Diego Rodriguez.

My services .

I specialize in bridging AI/ML capabilities with real-world products. From LLM-powered SaaS platforms to ML-driven risk detection systems, I deliver production-grade AI solutions that drive business growth.

AI-Powered SaaS Platforms

Full-stack SaaS platforms with LLM integration, dynamic content generation, intelligent recommendation engines, and automated business workflows powered by AI.

AI Agent Systems

Autonomous AI agents with tool/function calling, structured outputs, and machine-to-machine protocols for payment processing, automation, and business intelligence.

LLM Applications & RAG

Production RAG pipelines with LangChain/LangGraph, vector databases, chatbots, document processing, and domain-specific retrieval-augmented generation systems.

ML Risk Detection & Fraud Prevention

Real-time risk scoring engines, anomaly detection pipelines, behavioral analysis, and automated fraud prevention systems using scikit-learn and feature engineering.

Web Applications

Modern full-stack web applications using React, Next.js, TypeScript, Node.js, and Python backends with real-time features, data visualization, and scalable architecture.

Algorithmic Trading Systems

Automated trading platforms with reinforcement learning (PPO, SAC), real-time data pipelines, technical indicators, and portfolio management dashboards.

Frequently Asked Questions About These Services

What is an AI-Powered SaaS Platform and who needs one?+
An AI-Powered SaaS Platform combines full-stack product engineering with deep LLM integration to deliver intelligent, scalable software-as-a-service products. Businesses that benefit most include startups building AI-native tools, agencies automating client workflows, and enterprises adding AI capabilities to existing products. Built with Next.js or React frontends, Python (FastAPI) backends, LangChain or LangGraph for LLM orchestration, PostgreSQL with pgvector for data and embeddings, and Stripe for subscription billing. Typical features include dynamic AI-generated content, intelligent recommendation engines, automated proposal or report generation, and multi-tenant admin panels.
How do AI Agent Systems differ from regular chatbots?+
AI Agent Systems are autonomous software entities that perceive input, reason using an LLM, and take actions — unlike basic chatbots that only respond to prompts. Use cases include autonomous payment processing (using the x402 protocol), customer support automation, multi-step business process automation, and machine-to-machine communication. Built with LangGraph for stateful multi-agent orchestration, Model Context Protocol (MCP) for tool integration, structured JSON outputs with Pydantic validation, and function/tool calling interfaces compatible with OpenAI, Anthropic, and Gemini. Production-proven with a deployed AI-native payment gateway using these exact patterns.
What is RAG and when should I use LLM Applications with retrieval?+
Retrieval-Augmented Generation (RAG) grounds LLM responses in your own data — enabling accurate, hallucination-resistant answers over proprietary documents, databases, or knowledge bases. This is the right service for legal document Q&A, internal knowledge search, customer support bots trained on product documentation, and domain-specific research assistants. Production RAG systems are built using LangChain and LangGraph for pipeline orchestration, pgvector or Pinecone for vector storage, OpenAI or Anthropic for generation, and FastAPI for serving — deployed for SaaS products with high accuracy requirements.
How does ML Risk Detection achieve 94% precision in production?+
ML Risk Detection systems analyze behavioral patterns and transaction signals in real time to classify events as fraudulent, suspicious, or legitimate — without manual review. The production system for a high-traffic gaming platform achieved 94% precision by combining scikit-learn classifiers, behavioral feature engineering (velocity, device fingerprinting, session patterns), anomaly detection pipelines, and a FastAPI real-time scoring endpoint. Businesses that need this include gaming platforms, fintech products, e-commerce sites, and any system where fraud or abuse poses financial exposure. Stack: Python, scikit-learn, Pandas, FastAPI, PostgreSQL, and Redis.
What does a full-stack web application project include?+
Full-stack web applications deliver the complete product experience — from pixel-perfect frontends to secure, scalable backends. Built using React and Next.js (Pages or App Router), TypeScript throughout for type safety, Python (FastAPI) or Node.js (NestJS) for APIs, PostgreSQL or MongoDB for persistence, and Redis for caching and real-time features. Delivered projects include real-time dashboards with WebSocket data feeds, multi-tenant SaaS platforms with role-based access, data visualization tools for fintech clients, and complex form engines with third-party integrations. Deployable to AWS, Vercel, or your own infrastructure — includes testing, documentation, and handoff.
What is an Algorithmic Trading System and how does reinforcement learning apply?+
Algorithmic Trading Systems automate buy/sell decisions using quantitative strategies executed at machine speed — eliminating emotional bias and enabling 24/7 market participation. RL-based trading systems use PPO (Proximal Policy Optimization) and SAC (Soft Actor-Critic) algorithms from the Stable Baselines3 library, trained in custom PyTorch environments with historical market data. Features include multi-symbol portfolio management, technical indicator pipelines (RSI, MACD, Bollinger Bands), real-time data ingestion via broker APIs, backtesting frameworks, and performance dashboards. Suitable for retail traders, quantitative hedge funds, and fintech companies building trading products.