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Last month I built this automation for a Virtual try-on start-up, so the founder could actually track the sales teams KPI's everyday from just a message in teams at the end of the day rather than go through the whole CRM or not at all.
Totally, it saved around 5-7 hrs/week for the founder, gave the team a boost as well since they could see how much effort they were actually putting in. Quite effective for a start-up as well.
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I'll be honest — my last three posts flopped.
Took me a minute to see why. I'd been writing hot takes when I should've been writing what actually helps.
Here's a small automation I shipped last week that none of my "big" posts will beat for value:
A client kept losing meeting notes. Calls in Google Meet, transcripts in their inbox, action items nowhere. Standard SMB chaos.
The fix:
Google Meet transcript → n8n webhook → GPT extracts action items + owner → posts to their Slack channel + appends to a Notion database.
Now every meeting ends with a clean action list, automatically assigned. They stopped using the word "follow-up" in calls.
Not glamorous. Not contrarian. Just useful.
What's the smallest automation that made the biggest difference in YOUR work? Drop it below — I'm collecting ideas for clients. 👇
#n8n #Automation #Productivity
Agentic AI Workflow for Real Estate Lead Enrichment (USA)
I built a production-grade, engineering-first workflow that turns minimal “clues” (company name, property address, or Tax ID) into a structured owner/contact dossier.
Stack: self-hosted n8n, OpenAI (GPT-5), Airtable (UI + database), Google Search, RealTrack (closed system)
Input → Output: 1–3 clues in → a complete enriched profile in ~60–120 seconds (with citations/links to sources) ready for sales outreach and due diligence.
The Problem (How the team worked before)
The client’s manual process looked like “crossfire research”:
- Find a company name + street / Tax ID
- Open multiple platforms/tabs
- Cross-check signals, collect evidence
- Compile a dossier in spreadsheets/CRM
This was slow (20–45 minutes per request), error-prone, and hard to scale.
I delivered an end-to-end agentic workflow that automates the same multi-platform research the team did manually — but faster and with consistent formatting.
1. ~90% reduction in pre-call research time per lead
2. ~60–120 seconds per lookup vs 20–45 minutes manually
3. ~$0.02 per enrichment request (token cost; excluding optional external services)
4. Self-hosted n8n: dramatically lower operational costs vs cloud pricing at scale
Amazing job Oleh!
Telegram AI Agent with Memory, Tools, and Android Self-Hosting
Bzhela started as an experimental Telegram AI agent but evolved into a full research project focused on long-term memory, controlled context, and autonomous infrastructure. The goal was to build a context-aware assistant that tracks conversation flow and interacts with real-world tools without losing coherence in an active group chat.
The core of the system was a custom memory architecture. To prevent context collapse, I built a rolling strategy where older messages were compressed into a summary while fresh messages remained raw. I used Redis for short-term history and Pinecone for long-term fact retrieval. The agent actively extracted and managed facts, applying time-to-live limits to temporary memories so the database stayed clean.
This setup allowed the agent to be proactive rather than purely reactive. It operated with practical integrations tied to daily life. The agent checked local power outage schedules, managed Google Calendar events, and interacted bidirectionally with an iPhone via Shortcuts. It could trigger phone-side scenarios, like setting alarms or sending warnings based on battery levels.
The infrastructure was deliberately unconventional. Instead of a standard cloud server, the agent ran on a self-hosted Android phone without root. I set up a runtime using Termux, proot Ubuntu, and Node.js, managed by PM2. Cloudflare Tunnel ensured secure remote access. This gave the agent physical survivability via a power bank and access to real-world network signals.
Over several months, the system maintained a stable context, managing around 150 compressed facts and proactively messaging based on real-world triggers. It proved that an AI agent can act as a reliable, system-level tool rather than just a simple chatbot wrapper.
Key technical aspects:
- Custom memory stack using Pinecone and Redis
- Rolling context and automated chat summarization
- Event-driven proactive messaging
- Integrations with Google Calendar, webhooks, and iOS Shortcuts
- Self-hosted Android runtime with Termux and Cloudflare Tunnel
Building rolling memory with Redis + Pinecone on a self-hosted Android stack is serious agent engineering → this goes far beyond a typical “Telegram chatbot.”
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