Nestor Gomez Artiles

Co-Founder

Rexburg, Idaho, United States2 mos experience

AI EnabledAI ML Practitioner

Key Highlights

Founder of a platform for EU AI Act compliance.
Developed serverless solutions for procurement tracking at Amazon.
Implemented advanced forecasting models for supply chain optimization.

Stackforce AI infers this person is a SaaS-focused AI governance and supply chain optimization expert.

Contact

Skills

Core Skills

Ai GovernanceEu Ai Act ComplianceAwsSupply Chain ManagementData AnalysisForecastingQuantitative ResearchFinancial Analysis

Other Skills

risk classificationdocumentationaudit-ready outputsAPI GatewayLambda (Python)DynamoDBCognitoS3/CloudFrontXGBoostsupply demand forecastingdata-driven decision makingLSTMMonte Carlo simulationfinancial time series analysisNetwork and System Security

About

Building Complaix, a platform that helps teams comply with the EU AI Act by turning AI governance into a repeatable workflow: inventory → classification → documentation → audit-ready outputs

Experience

2 mos

Total Experience

2 mos

Average Tenure

2 mos

Current Experience

Complaix eu

Founder

Mar 2026 – Present · 2 mos

Building an AI governance platform for EU AI Act compliance (system inventory, risk classification, documentation).
Generating audit-ready outputs (e.g., system descriptions, risk controls, logs, evidence pack).

AI governanceEU AI Act compliancerisk classificationdocumentationaudit-ready outputs

Amazon

Intern

Jan 2026 – Apr 2026 · 3 mos · On-site

Built an end-to-end serverless procurement & inventory tracking platform on AWS (API Gateway, Lambda (Python), DynamoDB, Cognito, S3/CloudFront) to replace manual supply tracking.
Developed an XGBoost forecasting model to predict supply demand and improve planning accuracy; integrated outputs into the workflow for data-driven replenishment decisions.

AWSAPI GatewayLambda (Python)DynamoDBCognitoS3/CloudFront+3

Lawrence technological university

Research Assistant

Mar 2025 – Sep 2025 · 6 mos · On-site

Implemented LSTM-based sequential models
Conducting quantitative research using Monte Carlo simulation to analyze extreme risk scenarios in financial time series, focusing on tail behavior, stress testing, and uncertainty quantification.

LSTMMonte Carlo simulationquantitative researchfinancial time series analysisfinancial analysis