the workflow emails it to a reviewer and waits for a response.

If the message is approved, the workflow sends it and updates the prospect record. If changes are requested, the feedback is passed to a separate revision agent. The revised version goes back through the same approval loop rather than being sent automatically.

I used separate generation and revision agents because the two tasks need different instructions. The first agent focuses on creating a relevant message from the research. The second needs to preserve the useful parts of the draft while applying specific human feedback.

There is also a practical question around review fatigue. Requiring approval for every message provides control, but it could become the bottleneck once volume increases.

For people running human-in-the-loop outreach, do you review every message or only drafts below a confidence threshold?

Would you keep approvals inside email, or use a dedicated review interface with explicit approve and revise actions?

Workflow:

https://gist.github.com/meeramnoor16/af99d2d78198381880483fe551164b12

Two channels are running on this right now.

Math Unlocked, statistics and machine learning explained visually. 15 videos, 6 subscribers, 783 views total. Small, still early.

Financial Reality Check, personal finance breakdowns using real numbers instead of generic advice. 6 videos, 17 subscribers, 326 views.

Neither is going viral. That's not the point of this post.

What I actually want to show is the machine itself. The screenshot is the real n8n workflow, script generation, scene-by-scene visual generation, voice synthesis, upload, and even automated Discord posting when a new video goes live, all wired together, all running without me touching a timeline.

I'm not going to walk through the full architecture here, that's genuinely a longer conversation. I've been documenting the actual build, the bugs, the dead ends, the fixes, in a Discord for people who build this kind of thing themselves. Not a course, not a funnel, just the real build log as it happens.

Can't drop links in this post, so if you want in, comment "ADD" and I'll send it your way.

👋 Hey n8n Community,

A few weeks ago I built a Slack-based CV assistant for a friend's recruiter, who was drowning in CVs of every imaginable format. Since it landed well, I cleaned it up and pushed it to the n8n template library: Summarize candidate CVs in Slack with easybits Extractor.

What it does:

Recruiter drops a CV (PDF, PNG, or JPG) into a dedicated Slack channel → bot downloads it → runs it through the easybits Extractor with 8 fields → posts a clean structured summary as a threaded reply in the same channel. No leaving Slack, no manual reading, no format guessing.

How it's set up:

The trigger listens for new messages in the channel, ignores its own posts and anything without a file, checks the file type (PDF/PNG/JPG), downloads the private file with a bearer token, and sends the binary to the Extractor. The Extractor returns 8 structured fields, all with a "return null if not present" rule so the summary stays clean:

• full_name
• location
• total_years_experience
• top_skills (top 3 as short noun phrases)
• last_three_roles (title, company, start, end)
• education (degree, institution, year)
• salary_expectations (verbatim string, not normalised)
• linkedin_url

I also made a short video showing the workflow in action so you can see the recruiter flow end to end.

Want the Save-to-Sheet buttons too?

The template above also posts an interactive action card with Save to Sheet and Dismiss buttons under each summary. The workflow that handles those button clicks (appending the candidate to a Google Sheet, updating the card to "✅ Saved by user") is a separate n8n workflow, Slack interactivity needs its own webhook endpoint, so you can't have the trigger and the button listener in the same workflow.

That second part is on my GitHub: felix-sattler-easybits/n8n-workflows, together with 20 other workflows ranging from invoice classification and PO extraction through to more recruiting-side ones like this.

If any of these are useful, I'd hugely appreciate a ⭐ on the repo.

What other recruiter-side workflows are people building in n8n? Curious how far others have taken the ATS integration side of things.

Best,
Felix

I shouldn't be sharing this. But I just built something that replicates How posts get 200K+ impressions.

Giving it away for free.

𝗜 𝗴𝗼𝘁 𝗼𝗯𝘀𝗲𝘀𝘀𝗲𝗱 𝘄𝗶𝘁𝗵 𝗼𝗻𝗲 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻:

Why do some posts get 847 views while others get 847,000?

✔︎ Same platform. ✔︎ Same 24 hours.

So I analyzed 100+ viral posts. Found the pattern.

It's not better writing. It's not the topic. It's structure.

Viral posts follow a formula.

Hook placement. Sentence rhythm. Psychological triggers.

— The top 1% know this. — They use it every time.

I coded it into a Claude project.

Tested it for 30 days:

Impressions went from 800 to 12,000 average. Some hit 40K+. One hit 127K.

Same audience. Different structure.

This separates average content from viral. Not luck. Structure.

