AI ENGINEER · APPLIED AI · AI SAFETY
_{Agentic AI · RAG · MCP · Security · Document Intelligence · Data Engineering}

LinkedIn · GitHub · AYORAI · Applied Intelligence

I build AI systems where reasoning, authority and execution are explicit engineering boundaries.

Intelligence ≠ Authorization ≠ Execution

My portfolio focuses on Applied AI, AI Engineering and AI Safety, with emphasis on:

Architecture · Security · Evaluation · Reproducibility · Observability · Governance

The work spans agentic systems, RAG, MCP, document intelligence, data pipelines and deterministic controls for AI-assisted execution.

Repository · Evaluation

For partners and investors

AI can produce an answer. ATTRACTOR asks: where is the evidence?

ATTRACTOR is an evidence-verification layer for AI systems that transforms claims into traceable, reproducible and auditable verification results.

                    AI / Human Claim
                           │
                           ▼
                    ┌─────────────┐
                    │   Evidence  │
                    └──────┬──────┘
                           │
                           ▼
                    ┌─────────────┐
                    │    Stance   │
                    └──────┬──────┘
                           │
                           ▼
                    ┌─────────────┐
                    │ Provenance  │
                    └──────┬──────┘
                           │
                           ▼
                 ┌───────────────────┐
                 │ Evidence Clusters │
                 └─────────┬─────────┘
                           │
                           ▼
                 ┌───────────────────┐
                 │ Deterministic     │
                 │      Judge        │
                 └─────────┬─────────┘
                           │
                           ▼
                    ┌─────────────┐
                    │   Verdict   │
                    └──────┬──────┘
                           │
                           ▼
                    Auditable Record

Core principle: LLM ≠ Judge. The model may help discover or interpret evidence, but final verification is determined by explicit, auditable rules.

ATTRACTOR is designed for scenarios where traceability, integrity, security and auditability matter, including financial and regulated environments.

It can support governance and validation processes involving evidence, provenance and reproducibility. It does not claim regulatory compliance by itself.

Relevant Brazilian reference: the LGPD (Law No. 13,709/2018). The architecture is also designed to support controls informed by rules issued by financial-system and capital-market regulators, without asserting regulatory compliance by itself.

• Golden v0: 34 cases · 52 documents · SHA-256 manifest (development/evaluation set)
• F1 evaluation on Golden v0.1 (30 claims, frozen majority baseline 43.33%):
• Path B (commercial candidate): 66.67%, exact binomial p = 0.0085 vs. baseline · Path A (research-only, EVAL_ONLY license): 76.67% · Path C (rules): 33.33%
• Development sets use English documents; Portuguese performance will be measured on the hidden Golden v1
• Deterministic Judge: 6/6 public smoke fixtures passing
• CI: Python 3.11 · 3.12 · 3.13 with Ruff, mypy, pytest, CodeQL and workflow-lint
• Release: v0.3.0 · DOI: 10.5281/zenodo.23125083

Evaluation classification: A = EVAL_ONLY · B = Commercial Candidate

Do not trust the AI response alone. Verify the evidence.

Repository · Live platform

A public-facing intelligence platform combining automated technology ingestion, structured data, AI tooling intelligence and an AI Safety laboratory.

Role in the portfolio: public intelligence and research layer.

Repository

A security boundary where model intent does not automatically become executable authority.

Agent
  ↓
Identity / Delegation
  ↓
MCP Tool Integrity
  ↓
Runtime Containment
  ↓
Data-flow Guard
  ↓
Policy + Authorization
  ↓
Transaction / Egress Governance
  ↓
Tool Execution
  ↓
Provenance / Audit

Focus: least privilege · fail-closed controls · MCP security · threat modeling · adversarial evaluation · auditability.

The three featured projects form a compact applied-AI portfolio:

             AYORAI · APPLIED INTELLIGENCE
                         │
       ┌─────────────────┼─────────────────┐
       │                 │                 │
 Global Tech News   AYORAI ATTRACTOR   AYORAI AI Shield
 Intelligence       Evidence-first     Runtime defense
       │             verification            │
       └─────────────────┼─────────────────┘
                         ↓
              Security + Governance
                         ↓
             Authorization / Policy
                         ↓
                     Execution
                         ↓
              Audit / Observability

The recurring architectural boundary is:

Reasoning
   ≠
Authorization
   ≠
Execution

AI Engineering
Python · LLMs · Agents · RAG · Generative AI · NLP · Vision AI

AI Safety & Security
Policy Engines · Tool Authorization · Least Privilege · MCP Security · Threat Modeling · Adversarial Evaluation · Auditability

Data & Document Intelligence
SQL · pandas · NumPy · ETL/ELT · Data Quality · Power BI · OCR · OpenCV · Tesseract · PDF Processing

Backend & Infrastructure
FastAPI · REST · APIs · JSON · Docker · AWS · Local LLMs · Private AI

Engineering
Git · GitHub Actions · pytest · Ruff · mypy · Bandit · Dependency Auditing · CI/CD · Observability

AYORAI · Applied Intelligence
https://ayorinha.github.io/global-tech-news-ai/

GitHub
https://github.com/Ayorinha

LinkedIn
https://www.linkedin.com/in/anderson-leon-ayora

MBA — Data Science, Analytics & Artificial Intelligence — USP/Esalq
2026–2027

Technology in Database — FIAP
Completed · 2022

This portfolio and its original source code are released under the MIT License.

View MIT License

Copyright © 2026 Anderson Leon Ayora

AYORAI · Applied Intelligence
_{Applied AI · AI Engineering · AI Safety}