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Built Systems

Systems I have built and operated.

Examples of how problem framing, intelligence allocation, and system building come together in practice.

Live systems

Check Text Bias Logo

CheckTextBias

Curated Outbound Logo

CuratedOutbound

Judge My Image Logo

JudgeMyImage

Judge My Website Logo

JudgeMyWebsite

Text Response Hub Logo

TextResponseHub

Check Text Bias Logo

CheckTextBias

AI-powered text analysis for identifying framing and bias patterns in articles, news coverage, and written communication.

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Problem

People can sense that texts frame reality differently, but it is hard to compare those differences in a structured way.

Intelligence allocation

AI expands and structures the analysis. Human judgment defines what matters and how the result should be interpreted.

System

The system analyzes text for framing patterns, selection effects, emphasis, linguistic signals, and interpretive structure.

Demonstrates: Pattern extraction + structured interpretation

Curated Outbound Logo

CuratedOutbound

AI-assisted systems for prospect research, qualification, and personalized outreach.

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Problem

Outbound work is usually too manual, too generic, or too poorly qualified to scale well.

Intelligence allocation

Software handles repeatable workflow. AI interprets company context. Human judgment sets positioning, quality thresholds, and outreach strategy.

System

The system combines website analysis, prospect evaluation, email research, database workflows, and AI-assisted message generation.

Demonstrates: Automation + AI workflow integration

Judge My Image Logo

JudgeMyImage

Human-guided aesthetic refinement systems for evaluating and improving images.

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Problem

Image quality is not only technical. It depends on impression, context, taste, credibility, and human response.

Intelligence allocation

Humans evaluate subjective quality. Software structures the feedback process. AI can help summarize patterns and suggest improvements.

System

The system collects and structures human feedback so images can be improved based on actual viewer judgment.

Demonstrates: Where human judgment still matters

Judge My Website Logo

JudgeMyWebsite

Human-guided website evaluation systems for improving clarity, trust, usability, and communication.

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Problem

Website owners often struggle to understand how real visitors experience their site, where confusion emerges, and what weakens trust or conversion.

Intelligence allocation

Humans provide interpretation, trust judgments, and usability feedback. Software structures collection and analysis. AI helps identify recurring patterns and themes.

System

The system gathers structured human feedback from real visitors and organizes the results into actionable website improvement insights.

Demonstrates: Human feedback systems + structured interpretation

Text Response Hub Logo

TextResponseHub

AI-powered systems for analyzing surveys, reviews, feedback, support conversations, and other open-ended text datasets.

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Problem

Large collections of written responses are difficult to process consistently, making important signals and recurring themes easy to miss.

Intelligence allocation

AI processes and structures large text datasets. Humans define the interpretive goals, evaluate significance, and determine how findings should influence decisions.

System

The system analyzes qualitative text data to identify themes, patterns, concerns, sentiment structures, and recurring response categories.

Demonstrates: Large-scale qualitative analysis + insight extraction

What these systems have in common

They are not built around a single technology. They are built around a problem structure.

Software provides repeatability. AI expands and analyzes. Human judgment sets direction, evaluates quality, and handles meaning where automation is not enough.

That combination is the real system.

1

Frame the problem

Clarify what is actually being solved before anything is built.

2

Allocate intelligence

Decide what belongs to software, AI, and human judgment.

3

Build the system

Design and implement a system that works in practice.

Have a system worth building?

Let’s clarify the problem, allocate the right intelligence, and build something that works.