Edgardo Ponce
Architect of Intelligent Systems
Freelance developer specialized in LLMs, LangChain, RAG pipelines, and intelligent automation.
I build software that solves real problems without over-engineering.
I help businesses evolve from paper-based processes to intelligent cloud systems.
My main stack includes:
Areas of Expertise
Defining my capabilities across three clear and technical service pillars.
I design and develop sophisticated AI agents capable of reasoning, planning, and executing complex multi-step tasks. Powered by LangGraph and serverless backend for maximum reliability and efficiency.
From successful prototype to production solution: I build cost-effective and highly scalable serverless infrastructure on Google Cloud, offering complete transparency with LangSmith for monitoring and debugging.
An AI is only as good as its interface: I deliver the AI engine through a robust FastAPI endpoint and can build custom web interfaces (using Vue.js) for testing, validation, or final user interaction.
Functional AI Prototype in Two Weeks
My clear, predictable, and client-focused process.
Step 1: Scope Definition
Together we define a clear goal and measurable objective for the prototype.
Step 2: Active Collaboration
We work together as partners. I maintain constant and transparent communication throughout the development.
Step 3: Solution Delivery
I deliver a real and functional solution, not just a demonstration. A system you can test and validate with real data.
Case Studies
Practical demonstrations of my skills with technical analysis.
The Challenge
Enable users to ask natural language questions about a product catalog in JSON format, obtaining accurate and contextual answers.
The Architectural Solution
I implemented a RAG (Retrieval-Augmented Generation) pipeline. Catalog data is processed and stored as vectors in ChromaDB. User questions are converted into embeddings and used to search for the most relevant information fragments in the vector database. These fragments are injected into a prompt for an LLM (via Ollama), which generates a coherent response based on the data.
Tech Stack
The Challenge
Create an unattended system capable of receiving messages from a server, processing them with local AI logic, and sending responses, all controlled through a simple graphical interface.
The Architectural Solution
I developed a two-component application: a PyQt5 GUI that acts as a control panel, and a background daemon that runs the main loop. The daemon connects to the server, processes incoming messages with a local LLM through Ollama and ChromaDB, and manages response sending. This decoupling ensures that AI logic can run continuously without blocking the user interface.
Tech Stack
AI Architecture Models
My conceptual toolbox for systems thinking.
Recent Posts
My thoughts on AI, development, and systems architecture.
Complete Technical Profile
My experience, tech stack, and continuous training.
AI & Machine Learning
Backend & APIs
Frontend & Design
Infrastructure & DevOps
Databases
Let's Talk About Your Technical Challenge
If you're looking for a collaborator to design and build a complex and well-architected software solution, I'd be interested in learning the details. I'm available to discuss the technical feasibility and strategic approach of your project.