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Analyzed 853,824+ orders across 8 platforms and 12+ cities. Delivered 4-page Power BI dashboard with drill-down analysis enabling stakeholders to optimize logistics and identify growth opportunities.
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Built 5-page Power BI dashboard integrating 16 tables. Created 50+ DAX measures for revenue tracking, patient demographics, and doctor performance analytics with real-time KPI updates.
This might be the project that took the most time and effort to bring to a final finish line, but the result is truly worth it.
I am thrilled and excited to share with you OmniPulse Analytics Hub, an end-to-end Business Intelligence project I’ve been working on.
The idea wasn't just about putting a few charts together; the real challenge was deep in the backend and setting up a solid data model: linking 18 tables, building a proper Star Schema, and writing custom DAX measures to deliver accurate, error-free KPIs.
In this project, I focused on:
• Professional data cleaning and preprocessing.
• Table relationships and implementing Time-Intelligence using a dedicated DimDate table.
• Developing advanced DAX measures to analyze sales trends, return rates, and inventory levels.
• Designing a clean and intuitive user interface enabling management to read the numbers and make instant decisions.
• Integrating a one-click Executive Summary providing a complete project overview, key metrics, and dashboard status for immediate decision-making.
The dashboard features two main sections:
📌 Executive Overview (Comprehensive view of revenue, total sales, and overall performance rates)
📌 Category & Sales Breakdown (In-depth category analysis and low-stock tracking)
The biggest takeaway from working on this project: A massive amount of disorganized data is worthless. The true art lies in turning a mess of numbers into a clear, actionable story.
I'd love to hear your thoughts and welcome any feedback or suggestions! 👇
#PowerBI #DataAnalytics #BusinessIntelligence #PowerQuery #DAX #DashboardDesign #DataVisualization #DataModeling #Retail #DataScience
AgentFlow10 — AI Agent & Automation Platform
Lead Engineer · Self-hosted AI agent platform with a visual workflow builder
The problem
Automation tools are built for businesses that already have a card on file, reliable bandwidth, and a Slack workspace. That leaves most of the world out. In markets where WhatsApp is the customer channel and mobile money is the payment rail, the standard automation stack either doesn't connect or is priced in currencies that make it a non-starter.
There was a second problem. Every serious automation platform is someone else's cloud. If your data is sensitive, or your compliance rules keep it in-country, or you just don't want to hand your customer records to a vendor, your options run out fast.
What I built
AgentFlow10 is an AI agent and automation platform you can run on your own infrastructure. A visual builder lets you compose agents and multi-step workflows without writing code, connect them to the tools you already use, and deploy them where your data lives.
The design goal was to make AI agents something a small team can actually operate — not a research project requiring an ML engineer on staff.
What I did
I built and run it end to end: product direction, backend architecture, LLM orchestration, integration layer, the workflow engine, frontend, brand system, and go-to-market. Everything from the database schema to the landing page copy.
Notable pieces of engineering:
A workflow execution engine that handles branching, retries, and failure states — because an agent that dies silently mid-run is worse than no agent
LLM orchestration with prompt management and output validation, so results are structured and usable rather than free text
A self-hosting path that a non-specialist can actually deploy
Integration architecture designed for channels that matter outside Silicon Valley, including WhatsApp and mobile money flows
Why it matters for clients
This is the project I point to when someone asks whether I can build AI systems rather than just call an API. Running a platform means I've dealt with the parts that break in production — cost control, rate limits, non-deterministic model output, failure recovery. That experience is what I bring to client work.
Built with: Python, TypeScript, React, Next.js, Node.js, PostgreSQL, LLM APIs, Docker
