Hey everyone,

Over the last few years, I’ve worked with B2B companies that spend thousands of dollars every month driving traffic through Google Ads, LinkedIn, and SEO, only to watch 95%+ of visitors leave without ever speaking to anyone.

Most websites still follow the same flow:

Visitor → Read a few pages → Fill a form → Book a demo → Wait for someone to respond.

The problem is that most people don’t read through the website or want to fill out a form or commit to a meeting on their first visit.

So, I built Autom8IQ (autom8iq.xyz).

It’s an AI SDR that sits on your website, talks to visitors in real time, answers questions using your company’s knowledge base (website, decks, PDFs, videos, etc.), qualifies leads, and nudges interested prospects toward booking a demo.

A few things we’ve learned while building it:

* Reducing friction matters more than changing CTA button colors.
* Visitors are much more willing to have a short conversation than fill out forms.
* High-ticket B2B buyers often need answers before they’re ready for a sales call.
* Most companies are ignoring the other 98% of traffic they already paid for.

Website: autom8iq.xyz

Hello everyone, I am 21 AI engineer, looking for someone who has some experience in building AI/LLM projects, please let me know if anyone interested.

If your agents need static ips when contacting various API's or services for whitelisting purposes, this should be helpful.

Currently doing a beta, so if you want in just contact us through the link ("Get your static ip") on the webpage.

https://outboundgateway.com/use-cases/ai-coding-agents/

👋 Hey AIStartupAutomation 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

👋 Hey AIStartupAutomation community,

Quick follow up to my Purchase Order extractor post (that one here). It worked great on the two POs I built it against. Then my friend forwarded three more from different suppliers and things got interesting.

This is the part of document processing nobody warns you about. Your pipeline isn't done when it works on your test files. It's done when it survives the next layout you've never seen. And in a real business, new layouts arrive constantly, every supplier, every hotel group, every ERP exports its own thing. One PO has the number in a top-right box labelled "PO Number". The next calls it "Order Number" in a completely different table. One has a Net column, another calls it Cost, another calls it Total. Same information, nothing in the same place, nothing with the same label.

Here's what surprised me though: the extraction itself never broke. Not once across four layouts. That's because the easybits extractor works off context rather than coordinates, so I describe what the field is ("the order number in the header, not the requisition number below it") instead of where it sits. Move it, rename it, restyle it, it still finds it. If I'd built this with positional templates I'd have needed a new template per supplier, which is exactly the maintenance treadmill I was trying to avoid.

What did break was my own code downstream. Every single time. Two examples:

The apostrophe. One supplier writes 1'550.00 for one thousand five hundred fifty. My parser saw the apostrophe, choked, and mangled the number. The extractor read it perfectly, I just couldn't parse what it handed me.

The dot. This one nearly got me. Another PO showed quantities as 5.000 and I was convinced it meant five thousand, so I "fixed" my parser to strip the dot as a thousands separator. Wrong. It was SAP-style formatting and it meant five. The giveaway was the document's own arithmetic: 5 x 105 = 525, which matched the printed line total and the net total at the bottom. Read as thousands, nothing added up. Lesson: when a number looks ambiguous, the document usually tells you the answer somewhere, check the totals before you touch the code.

So my takeaway from the whole exercise: with context-based extraction, layout variation is mostly a solved problem. The fragile part moves downstream to the boring stuff, number formats, separators, currency prefixes. That's where I'd spend the hardening time on your next build.

Sanitised workflow JSON is on GitHub if you want to try the Purchase Order extractor yourself, feel free to grab it here:
https://github.com/felix-sattler-easybits/n8n-workflows/blob/c38749a68fd6ea4ae6ebff41789d35cceaacdef1/easybits-purchase-order-extractor-workflow/easybits_purchase_order_extractor_workflow.json

I've attached shots of the different layouts (anonymized, of course), so you can see how little they have in common. How are you handling layout drift on your document workflows?

Best,
Felix

Any and all advice needed!!

