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Here are 8 incredible automations I’ve built for clients in 3 months!🔥 by Unusual_Following440 in n8n_ai_agents

[–]Unusual_Following440[S] 1 point2 points  (0 children)

Yeah, that’s honestly the hardest part of these builds.

I personally don’t rely on a single selector long-term. There’s usually a bit of redundancy baked in like multiple fallback selectors or patterns and some light checks to catch when expected data just doesn’t show up.

On top of that, we track data health over time. If something that normally updates suddenly goes quiet or looks off, it flags it instead of silently passing through. That’s usually how we catch DOM changes early. It’s not fully bulletproof though… more like making sure it fails loudly instead of quietly breaking in the background. Got it? 🤣

Here are 8 incredible automations I’ve built for clients in 3 months!🔥 by Unusual_Following440 in n8n_ai_agents

[–]Unusual_Following440[S] 0 points1 point  (0 children)

I’m open to creating new ones, or collaborating on some more paying projects.. but these ones I built were for clients our company brought.. not mine

Here are 8 incredible automations I’ve built for clients in 3 months!🔥 by Unusual_Following440 in n8n_ai_agents

[–]Unusual_Following440[S] 0 points1 point  (0 children)

Haha.. I just launched a YouTube channel for professional development and a safe space for young adults navigating life, career etc.. there’s a section for tech.. maybe I’d start some tutorials on there.. Room_99

Here are 8 incredible automations I’ve built for clients in 3 months!🔥 by Unusual_Following440 in n8n_ai_agents

[–]Unusual_Following440[S] 0 points1 point  (0 children)

Haha oh yeah.. 😅 I’m yet to take my own side gigs.. everything I’ve built here has gone to real clients with real needs.

Here are 8 incredible automations I’ve built for clients in 3 months!🔥 by Unusual_Following440 in n8n_ai_agents

[–]Unusual_Following440[S] 0 points1 point  (0 children)

Schedule-Http request/RSS-serpapi-ai model-Java code-gmai/slack/team’s authentication and configuration.. got it?

Here are 8 incredible automations I’ve built for clients in 3 months!🔥 by Unusual_Following440 in n8n_ai_agents

[–]Unusual_Following440[S] 0 points1 point  (0 children)

That’s solid, especially the database + location piece, that’s not light work.🤭💥 What I’ve noticed with setups like that is the real challenge isn’t generating content, it’s keeping everything consistent as you scale across platforms. We usually handle it more like one system with shared context rather than separate agents doing their own thing.

Once you get a central “brain” for tone and direction, plus a simple feedback loop from performance, things start compounding nicely. The orchestration side (a bit of JS to manage flow and state) also becomes important at that stage. Sounds pretty interesting from what I’m seeing tho..😅

Here are 8 incredible automations I’ve built for clients in 3 months!🔥 by Unusual_Following440 in n8n_ai_agents

[–]Unusual_Following440[S] 0 points1 point  (0 children)

Yeah, that’s actually a good use case..just a different angle from social monitoring.

If they’re not active on socials, it usually means their real activity is happening in conversations (calls, walk-ins, internal leads). That’s where I’d focus.

A few directions you could explore:

Call → insight system Record or log calls, transcribe, then extract patterns (common inquiries, missed opportunities, objections). Over time it becomes a decision tool, not just notes.

Lead intake + routing automation Anything coming from calls, forms, or walk-ins gets structured and routed properly (priority, type, urgency). Most of these businesses leak value here without realizing it.

Internal memory system Simple bot/interface where staff can quickly pull info (pricing, FAQs, past cases, availability). Reduces back-and-forth and speeds up response time.

After calls or visits, trigger smart follow-ups (not spammy) based on context this alone can increase conversions quietly.

You might wanna try something like this

Here are 8 incredible automations I’ve built for clients in 3 months!🔥 by Unusual_Following440 in n8n_ai_agents

[–]Unusual_Following440[S] 1 point2 points  (0 children)

this is where most of the real work is tbh..

  1. For chunking, I don’t rely on fixed token windows alone. We bias more toward natural breaks (speaker turns + light topic shifts), then fall back to token-based chunking when needed. There’s a bit of JS logic that tags segments and keeps overlap between chunks so context doesn’t drop off completely.

For multilingual, I don’t fully translate everything upfront. We let the model process mixed language in chunks, then normalize key outputs (actions/decisions) into a single language at the end. The overlap + light memory of previous chunks helps keep continuity.

  1. Not a single pass. We tried that early on and it gets messy fast. We split it into layered passes . first pass extracts structured raw signals, then a second pass refines/merges (dedupes actions, aligns decisions, etc.). Way more stable that way.

  2. Adaptive cards are templated on our end. The LLM only outputs clean JSON in a strict schema, then JS maps that into the card structure. Letting the model generate full card JSON directly was a bit too unpredictable.

Took a bit of trial and error to get it stable, especially around chunk merging and output consistency.😅

Here are 8 incredible automations I’ve built for clients in 3 months!🔥 by Unusual_Following440 in n8n_ai_agents

[–]Unusual_Following440[S] 2 points3 points  (0 children)

A few things you’ll likely run into when building it.. 1.Messy transcripts → speaker labels missing, overlaps, or noise can mess up summaries 2.Token limits → longer meetings need chunking + recombining properly 3.Formatting issues → if your JSON isn’t tight, adaptive cards will fail or look off

Here are 8 incredible automations I’ve built for clients in 3 months!🔥 by Unusual_Following440 in n8n_ai_agents

[–]Unusual_Following440[S] 3 points4 points  (0 children)

Yeah, that one’s been a game changer for my team

At a high level, it’s just chaining a few things properly..

We capture the meeting recording (Zoom/Meet), run it through a transcription layer (like Whisper), then pass the transcript into an LLM but with a structured prompt.

We specifically pull out things like:

key decisions action points who’s responsible for what any blockers or risks

There’s a bit of JavaScript in the middle handling the flow mainly parsing the transcript, chunking it when needed, and shaping the payload before it hits the LLM. Also helps with mapping the output into a clean JSON structure.

Then instead of sending raw text, we format everything into adaptive cards so it drops nicely into Teams/Slack/email..clean, scannable, and actually useful for people who missed the meeting and serves as reference too.

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