Product Architect · AI Systems Builder

JDP

I design and build AI-powered operational systems that turn complex workflows into structured, testable, reusable processes.

AI SystemsProduct ArchitectureAgent WorkflowsOperational Tooling

View Selected Systems View Case Studies

scroll

Selected Systems

Production AI systems — from agent infrastructure to operational platforms.

Execution System ▼ open

Dark Factory

AI product engineering platform — from design interview to spec, wireframe, pipeline run, and published artifact.

Multi-agentSpec-firstFull-stack

page

Agent Governance

AEO Kit + AEO Console

Schema, CLI, and console tooling for governing agent-facing interfaces.

SchemaCLILocal-first

Platform Prototype

NexusAI Platform

High-fidelity AI operations console prototype with local-first data and orchestration UI.

PrototypeOperator UXsql.js

Localization Engine

Omniglot

Localization systems for structured translation workflows, file ingestion, QA, TM, and glossary support.

LocalizationSpec-drivenETL

Local MLOps

TuneKit

Local fine-tuning platform — training runs, adapter registry, eval suites, and efficiency analytics in one operator UI.

Fine-tuningAdaptersLocal-first

Knowledge OS

Knowledge Engine

Technical writer copilot and knowledge workflow system built around doc import, KB emulation, and validation patterns.

CopilotEmulatorValidation

Signal Intelligence

Noise Distiller

Feed aggregation and intelligence platform — correlated event clustering, AI briefings, and synthesis pipeline for signal-heavy research.

Feed aggregationIntelligenceSynthesis

LLM Evaluation

Benchy

LLM code benchmark platform — real production corpora, multi-task evaluation, and per-model analysis with recall, hallucination, and decay metrics.

BenchmarkingCode corporaMulti-model

Developer Tool

Night Loop

Spec-first project OS for agentic development — task lifecycle, work sessions, operational insight, and delivery metrics in a synthwave TUI.

TUISpec-firstLocal-first

Case Studies

Architecture, decisions, and outcomes — per system.

Execution System Case Study

Dark Factory

Explore how this system was designed, built, and validated.

View product page

// problem

AI-assisted product work fragments across chat, docs, and tickets — leaving specs untracked, runs unreviewable, and decisions undiffable.

// what I built

A full-stack platform covering the design-to-delivery loop: interview sessions, spec synthesis, wireframes, pipeline runs, quality scoring, and artifact publishing — all anchored to run IDs.

// why it matters

Treats every product decision as a traceable, replayable run — not a chat log. Specs diff. Runs compare. Artifacts ship.

// key capabilities

→Design interview sessions → definition → plan → roadmap
→Staged pipeline runs with quality scoring and pass rate
→Spec, architecture, QA, and task artifact outputs
→Run comparison and audit trail
→Provider-agnostic (DeepSeek, OpenAI, local)
→Command Center with real-time run metrics

// primary visual

// demo flow

01 Design

Start a product interview session — context, definition, roadmap

02 New run

Trigger a pipeline run from a design or manual spec

03 Inspect

Review run detail — status, quality score, audit log

04 Artifacts

Browse generated SPEC, ARCH, QA, TASKS outputs

05 Compare

Diff runs or export artifacts for publishing

// screenshot gallery

Applied Use Cases

Where these systems ship in practice—from governance to prototyping.

◆

AI-assisted engineering workflows

Turn vague product intent into specs, QA, architecture notes, and implementation tasks.

Proven by

◆

Agent governance

Define, validate, and operate agent-facing tools and policies.

Proven by

◆

AI localization operations

Manage structured translation workflows, file ingestion, QA, TM, glossary, and guarded AI assistance.

Proven by

◆

Local fine-tuning operations

Manage datasets, training runs, metrics, logs, and adapters for local LLM experimentation.

Proven by

◆

Knowledge and documentation intelligence

Import docs, emulate KB workflows, support copilot-style authoring, and connect knowledge to validation patterns.

Proven by

◆

AI platform prototyping

Design operator workflows, orchestration UIs, and local-first platform concepts before backend investment.

Proven by

Visual Proof

Interfaces from deployed systems.

Execution System Workflow orchestration — state graph view

Agent Governance Agent spec editor + eval panel

Platform Prototype Platform cost analytics dashboard

Localization Engine Review queue — multilingual diff view

Local MLOps Fine-tune experiment comparison view

Knowledge OS Retrieval trace inspector

Cross-Project Patterns

Recurring architectural principles.

◆

Spec-First Development

Specs and plans drive work: Dark Factory runs, AEO interface artifacts, and Omniglot Next Gen’s authoritative specs tree.

Dark FactoryAEO Kit + AEO ConsoleOmniglot

◆

Model-Agnostic Architecture

Dry runs, local LLMs, and swappable provider options where implemented—without hard-binding the whole stack to one vendor.

Dark FactoryNexusAI PlatformTuneKit

◆

Local-First + Cloud Hybrid

Workstations and SQLite-first paths, with optional cloud sync or hosting where the repo actually wires it (console, prototype, tuning UI).

AEO Kit + AEO ConsoleNexusAI PlatformTuneKit

◆

Human-in-the-Loop Validation

Review-oriented flows: Knowledge Engine validation materials, Omniglot QA in context, SME-style checks—scope needs confirmation per build.

OmniglotKnowledge Engine

◆

Deterministic Workflows

Dry mode, CLI checks, and artifact-oriented pipelines so outcomes are inspectable instead of one-off chat.

Dark FactoryAEO Kit + AEO ConsoleKnowledge Engine

◆

UI as Operational Surface

Consoles and prototypes built as operator surfaces—dashboards, jobs, tuning, settings—not slide-only narratives.

AEO Kit + AEO ConsoleNexusAI PlatformTuneKit

// closing

These systems represent a shift from documentation into AI-powered product architecture and operational platforms.

Explore All Systems