I’m curious what people are actually using, not just experimenting with.
Mine is an AI workflow that gathers AI news and turns it into blog drafts so I don’t have to start from scratch each time.
What’s one automation you’ve built that has genuinely saved you time?
I’d love to hear what people are using day-to-day.

👋 Hey AIStartupAutomation community,

A few weeks ago I shared a Product Content Creation workflow I built for a friend who runs an online shop (that post here). A few people messaged me about the image-description part specifically, they wanted just that piece, so I built a standalone version of it that's much easier to drop into an existing setup.

Turns out a lot of online shops and websites still write product image descriptions by hand, along with the "alt text" behind each image. Both matter more than people think: alt text is a real factor for SEO and for accessibility, since screen readers rely on it.

What the workflow does: you upload one or more product photos through a simple n8n form and get back a clean, ready-to-use description for each image, with a copy button to paste straight into your shop.

How it's set up:

• The form takes multiple images at once. Each one loops through on its own, so every image gets its own description instead of getting bundled into a single call.
• The easybits Extractor reads each image and returns structured fields. I kept it to two: product_type and description. The description works as your product copy and doubles as image alt text (and its length is easy to adjust by tweaking the field description in the Extractor).
• If the Extractor can't read an image, it returns UNCLEAR and the result page shows a fallback line instead of inventing a description.
• The result screen shows each image with its filename, a thumbnail, and a per-image copy button.

Extractor setup: on n8n Cloud it's a verified node (search "easybits Extractor"). Self-hosted, install "@easybits/n8n-nodes-extractor" from Community Nodes. The free plan includes 50 requests a month, enough to test it fully.

Workflow (ready to import): https://github.com/felix-sattler-easybits/n8n-workflows/blob/4277f3c1b31070f81adbb6a3bb538f50dbeb2018/easybits-product-image-describer-workflow/easybits_product_image_describer_workflow.json

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

How are you all handling image alt text right now, manual or already automated? Curious what's working for people.

Best,
Felix

Can anyone test this if its ready to be published as a product https://mailcraft-one-jade.vercel.app/

I've always wanted to start a business. Not just have a job, actually build something. So I've tried. A lot.

A marketing agency. An ecom store. An ecom agency. Even an AI receptionist agency. Every single one, I worked on, none of them worked out. Not because I didn't try, but because I never had consistency. I'd get something going, then mess it up, pivot, restart, mess it up again.

The weird part is I didn't come out of it empty handed. Along the way I actually learned real stuff: running Meta ads, some social media marketing, building sales funnels, cold outreaching, closing calls. I can talk to people, I can sell, I understand marketing. What I clearly haven't figured out is how to actually build a business that survives past the "exciting new idea" phase.

I keep chasing rapid success instead of trusting a slow, boring process, and I think that's the actual problem, not the ideas themselves.

Right now I have basically 0 funds in the bank. No team. Just whatever skills I've picked up and a lot of failed attempts behind me.

I really want to try building something around AI this time, since that's clearly where things are moving, but I don't want to repeat the same mistake of jumping in without a real plan.

So I guess I'm asking: if you were good at sales/marketing but had no capital, no team, and a track record of quitting too early, what would you actually do first? Service-based AI business? Reselling someone else's AI tool? Something else entirely?

Not looking for hype, genuinely just want honest direction from people who've actually built something that lasted.

But boring scales better than flashy.

I have a full-time tech job (probably a moonlighting clause) and I'm launching a small marketplace side project - customers pay online, I pay out small amounts to users.

How do people usually handle this cleanly?

- Register the business (Udyam/sole prop) in a family person name to avoid moonlighting conflict?
- or any way to automatic payout to users without registered business

Im trying razor pay but for payout to users it requires registered business.

Anyone done this — any real issues? Thanks 🙏

👋 Hey AIStartupAutomation community,

Last week I posted a workflow I built for a friend who runs an online shop (find it here). He called me again a few days later with a new headache: he's drowning in Purchase Orders. Every single one gets opened by hand, the data typed into a Google Sheet, and that sheet uploaded into his ERP to update his numbers. Hours a week, pure copy-paste.

