AJArtJeck Technology

artjeck/system  ·  v0.4.2

Production AI systems — with the QA discipline most teams skip.

// AI agents · RAG · web · iOS — designed with evals, edge cases, and regression checks from day one. Ships behaving the way you expect when real users start poking at it.

system onlineaccepting inquiries · reply within a few hours
./start-projectcat ./engagements.md
artjeck/system · status
San Francisco, CA · --:--:-- PT
›system check ── v0.4.2·build 2a4f1c7·uptime 99.97%·based in San Francisco, CA
agents·orchestrated · 3 active
evals·passing · 47/47
ci·green · main @ 2a4f1c7
release·ready · gated
recent events
—08mdeploy: web@0.4.2 → prod · ok
—14mevals: hallucination suite · 47 pass
—31magent: knowledge_assistant · 0 retries
—1htest: regression run · 0 new failures
›0 incidents · 0 regressions · waiting for input_
## services§ 01

Systems that move from idea to launch.

// From AI automation through polished products and release confidence — built around real business use, not throwaway demos.

@service/ai-automation01

AI Automation Engineering

Agents, RAG systems, prompt engineering, evals, fine-tuning, and workflow automation built around real business processes.

scope
· agents · RAG · evals · workflows
output
· production AI systems with citations
quality
· evals + edge-case prompts gated
@service/product-development02

Web & iOS Product Development

Modern websites, web apps, dashboards, APIs, MVPs, and SwiftUI iOS apps — designed for real users, not the demo.

scope
· web · api · dashboards · iOS
output
· shipped product · clear architecture
quality
· responsive · accessible · maintainable
@service/quality-engineering03

Software Quality Engineering

SDLC/STLC-based testing, regression coverage, edge-case validation, and the QA layer that gives you release confidence.

scope
· test design · regression · edge cases
output
· release-confident product
quality
· coverage that catches what users would
## about§ 02

AI engineering, product thinking, and quality discipline in one build path.

Most AI projects fail in the gap between prototype and production — great in the demo, brittle in real use. I close that gap by treating AI engineering, full-stack development, and software quality as one build path instead of three separate disciplines.

Agents, RAG pipelines, and web/iOS products designed with evaluations, edge-case coverage, and regression checks from day one — so what ships behaves the way you expect when real users start poking at it.

// no throwaway demos · no happy-path-only releases · quality gates wired into the lifecycle

AI workflows shaped around business outcomes

Full-stack products with maintainability in mind

iOS experiences designed for focused mobile use

Testing strategies that improve release confidence

## process§ 03

discovery → architecture → build → test → deploy → improve

// A clear workflow keeps product decisions, technical execution, and software quality moving together.

  1. 01
    stage / 01scoped

    Discovery

    Clarify the business goal, users, constraints, and what success needs to look like.

  2. 02
    stage / 02approved

    Architecture

    Design the product flow, data model, AI approach, integrations, and testing strategy.

  3. 03
    stage / 03shipping

    Build

    Develop the automation, web platform, API, or iOS product with maintainable code.

  4. 04
    stage / 04green

    Test

    Validate functionality, edge cases, regressions, AI outputs, and release readiness.

  5. 05
    stage / 05live

    Deploy

    Ship to production with clear configuration, monitoring paths, and handoff notes.

  6. 06
    stage / 06running

    Improve

    Use feedback, evaluations, and product data to refine the system after launch.

## engagements§ 04

Real repositories, shipped as portfolio projects.

// A curated slice of my GitHub work: production websites, AI agents, QA learning products, and the live ArtJeck Technology platform.

