My name is Alexis Diaz ( Alex ), but my close colleagues call me Wolf! I am the founder of Alpha Automated Intelligence Operations. Backed by a disciplined foundation forged in high-stakes, fast-paced operational environments, I bring composure under pressure, methodical problem-solving, and clear communication to every technical build. I translate complex operational bottlenecks into fault-tolerant, self-healing automated business infrastructure using Make, Zapier, n8n, Python, and UiPath RPA.
Systematic execution under pressure, ensuring fault-tolerant logic and clean recovery branches.
Prompt injection mitigation, schema validation, and strict audit trails applied across all pipelines.
Drawing from real-world high-stakes operational environments, I apply attacker-style thinking to uncover failure modes, prompt regressions, and data leaks before production deployment.
Analyzed prompt injection vectors, jailbreak variations, and unauthorized instruction overrides within autonomous API pipelines to prevent unwanted agent actions.
Evaluated LLM responses under contradictory instructions, missing context, and degraded API states to prevent silent pipeline halts and payload corruption.
Engineered Human-In-The-Loop checkpoints with confidence scoring, routing ambiguous outputs to supervisor review before triggering external actions.
Documented structured JSON schemas, telemetry logging, and decision rationale to guarantee complete operational compliance and auditing.
Real-world systems, intelligent agents, and RPA workflows designed by Alexis Diaz. Each architecture is presented in an explanation-driven narrative detailing the operational problem, custom solution, and verified value delivered.
Manual customer support workflows relied heavily on human triage and static routing, leading to delayed response times, frequent ticket transfers, SLA breaches, and inconsistent escalation handling. The lack of prioritization logic and contextual insight resulted in lower first-contact resolution and increased customer frustration.
Analyzed end-to-end support workflows to identify bottlenecks, redundant steps, and escalation gaps, then redesigned the process using AI-assisted ticket classification, intelligent routing, and predictive escalation logic. Introduced human-in-the-loop checkpoints to maintain quality control while reducing manual effort and improving workflow efficiency.
Reduced manual triage and unnecessary handoffs by streamlining support workflows with AI-driven classification and prioritization. Improved response times, SLA compliance, and first-contact resolution through intelligent routing and escalation prediction. Demonstrated scalable AI automation design that balances operational efficiency with human oversight and quality assurance.
Users seeking reliable information about medical marijuana faced inconsistent, time-consuming, or judgmental sources, creating barriers to understanding its uses, risks, and legal implications.
Developed an AI-driven FAQ chatbot that delivers instant, private, and nonjudgmental responses to user inquiries. Leveraged Zapier automations to handle up to 80% of routine questions, while building a structured “knowledge base” for accurate, compliant responses.
Reduced manual response workload by 80%, improving operational efficiency and response speed. Provided 24/7 access to trusted information, enhancing user trust and engagement. Demonstrated seamless AI workflow integration across systems through successful API connections and automation triggers with 100% functional test success.
Customer feedback is often collected but not acted on quickly, allowing negative experiences to escalate before teams are aware. Manual review of feedback is time-consuming, inconsistent, and limits a company’s ability to respond in real time to customer sentiment.
Designed an automated feedback analysis workflow that captures customer responses, processes them through an AI model for sentiment classification and summarization, and stores structured outputs in Google Sheets. Implemented conditional logic to trigger real-time email alerts when negative sentiment is detected, enabling faster response and intervention.
Automated sentiment analysis and summarization, eliminating manual review of customer feedback. Enabled real-time visibility into negative feedback through automated email alerts. Created a structured, scalable feedback system that supports faster decision-making and continuous operational improvement.
Local small businesses—such as boutique dog grooming services and independent yoga studios—often struggle to establish a visually appealing, conversion-focused online presence that reflects their brand identity and attracts local clients.
Designed two AI-visualized landing pages (for a dog grooming service and a Laguna Beach yoga studio) using Loveable.dev, integrating functional modals such as Gmail and Google Sheets for contact capture, booking, and client management. Developed tailored brand narratives and value propositions that clearly articulated each service’s unique market positioning.
Increased potential client engagement through high-converting CTAs aligned with business goals (“Book Online” and “Join a Class”). Established compelling local relevance by incorporating regional storytelling (e.g., SoCal beach lifestyle, Laguna wellness culture). Delivered a professional, market-ready online presence that supports ongoing customer acquisition.
A local coffee and ice cream kiosk relied on manual tracking of weather forecasts and sales patterns, leading to inefficiencies in staffing, product preparation, and promotional timing. This often caused inventory waste, under- or over-staffing, and missed sales opportunities.
Developed an automated AI system that analyzes daily weather forecasts to recommend optimal product mixes, staffing levels, and promotions. Implemented automated workflows that email daily summaries and severe weather alerts to management, replacing manual forecasting and planning processes.
Automated up to 80% of routine weather-related decision-making, significantly reducing managerial workload. Improved operational efficiency through data-driven inventory and staffing recommendations, while reducing product waste and increasing profitability through smarter daily planning.
Finance teams receive large volumes of invoices from multiple suppliers, requiring manual data entry to track due dates, totals, and purchase details. This process is time-consuming, error-prone, and makes it difficult for managers to quickly identify upcoming payments and high-priority invoices.
Built an RPA workflow using UiPath that automatically reads invoices from Google Drive, extracts structured invoice fields using document understanding, and generates AI-powered summaries. The workflow writes validated results into Google Sheets and flags low-confidence extractions for manual review to ensure accuracy.
Eliminated manual invoice data entry by automating extraction, validation, and reporting. Improved data accuracy by leveraging confidence thresholds and logging low-confidence values for human review. Enabled faster financial visibility by generating structured reports with summarized purchase descriptions.
Workplace incident reports were handled manually, making them time-consuming to review and prone to inconsistent categorization and severity assessment. Critical issues could be delayed or overlooked due to the lack of standardized triage, routing, and escalation. The process also lacked transparency and auditability once data was entered into spreadsheets.
I built an AI-powered automation that analyzes raw workplace reports, classifies issue categories, detects severity, and determines follow-up requirements. The workflow routes incidents to the correct teams and immediately escalates high-severity cases through automated alerts. All outcomes are logged as structured data with quality-control markers to ensure accountability and human oversight.
Automated end-to-end incident triage, reducing manual review time and response delays. Standardized classification and severity detection to improve accuracy and consistency. Enabled real-time escalation and auditable logging to reduce safety and compliance risk across the organization.
A unique blend of high-stakes asset security, client contract execution, and disciplined AI workflow engineering.
Alpha Automated Intelligence Operations (AAIO)
Honest Brothers Painting • New Hampshire (Contract)
Pechanga Resort & Casino • Temecula, CA
Specialized Applied Curriculum & Architectures
Comprehensive stack utilized across Alpha Automated Intelligence Operations.
Test live mission-critical workflows designed by Alexis Diaz. Select a trigger payload to watch data traverse ingestion, AI sentiment/classification, routing logic, and system synchronization.
Intercepts raw event, validates auth header, and parses JSON.
Sentiment extraction, categorization, confidence threshold checks.
Branching paths, emergency alerts, human review routing.
Commits structured data to HubSpot, Sheets, Drive, or Slack.
[AAIO-ENGINE] Standing by. Click any inbound scenario above to simulate real-world execution.
Estimate annual cost savings, recovered working hours, and operational velocity when eliminating repetitive manual tasks through AAIO architectures.
Capital saved from eliminated payroll drag and manual rework.
Generates instant audit report & locks specifications for Alexis Diaz.
Get in direct contact with Alexis Diaz (Wolf). Describe your current operational bottleneck to receive a tailored architectural blueprint.