AI Automations Architect | Infrastructure Engineer

Designing deterministic AI systems, agent-native SaaS foundations, and machine-discoverable repositories.

• AI-native backend systems (FastAPI, Rust, TypeScript)
• Deterministic orchestration engines
• MCP-based agent infrastructure
• AEO-optimized repository architectures
• Production-grade LLM workflows with guardrails

Low-variance task execution engine built for predictable orchestration behavior.

• Deterministic execution model
• Explicit failure states
• Structured logging
• Designed for reliability > abstraction

Status: Core logic production-ready

Modular foundation for AI-native SaaS products.

Includes:

• LLM routing layer
• Structured output enforcement (schema-first design)
• Vector-backed memory
• Guardrail + validation layer
• Reasoning trace logging
• Evaluation harness

Goal: Ship AI products without architectural drift.

Framework for building repositories that are:

• LLM crawlable
• Context-aware
• Schema-readable
• Agent-invokable

Implements:

• llms.txt standards
• MCP-config patterns
• Structured documentation layers
• Machine-readable repo manifests

Python | Rust | TypeScript

FastAPI | Actix | Node.js

PostgreSQL | pgvector | Redis

OpenAI | Anthropic | Local models
LlamaIndex | Structured outputs

Docker | GitHub Actions | AWS

1. Determinism before intelligence
2. Observability before scale
3. Schema before prompt
4. Guardrails before deployment
5. Proof-of-work over positioning

• Agent-to-agent infrastructure
• MCP hardware/API bridge
• AI-native SaaS primitives
• Sovereign AI deployment models

Pinned repositories represent live infrastructure systems:

• Deterministic scheduler engine
• Sovereign AI SaaS boilerplate
• AEO architecture framework
• MCP bridge implementation
• LLM evaluation harness

Open to:

• AI-native SaaS architecture consulting
• Infrastructure partnerships
• Technical moat design
• Early-stage advisory

Philippines (UTC+8)

Building infrastructure for the machine economy.