Applied AI, machine learning and scientific modeling for organizations facing complex prediction, automation and decision problems.
Technical depth across several fronts — combined only when a problem actually needs it.
We don't sell technology for its own sake. We solve problems where data, complexity and decision-making meet.
Forecast demand, risk, equipment failure or behavior from historical and real-time data.
Identify anomalies, critical events and regime changes in time series and signals.
Build AI systems and agents for repetitive or data-intensive workflows.
Extract structure and actionable insight from complex, scattered data.
Turn uncertain information into decision support backed by evidence — not intuition.
We combine these fronts as each problem requires — never selling technology the project doesn't need.
Predictive modeling, forecasting, anomaly detection, classification and optimization — built for production, not just notebooks.
RAG systems, enterprise knowledge assistants, AI agents and LLM integration designed around real workflows.
Data pipelines, analytics, dashboards and time-series analysis that turn raw data into a decision layer.
Scientific machine learning, computational modeling and simulation for complex and nonlinear systems.
A gamified investment simulator for learning how the Brazilian stock market really works — real market data, a portfolio to manage and instant feedback, with no real money at risk.
Try the simulatorA sample of what we've already built, with the method behind each solution.
Projects are structured around the problem, the available data, the target metric and the expected outcome — not a fixed methodology template.
We understand the problem, the available data and the project's real constraints.
We quickly build a verifiable solution, not a promise.
We technically measure whether the approach works before scaling it.
We integrate the solution into the real operating environment.
We continuously monitor and improve using real usage data.
Paulo founded Prota AI after a research career in computational physics and data science, with peer-reviewed publications on neural network synchronization and complex systems. That background shapes how the company works: problems are approached with the same rigor as scientific research, not wired together from generative AI APIs alone.
Let's assess, with technical rigor, whether AI can actually solve it — and how.