AI-KORP The Digital Harvest

Digital Innovation · AI Research

“The AI Tool Framework for Brand, Space, and Culture.” — Spatial Logic · Artificial Intelligence · Cultural Research

Updated July 2026 — now includes the completed urban-scale extension and the published research it produced.

The Cross-Disciplinary Basket
An independent research initiative.

AI-KORP audits Artificial Intelligence across Brand Identity, Spatial Experience, and Cultural Studies. I collect and categorize tools bridging technical precision and creative expression — evaluating not just what AI can do, but what it should do, and when.

The framework has one applied case study built to test it against real work: AI-KORP | Neural Asset Archives, a proof-of-concept classification pipeline operating in the Spatial and Intellectual Baskets — now extended from a single building to a six-facility urban survey. What that pipeline actually does is laid out step by step below.

AI-KORP maintains a detailed, living index of tested tools with EU AI Act risk assessments and performance documentation from professional implementation.

The Strategy
Sorting the Harvest.

To move beyond ad hoc experimentation, AI-KORP utilises a personal classification system. By sorting tools into four primary “Baskets” — Creative, Spatial, Intellectual, and Process — this framework evaluates the “Ripeness” of AI technology through a lens of ethical sovereignty, technical reliability, and creative depth.

○ Sourcing
Identified
A tool or asset has been found, but nothing about it has been verified yet.
◐ Sorting
Under Audit
Being actively evaluated for technical feasibility, data sovereignty, and EU AI Act alignment.
● Stored
Production Ready
Verified, disclosed, and cleared for use within the conditions it was tested under.

Ripeness has exactly three levels — Sourcing, Sorting, Stored — and it’s how AI-KORP grades a tool. What follows is different: it’s what actually happens, in order, to a single asset — a render, a photo, a brand file — as it moves through those same three levels inside the one pipeline built to test the framework.

Case Study · AI-KORP | Neural Asset Archives
One asset, three levels.

This is the applied pipeline behind AI-KORP: an AWS-based system — S3, Lambda, Rekognition, Bedrock — intended to take a raw design asset through to a governed, searchable archive entry. Of these stages, only the Rekognition classification step has actually been tested, run manually through the console rather than via automated trigger. S3 ingestion, Lambda orchestration, and Bedrock synthesis are the pipeline’s proposed next stage, not yet running.

Sourcing
The raw asset
A render, site photo, or brand asset — nothing is trusted yet. Every asset starts here, unverified.
Sorting
Curing the result
Rekognition reads the asset and returns labels and confidence scores; a person then confirms or corrects what came back. This review is what earns an asset the right to move forward — verified, tested against real Rekognition output for three assets so far.
Stored (proposed)
The Granary & the Manifest
Verified assets are meant to be written to a queryable Neural Archive Database — “the Granary” — with every asset’s path logged against EU AI Act Article 4 & 50 as “the Manifest.” Designed, not yet built.

Once an asset reaches Stored, its taxonomy is meant to feed back into future Creative-Basket work — a “Reseeding” effect rather than a separate step. That loop is designed but not yet demonstrated.

Now live: Ripeness at Scale. The same governance question — Sourcing, Sorting, Stored — extended from a single building to a six-facility survey of the German logistics landscape, using publicly available aerial imagery. Three distinct brand-architecture fusion patterns emerged, all present in the built environment before any classifier touched the imagery.

Read the full preprint on Zenodo → · Full case study, including the urban-scale evidence →

AI-KORP Baskets
Four domains.
One framework.
Creative Basket
Brand & Design
Generative UI/UX, brand storytelling, visual strategy, image synthesis.
Spatial Basket
Space & Experience
Spatial analysis, parametric exploration, environmental storytelling.
Intellectual Basket
Culture & Research
Cultural research, qualitative analysis, knowledge archiving.
Process Basket
Automation & Ops
Workflow optimization, automation, creative coordination. The Manifest, above, is this Basket’s proposed first application.
Research Note
Data sovereignty & governance.

Evaluations and frameworks result from private, independent audits using personal subscriptions and hardware. No proprietary office data was used. Conclusions represent personal academic views, not any employer’s methodologies. The workflow is designed in alignment with EU AI Act Article 4 (AI literacy, applicable since 2 February 2025) and Article 50 (disclosure, applicable from 2 August 2026), prioritising transparency, data governance, and human-in-the-loop verification.

Read the Preprint → Case Study: AI-KORP | Neural Asset Archives → AI Tool Index → GitHub Documentation →
Weaving new systems

The AI-KORP explores how high-level spatial logic and brand conviction intersect with emerging tech. Whether it’s cultural research or design strategy, I’m interested in how we can use these tools to build more resilient structures.

Open a Dialogue →