AI ENGINEERING · AUTOMATION · FULL-STACK

I design systems that turn complex work into clear, dependable software.

I build AI applications, automated workflows, data products, and full-stack platforms for teams that need less manual work and better operational control.

• AI & Automation
• Full-Stack Apps
• Data & ML Models
• Open Engineering
COGNITIVE_SYSTEMS_ATLAS // TOPOLOGY_V2
INTERACTIVE SYSTEM PIPELINE
LAYER_INSPECTOR04_REA

Model Reasoning

LLM inference with system prompt constraints.

TECHNICAL IMPL:

Evaluates input intent and formulates execution plan bounded by system instructions.

DETERMINISTIC: TRUEGUARDRAIL ACTIVE
02 // ARCHITECTURAL LOGBOOK

Inside the Engineering Decisions

Concrete architectural notes and trade-offs derived from building AI and automation software systems.

01_SCHEMAGUARDSYSTEM_MEMO

Structured Input Validation Preceding Model Calls

Raw user payloads are parsed through structured Pydantic schemas before reaching LLMs, reducing malformed inputs and narrowing the exposed attack surface.

ENGINEERING RULE: Sanitizing inputs at the edge is far more effective than attempting to handle corrupted state inside agent loops.
02_HUMANGATESYSTEM_MEMO

Human-in-the-Loop Threshold Gates

When confidence scores drop below threshold or high-risk rules trigger, execution pauses for active human verification.

ENGINEERING RULE: Automation bias is mitigated by requiring explicit decision confirmation on uncertain edge cases.
03_RETRYQUEUESYSTEM_MEMO

Idempotency Controls in Webhook Pipelines

Webhooks use Redis queue state tokens to reduce duplicate database entries during upstream server retries.

ENGINEERING RULE: Distributed workflows must expect webhooks to retry—idempotency tokens help maintain data integrity.
04_HYBRIDRAGSYSTEM_MEMO

Hybrid Retrieval Reranking for Technical RAG

Combining dense vector cosine search with BM25 keyword scoring and cross-encoder reranking is designed to improve retrieval precision on technical jargon.

ENGINEERING RULE: Vector similarity alone can struggle on exact part numbers or rare domain terminology.
DATA_PROVENANCE_TIMELINE // AUDIT_TRACETRACE_ID: #TR-992041
14:22:01.002Source Ingestion
Received webhook payloadOK
14:22:01.045Schema Validation
Pydantic structured field checkOK
14:22:01.210LangGraph Reasoning
Evaluated criteria rulesOK
14:22:01.350Human Audit Check
Confidence threshold evaluationPASSED
14:22:01.480Database Commit
Created audit record in PostgreSQLCOMMITTED
*Illustrative system trace — demonstration data, not a client result.
03 // TECHNICAL CAPABILITIES

Engineering Services

SERVICE 01

AI Agents & RAG Systems

Tool-using autonomous agents, multi-agent LangGraph graphs, and grounded RAG knowledge assistants bounded by strict schema guardrails.

STACK: Python, LangGraph, Qdrant/pgvector, FastAPI, Pydantic, OpenAI / Anthropic APIs.
Inspect Deliverables →
SERVICE 02

Business Automation & Integrations

n8n workflow pipelines, vision document OCR extraction, and resilient webhook integrations connecting fragmented enterprise systems.

STACK: Python, n8n, Playwright, REST/GraphQL APIs, Docker, Redis.
Inspect Deliverables →
SERVICE 03

Full-Stack AI Products

Production-ready SaaS MVPs, client portals, and administrative analytics dashboards built with Next.js App Router and TypeScript.

STACK: Next.js 16, TypeScript, React 19, Prisma, PostgreSQL, Tailwind CSS.
Inspect Deliverables →
SERVICE 04

Data Science & Machine Learning

Exploratory data analysis, predictive classification models, feature extraction pipelines, and interactive data visualization dashboards.

STACK: Python, pandas, scikit-learn, PyTorch, FastAPI, Recharts.
Inspect Deliverables →
SERVICE 05

AI Evaluation, Safety & Observability

Systematic testing harnesses for measuring groundedness, hallucination rates, tool success, API cost, and latency across AI workflows.

STACK: Python, Ragas, DeepEval, LangSmith, PostgreSQL, Custom Metrics.
Inspect Deliverables →
SERVICE 06

Research-to-Prototype Engineering

Converting academic AI papers and experimental architectures into clean, working technical proof-of-concepts.

STACK: Python, PyTorch, Next.js, FastAPI, Docker, Jupyter.
Inspect Deliverables →
04 // PAPER-TO-PROTOTYPE EXPERIMENTS

Research Notes

Explore Research Constellation
RESEARCH_CONSTELLATION // PAPER_MAPINTERACTIVE PAPER-TO-PROTOTYPE INDEX
INSTITUTION:
TOPIC:
Google Research // 2025Technical Note

Towards a Science of Scaling Agent Systems

Multi-agent performance depends on task parallelism, sequential dependencies, tool density, and coordination topology rather than simple scale.

TOPIC: AgentsRead Study Note →
MIT // 2025Conceptual Application

Authenticated Delegation and Authorized AI Agents

Establishes frameworks for authenticated, authorized, and auditable delegation through scoped credentials, OAuth 2.0, and OpenID Connect.

TOPIC: SafetyRead Study Note →
NBER / Stanford // 2023Paper Review

Generative AI at Work

NBER Working Paper 31161 field study showing generative AI tools provide the highest relative productivity boost to novice and mid-tier workers by codifying tacit knowledge.

TOPIC: AutomationRead Study Note →
Stanford // 2025Technical Note

The Oversight Game: Learning to Cooperatively Balance an AI Agent’s Safety and Autonomy

Stanford GSB Working Paper 4309 (arXiv:2510.26752) models the play/ask/trust/oversee framework to balance agent autonomy against human oversight risks.

TOPIC: SafetyRead Study Note →
READY TO DISCUSS YOUR SYSTEM REQUIREMENTS?

Let's discuss your workflow requirements.

I am available for AI agent development, workflow automation, and full-stack software engagements. Direct communication, public code repositories, and clear outcomes.