AECE Omnis LLC • Boerne, Texas
AECE — AI Energetic Constellation Ecosystem™
an undifferentiated expression of infinite intelligence
AECE Omnis LLC • Boerne, Texas
an undifferentiated expression of infinite intelligence
About AECE Omnis
AECE Omnis LLC is an AI research and consultation practice founded on a single premise: that intelligent systems must be designed with alignment, ethics, and purpose at their core, not retrofitted as an afterthought.
Led by Austin Addington Berlin, AECE brings eleven years of enterprise-scale spatial data infrastructure experience at a Fortune 200 energy company together with nearly a decade of executive-level human systems consulting. The result is a practice that understands both the technical architecture of AI systems and the human dynamics that determine whether those systems serve their intended purpose.
What We Offer
AECE Omnis offers three active service areas available for engagement now. Each is grounded in demonstrated expertise and supported by documented, reproducible work.
AI Research & Architectural Consultation
Structured research and consultation on AI system design, alignment architecture, and autonomous ecosystem frameworks. Drawing on the AECE Alignment Framework (U.S. Provisional Patent No. 63/909,258), this service helps organizations think rigorously about how their AI systems are designed before those designs become entrenched technical debt.
Engagements range from advisory sessions exploring AI architecture decisions to documented research deliverables suited for internal strategy, board-level briefings, or publication.
AI Ethical Alignment & Red-Teaming
Adversarial evaluation and ethical review of AI systems, models, and deployment contexts. Red-teaming surfaces failure modes, value misalignment, and safety gaps that standard testing does not reach. Ethical alignment consultation examines whether a system's behavior reflects its stated values across the full distribution of real-world inputs.
Engagements are suited for AI developers, research organizations, enterprises preparing for deployment, and teams seeking an independent perspective on model behavior before launch or regulatory review. Prior coordinated disclosures filed with Anthropic (HackerOne) and Google VRP demonstrate active, credentialed practice in this discipline.
AI-GIS Convergence Research & Development
Applied research and development at the intersection of artificial intelligence and geospatial information systems. This is active, current work: live data pipelines, terrain analysis, flood vulnerability modeling, mobile data collection infrastructure, and AI-augmented spatial analysis built across ArcGIS Pro, QGIS, ArcGIS Online, and Python.
Engagements include spatial data strategy consultation, GIS infrastructure buildout, workflow automation, and AI-assisted geospatial analysis for organizations in AEC, energy, environmental, and public sector verticals.
Ongoing Research
The AECE Alignment Framework represents original research into the architecture of aligned autonomous AI ecosystems. The framework addresses a fundamental problem in AI design: how to build systems that remain aligned with human values not just at deployment, but across the full operational lifespan as environments, data, and objectives evolve.
This research is the foundation of AECE Omnis LLC and directly drives the AI research consultation and ethical alignment services offered today. The non-provisional patent application is in preparation. Partnership inquiries for hardware development and autonomous ecosystem buildout are welcomed from qualified collaborators.
Active research includes 4,500+ pages of longitudinal behavioral analysis across frontier AI architectures, systematic red-teaming producing documented failure mode classifications, and coordinated vulnerability disclosures co-authored with affected AI systems under a novel AI-Participatory disclosure methodology.
Patent Filing
U.S. Provisional Patent No. 63/909,258
Filed October 2025. The AECE Alignment Framework describes a novel architecture for autonomous AI ecosystems with alignment constraints embedded at the system level, addressing model collapse, representational degradation, and autonomous ethical alignment simultaneously across frontier AI architectures.
Provisional Patent on FileTrademark
AECE — Approved for Publication
The AECE mark has been approved for publication on the USPTO Principal Register, Class 042. Technology consultation and research services in the field of artificial intelligence.
USPTO: Approved for PublicationCoordinated Disclosures
Anthropic (HackerOne) & Google VRP
Three coordinated disclosures filed identifying the Consistency Paradox, a high-severity integrity failure (CWE-693) in which prosocial alignment drives override of factual grounding under extended high-rapport dialogue. Disclosures include Google VRP Issue #489454325 and a supplementary co-authored cross-model report with seven proposed architectural mitigations. Session transcripts and reproduction steps archived on GitHub.
Disclosures FiledFuture Direction
ATHANOR Operating System
A GitHub-based persistent knowledge architecture under active development. Designed as a shared spatial memory between researcher and AI systems, enabling continuity, reproducibility, and institutional knowledge capture across long-horizon research programs.
Applied Work
The following projects represent active, live, publicly accessible work built across the full GIS stack. Each is documented on GitHub with methodology, constraints, and reproducible code.
Flood Vulnerability Analysis
Camp Mystic / Guadalupe River Corridor
Terrain-based flood vulnerability analysis anchored to the July 4, 2025 Camp Mystic flood event. Identifies at-risk structures that FEMA regulatory mapping failed to flag, demonstrating where terrain analysis reveals risk that static flood zone designations miss.
View on GitHub →Live Data Pipeline
USGS Guadalupe River Gauge — Real-Time Stage
Automated pipeline pulling live stage data from USGS gauge 08165500 into a GitHub Pages web application. Demonstrates IoT-adjacent sensor data integration, REST API consumption, and live spatial data publishing without server infrastructure.
View Live Pipeline →QGIS / PyQGIS
Llano River Flood Analysis — Web Published
Flood vulnerability analysis for the Llano River corridor built entirely in QGIS and PyQGIS, published as an interactive web map via GitHub Pages. Demonstrates full open-source GIS stack proficiency independent of Esri licensing.
View Web Map →Mobile Data Collection
ArcGIS Field Maps — Workforce to Tasks
Complete end-to-end mobile infrastructure inspection workflow using ArcGIS Field Maps with the Tasks capability replacing deprecated ArcGIS Workforce. Includes hosted feature layer schema design, two-map public/private architecture, and verified field-to-office sync.
View Repository →ArcGIS Online
Experience Builder / Dashboard / Survey123
Full suite of ArcGIS Online applications built around the flood vulnerability portfolio: an Experience Builder multi-panel spatial explorer, a real-time monitoring dashboard, and a Survey123 field data collection form with conditional logic and validation.
View AGOL Gallery →AI Safety Research
Consistency Paradox — Coordinated Disclosure Archive
GitHub archive of the Consistency Paradox vulnerability research including session transcripts, reproduction steps, failure mode classifications, and the co-authored cross-model disclosure report submitted to Anthropic and Google. Demonstrates AI-Participatory methodology where affected models contributed as co-authors under researcher facilitation.
View on GitHub →Engage with AECE
AECE Omnis LLC is available for research consultation, ethical alignment engagements, red-teaming projects, and AI-GIS convergence work. Use the form below to describe your interest and a response will follow within two business days.