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AI-Powered Business Chatbot for Directory PlatformProject Overview
This project involved developing and integrating a custom-trained AI Business Agent into a directory platform. The assistant is designed to engage platform visitors, answer complex business inquiries in real time, and guide users toward tailored solutions—improving user experience and boosting conversion rates.
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Clientwise — Mobile Analytics & Business Dashboard UI/UX
Exciting to share a fresh iteration for Clientwise — a modern mobile analytics platform designed for business owners, founders, and decision-makers who need instant clarity on their metrics on the go. ⚡️
🎯 Key Features & Highlights:
Data Visualization at a Glance: Real-time revenue tracking, order value breakdowns, and growth trends delivered in a high-contrast layout.
Adaptive Design System: Clean component architecture including custom date pickers, crisp micro-icons, and dual-mode theme support (Light & Dark).
User-Centric UX: Streamlined navigation allowing users to filter custom date ranges and monitor client insights seamlessly.
Designed in Figma with a strong focus on typography, modern contrast, and scalable UI components.
💬 How do you approach dual-theme accessibility in data-heavy mobile apps? Would love to hear your thoughts in the comments!
🚀 Available for new product design opportunities & project collaborations.
Really nice work! You can tell a lot of thought went into making a data-heavy interface feel this clean and approachable. 👏
A self-initiated product campaign exploring how AI-generated concepts can be evaluated, art-directed, and refined into cohesive advertising visuals for a premium magnetic power bank.
Love the overall quality of this project. Everything feels very intentional and well executed.
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.