DiscoverArchAI
An architecture-native intelligence that shortlists competition entries with the precision of a seasoned reviewer — trained on real submissions, design theory, and architectural excellence.
Submissions Analyzed
Across all competitions
Reviewer Agreement
Human–AI alignment rate
Competitions Powered
And growing each cycle
Avg. Evaluation Time
Per design entry
Architecture-nativeintelligencebuiltforprecision,fairness,andtransparency.
Your design work deserves a reviewer that sees technical precision, spatial creativity, and conceptual depth — not just surface aesthetics.
Bias-Free
Anonymized entries evaluated purely on design merit.
Explainable
Every shortlist includes a detailed AI reasoning report.
Architecture-Native
Trained exclusively on architectural submissions and theory.
Continuously Learning
Jury feedback improves the model with every competition cycle.
HowItWorks
Submission Ingestion
Every format. Every medium. Structured for analysis.
All competition entries — design boards, 3D renders, technical drawings, written manifestos, and site analyses — are ingested into the system in a standardised format. The AI strips identifying information and structures each submission for multi-dimensional scoring.
AI Shortlisting Engine
Six architectural dimensions. One precision score.
The model evaluates each entry across six architectural criteria — originality, technical execution, sustainability, presentation quality, brief adherence, and spatial creativity. A weighted composite score determines shortlist eligibility, with reasoning logs generated automatically.
Independent Jury Review
Human judgment. Uninfluenced. Final.
Shortlisted entries pass directly to DiscoverArch's expert jury panel of practicing architects, academics, and design researchers. AI scores are never disclosed to jurors — the panel deliberates independently, ensuring that human creative judgment remains sovereign at the decisive stage.
Privacy by Design: AI shortlisting scores are never disclosed to the jury panel. Jurors deliberate independently, ensuring unbiased human judgment at the decisive stage.
Built on
"Unlike generic AI, DiscoverArch AI is trained exclusively on data relevant to architecture — and nothing else."
BuiltonArchitecturalKnowledge.NotGenericData.
12,000+ Past Submissions
From DiscoverArch competitions
Real student entries across categories including housing, adaptive reuse, and urban design — scored, annotated, and curated by our team.
Architectural Literature
Academic & professional journals
Peer-reviewed papers, architectural theory texts, and professional critique — forming the intellectual backbone of the model.
Expert Jury Feedback
Real deliberations, not synthetic data
Written jury notes and scoring rationale from 40+ practicing architects and educators, providing human-calibrated signal.
Continuously Updated
Every competition improves the model
Post-cycle retraining incorporates new jury feedback, keeping the model aligned with evolving architectural standards.
BuiltforFairness&Transparency
Architecture-Native AI
Trained exclusively on architectural submissions, jury notes, and academic literature — not fine-tuned GPT applied to architecture as a side use-case.
Bias-Free Evaluation
All submissions are anonymised before scoring. Evaluations are grounded in design merit alone — not institutional name, location, or presenter identity.
Explainable Shortlisting
Every shortlist decision comes with a detailed AI reasoning report covering all six criteria. No black box. Full transparency.
Continuously Learning
Each competition cycle, jury feedback is re-integrated into the model as training signal. The AI improves with every edition.
Secure & Confidential
All submissions remain fully confidential. The AI never shares, repurposes, or trains on your design work without explicit consent.
Global Benchmarking
Scored against an international corpus spanning 25 countries and 12,000+ entries — giving your work a truly global reference point.