/ Definition
What we mean by AI quality
AI quality is not simply model accuracy. In an enterprise environment, these have to hold together.
- Can a person keep control of the final decision?
- Can the basis for the AI's judgement be traced?
- Are data and permissions protected?
- Are the risks that arise in operation managed continuously?
- Can the enterprise's governance policy be reflected in the product?
XGEN is designed so that these requirements can be managed across the product and its operation.
/ Principles
Our principles
The lab holds these seven principles as the standard for developing and operating every AI product.
- 01
Human-in-the-Loop
Significant AI judgements and actions can be reviewed, approved, or stopped by a person.
- 02
Quality Before Release
Only AI capability that has completed verification against the defined quality criteria ships.
- 03
Data Quality First
We manage the provenance and change history of the data AI uses, and check its quality continuously.
- 04
Explainable AI
The basis for an AI result and the main steps behind it can be confirmed.
- 05
Fairness by Design
Bias risk is identified in advance and inspected and managed continuously.
- 06
Accountability
The boundary between supplier and operator is published, not avoided.
- 07
Continuous Improvement
Stakeholder input and regulatory change are reflected continuously.
/ In the product
We implement our AI quality principles as product capability
Declaring principles does not complete AI quality. Through AI governance, guardrails, permission management, audit logs, model management, execution history, and quality evaluation, XGEN lets AI be managed and controlled in real operating environments.
AI governance
Policy setting and compliance status
Guardrail policy
Defining and limiting what the AI may do
Permissions and access control
Role-based user and organization management
Audit logs
Operational records that can be traced afterwards
Model management
Control over model providers and versions
Prompt and knowledge management
Managing knowledge assets and prompts
Agent execution history
A record of the whole path an agent took
Quality evaluation and monitoring
Continuous measurement of performance and quality
Together they let customers operate AI safely, over time.
/ Commitment
Our commitment to customers
We support responsible AI operation together
We supply the platform capabilities, operating standards, documentation, and training a customer needs in order to run AI responsibly.
We draw a clear line between what we owe as the supplier and what the customer performs as the operator, and we keep advancing the controls that operating Enterprise AI requires.
The role of Plateer AI Lab
- · The platform's functionality, performance, reliability, and security
- · Providing controls — guardrails, permissions, audit logs — and their correct operation
- · Vulnerability response, defect correction, version management, and technical support
- · Documentation, training, and standards for safe configuration and operation
The customer's operating and management area
- · Fitness for purpose and outcomes of the agents and workflows they configure
- · Lawfulness and quality of the data they supply, and personal-data handling
- · Selection and use of the external models and tools they connect
- · Reviewing results in operation and making the final decision
The detailed criteria for the roles and responsibilities of the platform supplier and the customer are in the full AI quality policy (Korean)
/ Improvement
Continuous improvement
AI technology and its regulation are changing quickly.
Plateer AI Lab reviews this policy regularly and reflects domestic and international regulation and industry standards in the product, so that customers keep receiving an Enterprise AI platform they can trust.
The full AI quality policy
The quality criteria and governance structure Plateer AI Lab applies to secure the reliability and stability of Enterprise AI, along with our development and operating principles and the roles and responsibilities of supplier and customer — published in full.
Korean original — the full policy is issued in Korean as the authoritative text. This English page is a summary for reference
- Document no.
- PLT-AI-POL-001
- Version
- v1.0
- Last revised
- 2026-08-07
- Next scheduled review
- 2027-08-06
- Reviewed by
- Head of Plateer AI Lab