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My own AI assistant for company documents and SQL data. Users ask in plain language and get answers from their files (RAG with LangChain and FAISS) or from their databases (questions turned into SQL queries). Django web app, Python/Flask microservices, OpenAI, Docker.
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34 tested API endpoints that AI agents can discover and pay for per request in USDC, with no accounts or API keys. It includes a paid MCP server, marketplace listings generated from real outputs, SSRF-safe fetching, and 19 Apify Actors that offer bulk versions of the endpoints.
agents paying per request in USDC with no API keys is lowkey the future
AI Property Maintenance Automation
AI-powered property maintenance automation designed to streamline how property management teams handle tenant maintenance requests.
The system takes a maintenance request, analyzes the issue, determines its priority and category, recommends a suitable vendor, and automatically creates a structured work order.
Workflow:
Tenant request → AI analysis → Priority & category → Vendor matching → Work order
Built with: Python, Flask, SQLite, HTML, CSS, JavaScript, and AI-assisted request classification.
This project was built as a portfolio demonstration of AI automation for property management operations.
Turning a tenant request into a ready work order in seconds removes the manual triage step.
"We can't afford a data analyst."
I've heard this from 3 different startups this year.
So I built one.
Metriva is an AI-powered business analyst that:
📊 Turns messy spreadsheets into executive dashboards
🧠 Writes C-level business briefs automatically
📈 Forecasts trends + detects anomalies
💬 Answers questions about your data in plain English
🎯 Finds your Pareto 80/20 — who drives 80% of your revenue?
No SQL. No Python. No data team.
Just upload your file and get answers.
Built with Flask, vanilla JS, and a lot of late nights.
🔗 Try it: metriva.ai1.vercel.app
#AI #BusinessIntelligence #Founder #IndieHacker #DataScience
The brief-writing piece is interesting — curious how you handle the validation step. AI-generated language tends to sound confident even when the underlying data is sparse or the trend is ambiguous. One pattern that helps: have the model output a confidence signal alongside the...