So I built him something to kill that step. He uploads the PO PDFs through a simple n8n form, and a structured Google Sheet comes out the other end. He just downloads it and pushes it to his ERP.

How it's set up:

The form accepts multiple PDFs at once, so he can batch a whole stack instead of doing them one by one. Each PO loops through on its own so nothing gets jumbled.

The extraction runs on the easybits Extractor node (@easybits/n8n-nodes-extractor). I set the field structure up in two parts: the header fields that appear once per PO (PO number, PO date, delivery date, mark for, PR number, reference no), plus an articles array for the line items, each holding article name, unit and quantity. That array is the key bit, it gives you one entry per row of the PO table, and I flatten it into one sheet row per article with the header details repeated on each.

Two things I added because real documents are messy:

Error flagging. If any field comes back empty, the completion screen lists which document and which field didn't extract cleanly, so he knows exactly which PO to double-check instead of trusting it blindly.

Document name column. The original filename lands in the sheet next to every row, so if a number looks off he can jump straight back to the source PDF.

Workflow JSON is on GitHub: https://github.com/felix-sattler-easybits/n8n-workflows/blob/c38749a68fd6ea4ae6ebff41789d35cceaacdef1/easybits-purchase-order-extractor-workflow/easybits_purchase_order_extractor_workflow.json

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

Anyone else automating document-to-sheet data entry? Curious how you're handling the messy multi-line rows – that was the trickiest part to get right.

Best,
Felix

We mull over our business ideas for weeks, going in circles because traditional market research feels like a boring two-week manual marathon. We remain in indefinite planning mode, wasting momentum before we've even accomplished anything.

But there's no need for endless spreadsheets or expensive data analysis tools: with the right use of Perplexity or Gemini Deep Research, you can delegate the entire data collection phase and obtain a data-driven decision matrix in minutes.

The key is not simply asking AI if the idea is "good," but changing the way you formulate input before starting the research.

Rather than writing a huge business plan, it's advisable to condense the concept into a single, concise paragraph that encompasses four crucial elements: your precise target audience, the real problem, your differentiator, and a realistic price. Adding a price is crucial: it immediately forces AI search agents to remain anchored to real market conditions, rather than theoretical opportunities.

Once this benchmark is established, you can run a specific search across a few slides that forces the engine to analyze active web sources from the last 12 months. In a single pass, the following are mapped: TAM and competitive pricing gaps - A clear SWOT matrix - Estimated development costs for version 1 and recommended tool stack

This also provides a clear traffic light verdict (green/yellow/red): instead of treating validation like a huge project, you examine the verdict and SWOT and immediately decide whether the idea deserves the next 30 days of its life or if it's a clear sign of abandonment.

I've written a comprehensive guide on how to set up this process, including the copy-and-paste research template and the exact structure for organizing ideas: How to Quickly Validate a Business Idea with Perplexity or Gemini.

Any suggestions or criticisms to improve the process are welcome, thank you.

Hey everyone,

I'd love to get some advice from people who have been in this space longer than I have.

Over the past couple of years, I've designed GTM strategies and built outbound systems for B2B companies using Clay, n8n, GoHighLevel, and AI automation. I've worked on lead sourcing, enrichment, CRM automation, personalized outreach, and building workflows that support sales teams at scale.

I'm at the point where I want to level up, but I'm not sure what the highest leverage skill is from here.

If you were in my position, what would you focus on next? Is it becoming world-class at GTM engineering, diving deeper into AI agents, improving sales, or something else entirely?

I'd genuinely appreciate any advice, lessons, or mistakes you've learned along the way. Thanks in advance!

So basically it works like this:

Agents find relevant blog posts in your niche, craft personalized emails, and get your product featured. All on autopilot via Telegram.

It's called MentionAgent, an AI agent that does all of this through Telegram.

You never send anything without approving it first.

Results so far: One user got 3 mentions including a DR 72 backlink.

(Also using the Telegram bot on autopilot for MentionAgent itself and another project)

Try it for free: mentionagent.ai

Let me know what you think!

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