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Built RealtyFlow AI - a 24/7 AI sales agent for real estate professionals. Monitors Gmail, responds to inquiries in <60 seconds, extracts lead info (budget, timeline, preferences), scores leads as Hot/Warm/Cold, and books appointments automatically via calendar integration. Full-stack: Python/FastAPI backend, React frontend, Groq LLM for AI, PostgreSQL database. Production-ready, handling real leads.
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FlowPilot is a fully interactive SaaS demo showcasing an AI-powered business operations agent and its end-to-end workflow from client request to paid project.
hi looks cool....btw did we receieve credits for the challenge?
My core stack is less "generate pretty pictures" and more "build the pipeline that does the thinking":
n8n — the glue. Great for orchestrating multi-step agent workflows and connecting APIs without writing a service from scratch. Weakness: gets messy fast on complex branching logic, and debugging a broken node chain eats time.
RAG (usually with a vector DB + LangChain-style retrieval) — lets me ground LLM output in actual client data instead of hallucinated generalities. Strength: dramatically improves accuracy for domain-specific asks. Weakness: chunking/retrieval quality is finicky, and a bad embedding setup silently tanks results without throwing an error.
Claude/GPT for reasoning + code — my default for anything needing judgment: analysis writeups, SQL/Python generation, proposal drafting. Strength: fast iteration. Weakness: still needs a human check on numbers — confident-sounding ≠ correct.
Power BI / Tableau — where the AI output actually becomes something a client trusts. AI can shortcut the analysis, but the dashboard is what gets signed off on.
The honest lesson: the "AI" part is maybe 30% of the workflow. The rest is validation, data plumbing, and making the output legible to a non-technical stakeholder.
Silent failures are what I watch closest. I treat an answer as untrusted unless I can show exactly which source text the model read, because fluent output can hide a bad match.
A multi-tenant AI orchestration platform that lets organizations deploy and manage AI agents at scale. It supports persona-based agents with defined roles and behaviors, webhook-driven triggers for automated actions, and single-prompt management of complex multi-step workflows. Built to run reliably on Kubernetes and Docker across multiple organizations.
Whats your thoughts on this?
Very impressive architecture. Multi-tenancy, persona-based agents, and Kubernetes scalability make this a strong foundation for enterprise AI automation. 🚀