E01¶


Research	R0042 — Private AI Motivations
Run	2026-04-01
Query	Q002
Source	SRC03
Evidence	SRC03-E01
Type	Analytical

Allganize's documentation of customization as an on-premises AI motivation

URL: https://www.allganize.ai/en/blog/enterprise-guide-choosing-between-on-premise-and-cloud-llm-and-agentic-ai-deployment-models

Extract¶

On-premises deployments enable organizations to "tailor the AI solution to the specifics of the industry, enterprise, and teams." The guide states that on-prem achieves "highest accuracy as LLM and RAG can be specifically trained on business-specific data."

The customization described includes: - Industry-specific terminology and patterns - Business-specific data training - Team-level tailoring

The guide does NOT mention sycophancy, response style control, interaction norms, or behavioral correction as customization goals. Customization means accuracy and domain specialization.

Relevance to Hypotheses¶

Hypothesis	Relationship	Strength
H1	Contradicts	Customization IS documented but not sycophancy-related
H2	Supports	Confirms customization is about accuracy/domain, not behavioral correction
H3	Contradicts	Customization beyond security IS documented

Context¶

The type of customization Allganize describes is "additive" — making the model better at a specific domain. This is fundamentally different from the "corrective" customization Q002 asks about — fixing behavioral defects like sycophancy. The enterprise customization conversation is about adding capability, not removing pathology.