Author
Fabio Xavier
Founder of Chatydata
Fabio Xavier is the founder of Chatydata, with over 25 years in technology, data, product and digital solution architecture for companies. His experience spans consulting, technical leadership, platform development, data strategy, automation and the practical application of AI in enterprise contexts. Throughout his career he has led technology and product initiatives in high-complexity environments, connecting business vision, engineering, data and operations. Chatydata’s thesis grew out of that experience: before the agents comes the context. Without prepared data, governance and traceability, enterprise AI does not scale with confidence.
Published articles
Ensuring LGPD Compliance in AI Solutions
Explore best practices and technological solutions to ensure your AI implementations comply with Brazil's LGPD regulations.
AI for customer service: what changes when the answer has context
Applying AI to support is more than a chatbot on your site. See what separates an automation that frustrates from an agent that resolves, with source, scope and clear rules.
Document chatbot: how to build a reliable base for corporate use
A document chatbot your company actually uses depends on the base: source of truth, scope, permissions, source citation, and measurement. The step-by-step.
Before the agents comes the context
The Chatydata manifesto. The race for AI agents reversed the order of things. Before you automate, you have to prepare the foundation: data, context and governance.
Why AI projects fail before the pilot
Most AI projects don’t fail at the model. They fail earlier: in the data, the context and the governance. Understand the real bottlenecks and how to avoid them.
What "data ready for AI agents" actually means
Having lots of data is not the same as having data ready for AI. Here are the five criteria that make a base trustworthy enough to feed agents.
How to know if your company is ready for AI agents
Five dimensions to objectively measure whether your company is ready to use AI agents safely, and what to do at each level.
How to prepare data for AI agents
Preparing data for AI means organizing sources, defining one source of truth per type of information and making content traceable. Here is the step by step.
How to reduce hallucination in enterprise agents
AI hallucination drops when the agent works with defined sources, clear scope and sourced answers. See the practices that lower the risk in production.
What RAG is and why it matters for companies
RAG is the technique that makes AI fetch information from your base before answering. Understand how it works, why it reduces errors and what changes in enterprise use.
Chatbot with documents is just the start: limits, risks and next steps
Connecting a chatbot to your documents is easy to demo and hard to operate. See the limits, the risks and what it takes to reach a trustworthy agent.
AI agents for companies: how to start without creating risk
Starting with AI agents without risk is a matter of sequence: a clear use case, a prepared base, defined scope and metrics. See the step-by-step path.
Generative AI governance: a checklist for scaling safely
Generative AI governance: the executive checklist (data, access, traceability, privacy and audit) for scaling AI safely instead of by accident.