I'm giving you the formula.

Complete Claude project. Trained on 100+ viral posts.

Ensure we are connected first, then Comment "𝗖𝗟𝗔𝗨𝗗𝗘" below. Go to this link - https://www.skool.com/corporate-automation-hub

Repost this and I'll send you the complete setup guide.

First 500 people only.

I sold this AI automation for $10,000.

Want to know how we cut document processing time by 50%?

So I met with an Australian migration visa company. They help businesses hire foreign talent legally. Their process was just brutal.

→ 8 hours per client. Every single day. → Using ChatGPT but hitting walls. → Long chats and constant reprompting. → Manual research and copy-pasting.

Sound familiar?

So we built an n8n workflow that changed everything. A custom GUI for uploads. 6 automated documents.

First, we tried one big agent. That failed. Poor performance. Too many hallucinations.

Then we tried something different. Each section got its own specialist agent. The title page? Dedicated agent. Labor market testing? Another agent. Salary justification? You guessed it.

Quality skyrocketed. Accuracy improved dramatically.

We fed each agent examples of their previous documents. Showed them exactly how to structure things. Tables. Bullet points. The works.

We added tools too. No more manual searching. Agents access Perplexity for deep research. Pull information directly into sections.

The result? 50% time savings. They can process double the documents now. Without hiring more people.

🔥 Here's a pro tip: when building workflows for clients, we focus on the n8n infrastructure. We give them the basic prompts, then they handle the prompt tweaking for their specific needs.

1 limitation: screenshots. Cloudflare blocked some protected sites. We're working on that.

This isn't just for migration companies. Legal documents. Medical underwriting. Insurance forms. Any document-heavy process can benefit.

🟣 First Connect with me, so I can DM you the workflow then, 🟣 Comment "NANOAGENT" to get my nanoagent framework guide

Follow Ritesh Kanjee and Augmented AI to never miss a post.

Hey,

I kept seeing the same thing with n8n. People build a workflow, test the happy path once, flip it on, and then something weird happens in production. Duplicate webhook. Missing field. Stripe retry. Suddenly you emailed half your list twice or charged someone wrong.

So I built Crash Labs.

You paste your n8n workflow JSON. It runs it against a fake company (mock email, Stripe, Slack, etc). Then it shows the blast radius. What would have been sent, charged, created. Pass / warn / fail.

It is early. Not every node is covered yet. Coverage and blind spots are in the report so it does not pretend it proved more than it did.

If you build client automations or anything that touches money / customers, try dumping a workflow in and tell me what it gets wrong.

the first one is already free. If you tell me your email you signed up with i can grand you more so you can test the product full!

Happy to take brutal feedback.

www.crashlabs.app

This is the second subworkflow in a larger AI sales prospecting and CRM intelligence system I am building.

The first workflow handles company research and ICP qualification. This one deals with what happens when a prospect is ready to enter HubSpot.

The workflow pulls pending prospect records and searches HubSpot for an existing contact. If the contact exists, it updates the record. If not, it creates one and stores the returned contact ID.

It then repeats the same process at the company level, using the company domain to decide whether to update an existing company or create a new one. Once both IDs are available, the contact is associated with the company, the generated contact intelligence is added, and the prospect is marked as synced.

The main design choice here was to deduplicate contacts and companies separately. A contact may be new while their company already exists, or the opposite. Treating the whole operation as a single create-or-update action would miss those cases.

The part I am still thinking through is identity resolution. Email and domain work well for straightforward records, but things get messy with subsidiaries, personal email addresses, multiple company domains, and contacts who change employers.

How are people handling these cases in production?

Would you keep email and domain as the primary matching keys, or add a second layer of fuzzy matching before creating anything?

Here is the workflow:

https://gist.github.com/meeramnoor16/611c65ef056d531680c6cd4f0a9aa033

👋 Hey n8n community,

A while back I posted a pipeline I built for a friend who runs an online shop, turning his delivery documents into finished product content (that post here). He came back with a follow-up: he wanted SEO in the same place.

Not an SEO platform. His words were roughly "I already come here to create content, I want to come here to fix content too." That framing is why this got built the way it did.

Why not just use Search Console? He has it. He opens it once a quarter, squints at a graph, closes it. The data was never the problem. Search Console tells you a page sits at position 8.9 with 0% CTR, and then stops. It won't rank your 3,000 pages by opportunity, won't tell you what's wrong on the page, and won't write you a better meta. So the real workflow was five tools deep before anything got fixed.

