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Developed an AI-powered real estate assistant using n8n that interacts with clients in real time, answers property inquiries, schedules appointments, and retrieves property information automatically. The workflow integrates OpenAI models, memory handling, Google Calendar, and property databases to provide intelligent and contextual responses. Built to automate lead qualification, customer support, and appointment booking while improving response speed and customer engagement for real estate businesses.
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Connect with clients, get discovered, and run your business 100% commission-free
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Real Estate Mobile App With Booking Flow
Designed a full end-to-end iOS property app covering every screen from listing browse to booking confirmation built for real estate platforms that need more than a feed.
𝗪𝗵𝗮𝘁 𝘄𝗮𝘀 𝗱𝗲𝗹𝗶𝘃𝗲𝗿𝗲𝗱:
⌐ Home feed with Rent/Buy/Sell toggles, filter controls, and personalized greeting
⌐ Map explore screen with NYC listings and live price range bubble ($2.85M–4.85)
⌐ Property details with full-bleed photography, 5 spec tags, description, and sticky Book Now CTA
⌐ Book A Visit screen with March 2026 calendar picker and 11:30 AM scroll-wheel time selector
⌐ Payment screen with Mastercard, PayPal, and Apple Pay options plus full cost breakdown ($5,864.00)
⌐ Booking Confirmed screen with checkmark closure and visit scheduled confirmation
Built for proptech startups, real estate platforms, and property marketplace founders who need a complete mobile booking experience, not just a listing feed.
If your platform needs a mobile app that takes users from browse to booked in four taps, this is the kind of work we do.
Tools: Figma
Let's build something better. 👋
Really satisfying to look at
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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