Core Philosophy
Human Amplification
over Full Automation
AI should strengthen human expertise, not replace it. In high-stakes environments, bad decisions or direct attacks can lead to explosions, environmental damage, or costly failures. That is why human-in-the-loop architecture is essential to the operation and safety of agentic systems.
I support an AI-native mindset, but not an "automate everything" approach. Teams still need clear decisions, shared context, and human judgement. Maintaining a human-centered design approach is key to make advanced agentic systems trustworthy, reliable and more useful.
- Daniel Arevalo, October 2025
Design Process
For Enterprise SaaS and Cybersecurity Solutions
My skills enable teams to execute fast, correct direction, align quickly, and iterate with high velocity towards practical solutions.
Strategy
User Research
Analyzing data from user interviews and interaction, I turn research into actionable insights and align stakeholders around the right problems, opportunities, and direction.
Discovery
Quick Prototyping
From research and rough sketches to pixels and motion, I collaborate with developers to take concepts, user flows, or entire products from ideas to MVPs ready for user testing.
Execution
Iterative Development
Taking into account a wide range of end-user’s feedback, I refine concepts all the way through launch, with special attention to cognitive load in mission-critical systems.
Speaking & Advocacy
Signature Talk: "The Human Factor in AI-Powered IT/OT CyberSecurity
- Event Tekna AI & Cybersecurity Operationalizing AI for Engineers
- Event RunwayFBU Founder Events The Designer's Role in High-Stakes Tech
- Event ProductTank Riga Amplification vs Automation Architecture
Technical Stack
Practitioner + Thought Leader
My workflow combines strategic research with production-ready prototyping directly in code. I use AI coding agents and generative AI to ship faster while keeping a human-centered approach to decision-making.
Research synthesis, literature review, and fast knowledge extraction for complex topics.
Agent and MCP-oriented workflows for structured experiments, tool integration, and reproducible research loops.
IDE-native coding assistance, completions, and refactors grounded in the codebase and workflows.
Long-context reasoning, scenario exploration, and structured synthesis for strategy and stakeholder alignment.
Source-grounded notebooks for themes, narratives, and evidence-backed JTBD and opportunity framing.
Code generation, architecture support, and technical documentation.
Cloud workspaces and instant deploys for quick experiments, MVPs, and shareable demos.
AI-native app building for fast iteration on flows, UI, and interactive product experiments.
Prompt-to-full-stack builds for polished UI prototypes and rapid user validation.
Agent-first editor for repo-wide context, inline AI, and fast iteration on production code.
Google’s IDE for agentic workflows—multi-file edits, grounded context, and reviewable changes.
Design context, components, and handoff in the agent loop via Model Context Protocol.
Issues, projects, and roadmaps available to agents for planning and delivery workflows.
Local Postgres, migrations, and project scaffolding from the terminal for fast backend iteration.
Webhook forwarding, test charges, and billing flows in local and staging environments.
All these tools are the key to speeding up learning loops between product decisions and business impact—especially when research synthesis, assumption testing, and rapid prototyping stay in the same tight loop.