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Mahesh Fabricators — Business Intelligence Dashboard
A comprehensive Power BI solution designed to bring financial and operational performance into a single, decision-ready view.
The dashboard transforms business data into clear, interactive insights across sales, production, expenses, and receivables/payables—giving management a more efficient way to monitor performance, identify trends, and make informed decisions.
Built with a focus on clarity, usability, and actionable business insights.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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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.
Great work!
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.
Built an interactive Power BI dashboard to analyze sales, customer trends, and regional metrics using DAX and data modelling. Enabled faster decision-making for inventory, marketing, and revenue optimization with dynamic filters and KPIs.
