A system i made for a Real-estate investing company.
System Name: DeepSite
Problem: My client was stuggling with the manual analysis of unqualified properties. Wasting 10's of hours ber week that should be spent of closing deals instead of trying to validate them.
Solution: I built an automation that utilized AI,Google maps satellite images as well as street view images to analyze the property and tell it's Type,Condition as well as a overall summary.
Results: First of all, my client got a lot of time back. Secondly his process got much much faster because the system was capable of conducting analysis faster than your average VA. And Lastly the information that this system generated about the property helped my client close deals at a better bargain.
Next time, it's going to be even better due to zillow integration!
An AI-powered phone receptionist built for real estate agencies that answers inbound calls, qualifies leads in real time, and books showings directly to Google Calendar — all within the call itself, with no manual follow-up required.
The problem: Real estate agencies routinely lose leads to missed calls — after-hours inquiries, calls during showings, or overflow during busy periods. A slow callback often means the lead has already moved on to a competitor.
What it does:
Answers every call instantly, 24/7
Naturally qualifies the caller (buy/sell intent, area of interest, showing vs. agent call)
Collects contact details and preferred timing conversationally
Checks real-time calendar availability before booking
Prevents double-bookings and duplicate entries automatically
Confirms the appointment out loud before ending the call
Built with: Retell AI (conversational voice layer), n8n (workflow automation and business logic), Google Calendar API (scheduling)
Available for: Custom builds for real estate agencies, brokerages, or property management companies looking to stop losing leads to missed calls
Agent products resist normal product marketing. There's no single interface to screenshot and the category vocabulary has gone generic, so a feature list communicates almost nothing.
We built Heron's explanation as a four-beat scroll that runs the agent's actual loop. The model appears and is watched. Violations surface with plain language and a real IBC clause. An edit is applied after approval. The system learns the firm's standards.
Pacing was the main design problem. Each beat needed enough scroll distance to register as a separate action, without the section becoming so long that people bail before the payoff. The sequence carries the technical detail that would otherwise sit in a spec table.
The four-beat scroll is a smart way to make the agent’s loop feel tangible—much clearer than a static feature list. The restrained visual system gives the interaction room to breathe.
AI Lead Qualification & Appointment Automation System
AI Lead Conversion Platform — Telegram, CRM & Automated Scheduling
Designed and developed an AI-powered lead conversion system that handles incoming Telegram conversations from first contact to qualification, CRM synchronization, and appointment booking.
The AI works with conversation history, lead profile data, unanswered questions, current funnel stage, available actions, and verified CRM data to determine the next appropriate step and generate structured responses.
The system automatically creates and updates leads in the CRM, manages qualification stages, books meetings, generates Yandex Telemost links, sends reminders, handles rescheduling, and escalates exceptional cases to a human manager.
The architecture includes dialogue state management, message deduplication and batching, PostgreSQL persistence, concurrency protection, validation before actions are executed, and safeguards against sending unverified or prohibited information.
This is not just an AI chatbot — it is an automated sales workflow that connects Telegram, AI, CRM, scheduling, and human escalation into one controlled process.
AI makes decisions within controlled business rules instead of operating as an unrestricted chatbot.
The concurrency safeguards and validation-before-actions detail is a strong distinction here—booking systems usually fail at the edges, not in the happy path. I’d expose a compact lead state to the operator (stage, next action, last unanswered question) so a human can resume...