How it's set up:

Quick wins across the site. Pulls every page from the Search Console API, scores each one in a Code node, returns a ranked list. Two signals: striking distance (position 5-15 with real impressions, closest to page one) and low CTR (lots of impressions, barely any clicks, so the title and meta aren't earning it). No page fetching, no LLM. It's fast because it's boring.

Analyze a page. Paste a URL. It fetches the live HTML, pulls the title, meta, headings, alt text and schema, grabs that page's GSC queries, scores it, and hands back paste-ready rewrites grouped by section and ordered by severity.

The two chain: each quick-win row expands and runs the full analysis inline, on click. So you only spend an LLM call on a page you actually decided to work on.

Three things worth stealing:

Deterministic JS scores, the LLM only writes. Fixed rules emit the flags and the severity order. Gemini never grades or reorders. If the model finds the problems and ranks them, you get confident nonsense in an order you can't audit.

Verify every rewrite after generation. A second Code node re-checks each one against the hard limits. First real run, Gemini returned a meta it was very happy with. 160 characters. Limit is 155. That would have shipped truncated.

Rolling date ranges on the GSC query. I hardcoded a window that ended before my data started. GSC returns a clean 200 with no rows, so every node stays green and reports "no data available". Nothing errors. Use new Date(Date.now() - 90*24*60*60*1000) and end 3 days back, since GSC runs ~48h behind.

Both workflows are on GitHub: https://github.com/felix-sattler-easybits/n8n-workflows/tree/367accdd405397366fc93ff391f592d3ec72cc41/easybits-ai-seo-support-workflow

I also made a short video showing how the workflow works.

What signals would you score beyond striking distance and CTR? That's the part I'd most like to improve.

Best,
Felix

I built an AI agent that creates videos while I sleep. Sold it for $2,530 to a marketing agency.

Last night it produced 12 pieces of content while I slept.

This agent handles the entire content pipeline: → Pulls data from emails and Google Drive → Generates images with Flux Dev → Edits visuals with Gemini Nano Banana → Creates videos from text prompts → Converts images to dynamic content → All via Telegram commands

The agency was spending 20+ hours weekly on content. Now their team focuses on strategy while the agent handles production.

That's the power of high-ROI automation. Not replacing humans - freeing them to do work that matters.

Want to see how this works for your business?

🟣 To get my library of free 60+ corporate AI workflows go to this link https://www.skool.com/corporate-automation-hub

Follow Ritesh Kanjee and Augmented AI for more automation insights.

I am working on a modular AI sales prospecting and CRM intelligence system, and I decided not to build the whole thing as one large workflow.

This is the first subworkflow: company research and ICP qualification.

The flow starts by collecting leads, processing them one at a time, and sending each company through an ICP scoring agent. Leads that pass the qualification step continue to a product matching agent, which tries to identify the most relevant offering based on the company profile.

The workflow then fetches the company website, extracts the page content, and passes it to a separate research agent. That agent creates structured company intelligence before the final record is added to the database.

I separated the agents because I want each part to have a narrow responsibility:

• ICP scoring decides whether the company is worth pursuing
• Product matching decides what might be relevant to them
• Company research gathers context for later outreach and CRM enrichment

This is still an early version, and I am thinking through how much confidence to place in website content alone. Some company sites are outdated, vague, or full of marketing language, so the research output may appear structured yet be inaccurate.

For people building similar prospecting workflows, would you use a fixed scoring rubric for ICP qualification, or let the model interpret the criteria more freely?

I am currently not using a GoHighLevel-type CRM. I wanted to maintain a proper record in HubSpot, but I hit the limit of 10 custom properties. What record managers do you guys use when testing workflows? Cheaper options, please!

Here is the workflow:

https://gist.github.com/meeramnoor16/87fe4f5b34b2293e04c831a707644cd0

Full disclosure: I'm the developer and founder.**

 

 I've been building DVOcean — an engine where you drop in a voiceover audio file (or just a topic) and the pipeline does the rest: fetches contextually relevant footage for every scene, cuts it to the audio, and bakes in captions automatically. The goal was simple but technically brutal: zero human editing.

 

 This screen recording is the entire flow, from dropping in the audio to the raw, untouched output.

 

 I'd love blunt feedback:

 1. How does the visual pacing and caption timing look to you?

 2. If you run a faceless channel or video podcast, what's missing that would make this a daily tool?

 

 (There's a free 1-minute trial if anyone wants to stress-test it with their own audio — link in comments so I'm not spamming the post.)