ArtJack/email-agent

Self-Hosted AI Email Agent

Daily inbox review was noisy and manual, with important messages mixed into low-priority mail.

approach
· A local agent pulls Yahoo mail over IMAP, asks Claude to triage and summarize, then sends a Telegram digest.
stack
· TypeScript, Node 20, imapflow, mailparser, Anthropic SDK, SQLite, Telegram Bot API, launchd
quality
· Includes test-connection and dry-run paths, SQLite dedupe for idempotency, and cost reporting per run.
result
· A self-hosted morning digest that runs on schedule and keeps AI spend to pennies per run.
TypeScriptruntime·launchdcost·pennies/runpipeline·imap → claude → telegram
Source
ArtJack/dm-express-site

DM Express Trucking Website

A small trucking company needed a credible, fast, phone-friendly site to help recruit drivers.

approach
· A one-page React/Vite site with light/dark theme, animated sections, and a structured driver application flow.
stack
· React, TypeScript, Vite, Vitest, plain CSS, mailto application flow, Vercel
quality
· 29 tests using equivalence partitioning, boundary value analysis, decision tables, and state transition testing.
result
· A sanitized real-client portfolio build tuned for iPhone behavior, accessibility, and sub-500KB first load.
TypeScripttests·29 passingpayload·<500KBmobile·iPhone tuned
SourceLive
ArtJack/ai-agent-bootcamp

AI Agent Bootcamp

Learning agentic systems needs more than API calls; it needs progressive examples that show how agents reason and use tools.

approach
· A staged Claude API bootcamp that moves from a basic message call to a ReAct agent with tools and structured logging.
stack
· Python 3.12, Anthropic Python SDK, dotenv, JSON Schema tool definitions, ReAct loop
quality
· Exercises graceful error handling, unknown-input paths, tool stop reasons, and logged multi-turn tool calls.
result
· A clear learning repo that demonstrates tool use, prompt control, parallel calls, and production-minded agent structure.
Pythonpattern·ReActtools·3 demo toolsnext·RAG + MCP
Source
ArtJack/istqb-study-kit

ISTQB Study Kit

ISTQB prep often turns into disconnected PDFs instead of an active learning path for new QA engineers.

approach
· An open CTFL v4 study product with a study guide, content map, chapter notes, glossary, quizzes, and roadmap.
stack
· Markdown, CTFL v4 content architecture, study guides, glossary, chapter quizzes, product roadmap
quality
· Content is organized around syllabus coverage, practice questions, answer explanations, and readiness checkpoints.
result
· A QA-focused knowledge product that shows documentation architecture, testing fundamentals, and learner-centered planning.
Docssyllabus·CTFL v4format·study productroadmap·quiz app
Source
ArtJack/artjeck-technology

ArtJeck Technology Portfolio

The brand needed a live professional site that clearly positions AI automation, product engineering, and QA discipline.

approach
· A production Next.js portfolio with terminal-style visuals, animated system UI, project cards, contact form, and SEO metadata.
stack
· Next.js, React, TypeScript, Tailwind CSS v4, Resend-ready contact action, Cloudflare email routing
quality
· Verified with lint, production build, browser checks, contact email routing, and an audit-clean dependency override.
result
· A live personal brand site at artjeck.com with direct email contact and a deployable project showcase.
TypeScriptstatus·liveemail·hello@artjeck.comsource·private
Liveprivate source
## skills§ 05

One build path: AI, product, and quality.

// The skill set is intentionally cross-functional so implementation, testing, and launch readiness stay connected.

› @artjeck/ai01
// ai engineering
import {
  • agents,
  • rag,
  • prompt_engineering,
  • evals,
  • fine_tuning,
  • vector_databases,
  • llm_apis,
} from "ai-engineering"
› @artjeck/product02
// development
import {
  • web_development,
  • apis,
  • frontend,
  • backend,
  • ios,
  • swift,
  • swiftui,
  • deployment,
} from "development"
› @artjeck/quality03
// quality assurance
import {
  • sdlc,
  • stlc,
  • eq,
  • bva,
  • decision_tables,
  • state_transition_testing,
  • regression_testing,
  • api_testing,
  • ui_testing,
  • test_documentation,
} from "quality-assurance"
## contact§ 00

Let’s build something reliable together.

// Bring the workflow, product, or AI idea you want to move forward — we’ll shape it into a practical system and decide the clearest next step together.

hello@artjeck.com@artjeck

best fit

› AI agents and RAG assistants that need evals
› AI-feature rollouts that need a QA layer
› MVPs and integrations that ship without surprises