Files
papers/hanzo-ai-cloud/hanzo-ai-cloud.tex
zeekay 0d1d121451 papers: cover-consistency + build pass on Hanzo Tier-2/3 catalogue
Fix shared/ include paths to ../shared/ for 55 subdirectory papers so
that hanzocover.sty and lstlang.tex resolve when building from each
paper's own directory (the build pattern used by latexmk per paper).

Build fixes:
- hanzo-operative/sections/03-screen-understanding-pipeline.tex: move
  \COMMENT from after \FOR{...} onto the \STATE line so the algorithmic
  env doesn't mis-parse.
- hanzo-network-architecture (abstract + sections 04, 12, 13): escape
  unicode mu (U+03BC) as $\mu$ for pdflatex.
- ntt-transform/sections/08-benchmark-results.tex: same mu escape.

All 55 Tier-2/3 papers now build clean with latexmk -pdf.
2026-05-13 01:11:04 -07:00

110 lines
3.8 KiB
TeX

% Hanzo AI Cloud: Confidential Inference, Agent Hosting, and Model Serving
\documentclass[11pt]{article}
\usepackage{../shared/hanzocover}
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\usepackage{mathtools}
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\usepackage{booktabs}
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\newtheorem{theorem}{Theorem}
\newtheorem{definition}{Definition}
\title{HANZO AI CLOUD\\[0.3em]
{\large Confidential Inference, Agent Hosting, and\\Model Serving on Sovereign Infrastructure}}
\author{
Zach Kelling \\
\textit{Hanzo Industries Inc (Techstars '17)} \\
\texttt{[email protected]}
}
\date{March 2026}
\begin{document}
\hanzocoverpage
% ============================================================================
% ABSTRACT
% ============================================================================
\begin{abstract}
We present Hanzo AI Cloud, an infrastructure layer for AI workloads where the
provider never sees the user's data. Current AI cloud platforms (OpenAI,
Anthropic, Replicate, Together) require users to transmit plaintext prompts,
receive plaintext completions, and trust the provider not to log, train on, or
leak their inputs. This trust model is incompatible with regulated industries
(healthcare, finance, defense) and with any user who values privacy as a
technical guarantee rather than a policy promise.
Hanzo AI Cloud eliminates this trust requirement through three mechanisms:
(i)~confidential inference via CKKS fully homomorphic encryption, where inputs
are encrypted client-side, evaluated homomorphically on the provider's GPUs,
and decrypted only by the client; (ii)~agent hosting with capsule isolation,
where each agent receives a private vault, a decentralized identifier, a
capability budget, and encrypted memory that no other agent or the platform
can access; and (iii)~model serving with verifiable attestation, where every
inference result is accompanied by a cryptographic receipt anchored to the Lux
A-Chain, proving which model produced it, on what hardware, at what time.
We describe the system architecture, the FHE inference pipeline, the agent
capsule model, the model registry, Hanzo Candle (the Rust ML framework
powering GPU inference), the Jin multimodal architecture, edge inference for
mobile and IoT devices, and the provider marketplace with stake-weighted
selection and SLA enforcement.
\end{abstract}
% ============================================================================
\input{sections/01-introduction}
\input{sections/02-the-ai-cloud-problem}
\input{sections/03-confidential-inference}
\input{sections/04-agent-hosting}
\input{sections/05-model-serving}
\input{sections/06-hanzo-candle}
\input{sections/07-jin-architecture}
\input{sections/08-edge-inference}
\input{sections/09-provider-marketplace}
\input{sections/10-comparison-to-existing-platforms}
\input{sections/99-conclusion}
\end{document}