Matthew Reniva
AI & Backend Engineer building scalable automation
New to Contra
Matthew is ready for their next project!
🚀 Build AI Automation Agents for Your Business (What I’m Actually Doing Right Now)
Most businesses don’t need “more AI”…
They need less manual work.
Lately, I’ve been building AI automation agents that:
→ Read and respond to emails automatically
→ Extract data from documents (invoices, reports, forms)
→ Connect APIs between tools (CRM, databases, dashboards)
→ Turn messy workflows into clean, automated systems
And the goal is simple:
Save hours of manual work every single week.
đź’ˇ Example:
One system I built reduced manual document processing by ~70% using AI + backend automation.
What I’ve noticed in 2026:
Businesses are overwhelmed with tools…
But what they really need is integration + automation that actually works.
That’s where I focus.
If you’re:
âś” Running repetitive operations
âś” Manually handling data or emails
✔ Using tools that don’t talk to each other
You’re leaving time (and money) on the table.
I’m currently looking to:
🔧 Build real-world AI automation systems
đź“Š Solve messy backend/data problems
🤝 Work with founders & teams who want efficiency
If this sounds like what you need,
Let’s connect or message me.
Or just follow — I’ll be sharing real builds (not theory).
#AI #Automation #Backend #Startups #SaaS #Productivity
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Title: Automated Defective Product Detection System on a Production Line
Developed a deep learning model for real-time image analysis to identify and flag defective products on a production line. Automated quality control processes to minimize manual inspection.
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Personalized Recipe Recommendation App Base on Dietary Restrictions and Preferences
Designed an application providing tailored recipe suggestions based on userdefined allergies, dietary restrictions (vegetarian, vegam, gluten-free), and cuisine preferences.
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Our project focused on making a computer program better at telling the difference between real photos and fake ones made by AI. We took an existing program and changed it to make it much more accurate. Now, it can correctly identify fake images about 95% of the time, which is a big improvement from its original 70% accuracy.
With AI now able to create very convincing fake images quickly (Khadatkar, 2024), our work is important for spotting these fakes. 1 Our research paper explains how we made these improvements using a special kind of computer learning. This work can help address the challenges that come with the rise of AI-generated images.
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Built a web-based betting dashboard with separate admin and player portals. The admin can create games, generate unique player links, track bets, monitor the total pot, update live scores, and settle results. Players can access their own dashboard, place bets, view live match status, and check their final payout. I also designed a modern dark UI and implemented rules such as a minimum bet amount and automatic fee deduction for winnings.
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