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Building AI systems for real-world impact.
From electoral intelligence and territorial analytics to climate-risk prediction and public-sector dashboards, I use Data Science to transform complex data into strategic decisions.
Currently working on: • GeoAlerta AI
• Electoral predictive models
• Geospatial intelligence systems
• AI-driven public analytics
#AI #DataScience #Python #MachineLearning #GeoAI #Analytics
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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I used Claude for two very different things this weekend:
✦ building an interactive effect for my website
✦ planning the construction of a piece of furniture I want to build on my balcony
And this is exactly why AI feels so exciting to me!
Digital world
Physical world
Same creative brain
Same need to turn an idea into something that works
This is why I find AI genuinely useful. Not as a magic button, but as a translation layer between imagination and execution
Context is everything. You can have the best tech in the world, but without that deep community knowledge, the automation and design won't hit the mark. It’s all about turning those years of context into a seamless journey for the creator. Great take!
Designed, built and deployed a Prospect Finder tool.
AI-Powered Lead Intelligence & Outreach Platform.
Built a fully custom, AI-powered prospecting and outreach platform from the ground up to power an entire sales operation solo, without an engineering team.
The platform functions as a complete sales intelligence command center, pulling prospect data across multiple sources simultaneously; Google Maps, LinkedIn, business websites, Yelp, Facebook, license databases, and directories and scoring each lead with a proprietary AI Fit Score to prioritize outreach by conversion likelihood.
From there, the system manages the full 8-touch outreach campaign lifecycle per prospect, tracking each contact’s position across the sequence in real time. When prospects reply, the platform surfaces AI-generated response suggestions tailored to the conversation context, allowing instant, intelligent follow-up with a single “Approve & Send.”
What I built:
[] Search Command Center with multi-source data scraping (Google Maps, LinkedIn, Yelp, Facebook, Licenses, Directories)
[] AI Fit Scoring engine that ranks prospects by conversion likelihood
[] 8-touch campaign status tracker per contact
[] AI-suggested reply system with Ignore / Edit / Approve & Send workflow
[] Lead management dashboard with overview, replies, leads, campaigns, network, and archives modules
[] Export functionality for pipeline data
Platform tools of dev: Agentic AI development workflows, Claude API, n8n automation, no-code platforms.
End product output: A single operator running a sales intelligence and outreach system that would typically require a dedicated SDR team and a $30k+/year software stack. Built and deployed solo in weeks.
Most AI agents are unnecessarily expensive and the problem is usually architecture, not the model.
One of the biggest pain points when building AI agents is LLM cost. Many teams use high-end models like Claude Sonnet or GPT-5 for everything, even tasks that don’t need that level of reasoning. That quickly becomes unsustainable at scale.
The reality is: not every task needs a premium model. Models like Qwen 3.x can cost up to 10x less and still handle tasks like intent classification, basic responses, structured extraction, and routing decisions with similar efficiency.
A well-designed AI agent isn’t just an LLM wrapper it’s a layered system. Routing decides which model to use, context (RAG/memory) reduces tokens, tools handle logic, and execution orchestrates everything. The LLM is just one component.
The winning strategy is simple: use the right model for the right job.
If your AI agent is expensive, it’s probably an architecture problem.
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