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š Building Aloko AI: Business Intelligence and AI-Powered Customer Support
I'm building Aloko AI, an AI platform designed to help businesses analyse their data, make better-informed decisions, and improve customer support.
š 1. Aloko Business Analyst
The goal is to help businesses turn their data into useful insights. The system is designed to work with business datasets and support tasks such as data analysis, identifying trends, and generating reports.
š¬ 2. Aloko Customer Support AI
I'm also developing an AI customer-support solution that uses a business's approved information to answer common questions about products, prices, services, delivery, and company policies.
š ļø Technology Stack
Python ā backend development and AI integration
FastAPI ā building backend APIs
React and Vite ā frontend development
PostgreSQL ā database management
Groq API ā language-model integration
Data analysis and LLM-based workflows ā supporting business insights and AI responses
šÆ The Vision
My goal is to build practical AI tools that help businesses understand their data, reduce repetitive work, and deliver better customer experiences.
I'm continuing to develop and improve Aloko AI, with a focus on making AI useful for real business problems.
I'm interested in connecting with business owners, ecommerce founders, and people working in AI who would like to explore practical applications of AI in business.
š Explore Aloko AI: https://aloko-1.onrender.com/
#ArtificialIntelligence #BusinessIntelligence #CustomerSupport #Python #FastAPI #LLM #AlokoAI
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Building MLOpsLab has taught me something interesting:
Most workflow problems aren't caused by missing automation.
They're caused by missing visibility.
A form gets submitted.
A webhook fires.
An API responds.
Everyone assumes the process worked.
Then days later someone discovers a missing lead, duplicate record, failed sync, or unexplained gap.
Lately I've been spending time building and testing workflow reliability scenarios around:
ā¢ Intake validation
ā¢ Duplicate event handling
ā¢ Failure detection
ā¢ API and database integrations
I'm documenting what works, what fails, and where assumptions break.
If you've ever spent hours tracking down a workflow issue that "should have worked", I'd be interested in hearing what the failure looked like.
#automation #n8n #api #workflow #supabase #buildinpublic
The hardest one to catch is the green run that did nothing. An API returns 200 with an empty list, every later step runs on zero items, and nothing errors. A cheap guard: count items in and items written at the end of the run, and alert when they differ. Are you testing that case in MLOpsLab, or mostly hard failures?
My Weekend Projectš - Designing a seamless FinTech app UI!
Focused on making QR transfers and digital wallet navigation feel effortless.
What are you building or designing this weekend?
This weekend, Iām working on making financial records cleaner and helping businesses understand their numbers better!š
Computer vision project developed for NASTP (National Aerospace Science and Technology Park) as an industry-oriented project. I built a Python and OpenCV pipeline that uses a YOLO deep learning model to detect and classify vehicles on the road, [apna maqsad likhein, jaise āfor traffic monitoring and analysisā]. The demo shown here runs on a public dashcam frame, where it detected both cars with 98% and 96% confidence.
which yolo version did you end up using?