HugstonOne Enterprise Edition: Architecture, Privacy Model, and Capability Benchmark for a Privacy Local First AI Workstation | Zenodo
Skip to main
You are using an outdated browser. Please upgrade your browser to improve your experience.
Hugston
Published July 21, 2026
| Version HugstonOne Enterprise Edition 3.0.0
Software documentation
Open
HugstonOne Enterprise Edition: Architecture, Privacy Model, and Capability Benchmark for a Privacy Local First AI Workstation
Authors/Creators
Fernandez Vidal, Leyden<br>(Researcher)1, 2
Bregu, Klaudi<br>(Project leader)3, 4, 2
Show affiliations
1.
Umeå University
2.
Bionomic AB
3.
Hugston.com
4.
HugstonOne
Description
The core claim is simple: as of June 20, 2026, no other standalone, publicly documented local AI application combines the full set of features HugstonOne Enterprise Edition offers, in one fully user controlled interface.
HugstonOne Enterprise Edition is a standalone, cross platform, Privacy local first AI workstation that combines local model execution, large source RAG, document processing, coding, agents, research tools, encrypted collaboration, session continuity, and explicit network and memory controls within one desktop environment. It is designed to reduce the fragmentation, privacy risks, and operational complexity created when these capabilities depend on separate applications, cloud services, user accounts, telemetry, or external APIs. This whitepaper presents the product architecture, annotated interface evidence, benchmark methodology, competitive application profiles, limitations, and verification roadmap. Its supporting evidence includes a weighted 12 pillar capability benchmark assessing the documented functionality and integration of privacy first local AI workstations rather than raw inference speed. The paper is intended for enterprise technology leaders, security and privacy teams, AI engineers, researchers, developers, and organizations evaluating locally controlled AI infrastructure
Technical info
local-first AI<br>local AI workstation<br>privacy-preserving AI<br>offline AI<br>edge AI<br>large language models<br>local LLM inference<br>retrieval-augmented generation<br>RAG<br>AI agents<br>enterprise AI<br>secure AI infrastructure<br>research software<br>GGUF<br>AI benchmarking
Files
HugstonOne_Enterprise_Whitepaper_2026.pdf
Files<br>(1.4 GB)
Name<br>Size
HugstonOne Enterprise Edition-3.0.0-portable-x64.exe
md5:6ddb796a5b866161c2ae39a41b5622d9
452.4 MB
Download
HugstonOne Enterprise Edition-3.0.0-setup-x64.exe
md5:c8e1fbbeddd1e99ffd3f84922b38d1c3
452.7 MB
Download
HugstonOne Enterprise Edition-3.0.0-x64.msi
md5:de8600550c5f3cb4d0cda6e20b305dd0
465.2 MB
Download
HugstonOne_Enterprise_Whitepaper_2026.pdf
md5:a245997387278e74c1d87e4898edcc51
2.8 MB
Preview
Download
Additional details
Dates
Copyrighted
2026-07-20
New Major Upgrade
Software
Repository URL
https://Hugston.com
Programming language
JavaScript
CSS
HTML
Development Status
Active
References
https://Hugston.com
https://github.com/Mainframework/HugstonOne
Views
Downloads
Show more details
All versions<br>This version
Views
Total views
Downloads
Total downloads
Data volume
Total data volume
0 Bytes<br>0 Bytes
More info on how stats are collected....
Versions
External resources
Indexed in
OpenAIRE
Communities
Details
DOI
DOI Badge
DOI
10.5281/zenodo.21471816
Markdown
[](https://doi.org/10.5281/zenodo.21471816)
reStructuredText
.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.21471816.svg<br>:target: https://doi.org/10.5281/zenodo.21471816
HTML
Image URL
https://zenodo.org/badge/DOI/10.5281/zenodo.21471816.svg
Target URL
https://doi.org/10.5281/zenodo.21471816
Resource type<br>Software documentation
Publisher<br>Zenodo
Conference
Intellectual Property and GDPR compliance<br>, Umeå, 2026_05_25
Languages
English
Rights
License
Creative Commons Attribution 4.0 International
The Creative Commons Attribution license allows re-distribution and re-use of a licensed work on the condition that the creator is appropriately credited.
Read more
Hugston Licensed
This is a Proprietary License issued by Hugston
Read more
Copyright
Copyrights (C) 2026 to the Authors
Citation
Export
Technical metadata
Created
July 21, 2026
Modified
July 21, 2026
Jump up
This site uses cookies. Find out more on how we use cookies
Accept all cookies<br>Accept only essential cookies