Denys Kucher
Independent AI Automation Architect | n8n & LLM
New to Contra
Denys is ready for their next project!
You're a solo entrepreneur and just got 47 emails.
47 potential clients. 47 opportunities. And only two hands.
Do you pick the two that look best?
THE SCALING PROBLEM NOBODY TALKS ABOUT
When your business grows, you hit a wall:
š“ One brain, three channels (Telegram, Email, DM, Messenger...)
Client writes at 2 PM, you see it at 10 PM. They've already bought from a competitor.
š“ Diagnosis is the bottleneck
Client describes their problem vaguely. Five emails back and forth. They get tired before you get an answer.
š“ You're guessing blind
Which questions convert? What's the average deal size? Which objections are deal-killers? You don't know ā just intuition from 30 deals.
The obvious next step: hire someone.
But let's do the math:
SDR/BDR salary ā ā¬2-3k/month
Bureaucracy (contracts, taxes)
Vacation, sick days
Retraining every time they leave
You're bleeding ā¬25-40k a year plus 6 months of lost momentum.
THERE'S ANOTHER WAY
I'm building ALEK.
Not a person. An agent.
What it does:
All 47 emails get a response in 5 seconds.
24/7. No weekends. No coffee breaks.
Diagnosis works like a 10-year veteran.
Asks the right questions. Sees the context. Requests the details that matter.
Deal closes in 2 exchanges instead of 7.
Results after three months:
ā
Conversion +35%
ā
Sales cycle -60%
ā
CAC cut in half
ā
Zero salaries
And here's the thing?
Every conversation is data. After a month, Alek knows what works. After three months, Alek closes deals better than I do.
WHY NOW
AI agents stopped being sci-fi. They're production tools.
If you're a solo founder ā this is your window.
Before, scaling = hiring. It was expensive and risky.
Now, scaling = configure bot + time.
In 2-3 weeks of setup, you get what used to take 6 months of hiring.
I'm not saying this replaces people. I'm saying this replaces the expensive mistake.
If you're drowning in scaling one person deep, let's talk.
I can help you build your Alek.
#AI #Automation #SoloPreneur #Scaling #ProductLed
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ARIA (Automated Response & Influence Agent)
Project Title: ARIA: Multi-Platform AI Influence Engine & In-Game RAG
Description:
Designed ARIA, an autonomous multi-agent system built to monitor, analyze, and engage with target communities across Discord, Telegram, Reddit, and web forums.
The core architecture runs on a dual-LLM pipeline: Grok drafts highly contextual responses by pulling conversational history from a PostgreSQL database, while Claude acts as an overarching moderation agent to ensure brand safety and quality before publication.
Killer Feature (In-Game RAG): Beyond social media, I engineered a direct in-game integration for an MMORPG environment. Players can ping the AI directly in the game chat. A webhook sends the character's live context (level, zone, class) to the FastAPI backend, where a pgvector-powered RAG pipeline searches the server's knowledge base. Grok formulates the answer, Claude verifies its safety, and the response is instantly delivered back to the player in-game.
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Project Title: SINDIKAT: Algorithmic API Engine
Description:
Built a fully automated quantitative engine running entirely on n8n. This system bypasses basic automation limitations by integrating dynamic mathematical risk filters (EV) and automated PnL feedback loops.
The core achievement of this architecture is the seamless execution of complex JavaScript logic and mathematical modeling without API authorization drop-offs. It operates as a deterministic, high-speed pipeline designed for pure mathematical execution and strict risk management.
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37
Project Title: Quantum Prism: Autonomous Hardware Diagnostics AI
Description:
Designed an autonomous multi-agent AI system that diagnoses complex laptop motherboard faults. The architecture uses Claude Code as the terminal orchestrator, managing sub-agents in self-hosted n8n via the Model Context Protocol (MCP).
Instead of generic AI wrappers, this system relies on a Python FastAPI microservice for computer vision (analyzing boardviews/schematics) and a Qdrant Vector DB for RAG-based hardware theory. It enforces a strict, zero-hallucination diagnostic cycle based on exact physical values (Volts/Ohms/Pins) to navigate from a raw symptom to a precise component verdict.
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