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 · 1–2 day reply
./start-projectcat ./engagements.md
artjeck/system · status
--:--:-- UTC
›system check ── v0.4.2·build 2a4f1c7·uptime 99.97%
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

The combination matters: AI engineer who tests like QA.

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

Patterns I take from zero to production.

// Each pattern below is shaped around real business problems, with the engineering choices, quality checks, and measurable outcomes that ship the work — not a demo.

artjeck/ai-knowledge-assistant

AI Knowledge Assistant

Teams need faster access to trusted internal knowledge without manually searching long documents.

approach
· A retrieval-based assistant that grounds answers in approved sources and exposes citations for review.
stack
· LLM APIs, RAG pipeline, vector database, document ingestion, web interface
quality
· Evaluated retrieval quality, citation coverage, refusal behavior, and edge-case prompts before release.
result
· A reliable assistant experience that can reduce research time while keeping answers auditable.
TypeScriptcitation coverage·100%refusal eval·passingp95 latency·1.4 s
artjeck/workflow-automation

AI Workflow Automation System

Manual operational handoffs create delays, inconsistent outputs, and repeated status-check work.

approach
· A multi-step automation that routes inputs, triggers agent tasks, validates outputs, and notifies stakeholders.
stack
· Agents, workflow orchestration, API integrations, structured prompts, dashboards
quality
· Covered happy paths, failure branches, retry behavior, data validation, and regression scenarios.
result
· A production-oriented workflow that turns repeatable manual tasks into a dependable system.
TypeScripthappy path·coveredretry policy·exp-backoffincidents·0 / 30d
artjeck/ios-productivity

iOS Productivity App

Mobile users need a focused way to capture tasks, organize priorities, and keep momentum while away from desktop tools.

approach
· A SwiftUI iOS app with simple flows, persistent state, responsive screens, and clean task organization.
stack
· Swift, SwiftUI, local persistence, notifications, iOS design patterns
quality
· Validated key user journeys, empty states, state transitions, device layouts, and release smoke tests.
result
· A polished mobile experience shaped for everyday use and ready for iterative product growth.
Swiftsmoke suite·greenscreen sizes·allcrash-free·100%
artjeck/business-website

Production Business Website

A business needs a trustworthy web presence that explains the offer clearly and converts qualified leads.

approach
· A fast responsive website with structured messaging, service pages, lead capture, and SEO foundations.
stack
· Next.js, TypeScript, Tailwind CSS, analytics-ready architecture, deployment
quality
· Checked responsive layouts, form behavior, accessibility basics, cross-browser rendering, and launch paths.
result
· A clean production site that communicates credibility and gives customers a clear next action.
TypeScriptlighthouse·98 / 100a11y audit·AAdeploy·static
## 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 that holds up in production.

// Tell me about the workflow, product, or AI system you want to ship — I’ll reply within 1–2 business days with whether it’s a fit and what the next step looks like.

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