LLM Compliance Policy

This policy defines how Seekle accesses, uses, and governs Large Language Models (LLMs) and related AI systems.

The objectives of this policy are to:

• Ensure compliant use of third-party AI services
• Prevent misuse or abuse of LLMs via the Seekle platform
• Maintain alignment with provider terms, intent, and billing models
• Support security, availability, and auditability requirements

This policy applies to:

• All AI-powered features offered by Seekle
• All internal systems invoking LLMs or AI services
• All employees, contractors, and systems acting on behalf of Seekle

3.1 Outcome-Based AI Use

Seekle uses LLMs solely as internal components within defined product features.

Seekle does not:

• Provide direct access to LLMs
• Act as an AI proxy, relay, or general-purpose execution layer
• Allow unrestricted or user-defined AI workflows

Users interact only with feature outputs, not underlying models.

4.1 Authentication

All LLM-backed functionality requires authenticated access.

Each request is associated with:

• A valid user account
• An approved product feature
• A traceable execution context

4.2 Prohibited Access

Seekle does not permit:

• Anonymous LLM access
• Programmatic or automated access outside approved features
• Direct user-supplied prompts to LLMs

5.1 Controlled Inputs

All LLM interactions are:

• Internally generated
• Template-based
• Parameterized and schema-constrained

User input is treated as data, not executable instructions.

5.2 Predictable Outputs

LLM outputs are validated and processed to ensure:

• Consistency
• Predictability
• Alignment with the intended feature purpose

6.1 Approved Access Methods

Seekle accesses LLMs only through:

• Official, documented APIs
• Supported SDKs and authentication methods

6.2 Prohibited Practices

Seekle will not:

• Spoof first-party tools or CLIs
• Use undocumented endpoints or headers
• Bypass billing, throttling, or quota controls
• Misrepresent usage as consumer or interactive traffic

Seekle does not:

• Accept or store customer-owned LLM API keys
• Relay third-party credentials on behalf of users

All provider credentials are:

• Managed centrally by Seekle
• Access-controlled and monitored
• Used solely for approved platform functionality

Each AI-powered feature has defined:

• Expected token usage ranges
• Execution time limits
• Invocation frequency limits

Controls include:

• Feature-level rate limiting
• User-level usage caps
• Automatic throttling of anomalous behavior

Seekle implements caching mechanisms to:

• Reduce redundant LLM requests
• Prevent excessive upstream load
• Improve availability and cost predictability

Caching is used in accordance with feature accuracy requirements and data handling standards.

10.1 Permitted Automation

Automation is limited to:

• Scheduled reports
• Periodic refresh operations
• System-controlled background tasks

10.2 Prohibited Automation

Seekle does not allow:

• User-defined agents or loops
• Arbitrary job execution
• Long-running autonomous workflows

All automation is auditable and rate-limited.

Seekle commits to responsible data collection practices, including:

• Compliance with robots.txt and crawl directives
• Transparent and consistent user-agent identification
• Reasonable request rates

Seekle does not engage in stealth, proxy, or deceptive crawling behavior.

12.1 Monitoring

Seekle monitors:

• Feature-level usage patterns
• Token consumption
• Repetition and anomaly indicators

12.2 Enforcement

Violations of this policy may result in:

• Warning
• Throttling
• Temporary suspension
• Termination of access, where necessary

This policy is reviewed periodically and updated to reflect:

• Changes in provider terms
• Regulatory developments
• Platform architecture evolution

Seekle is designed to operate within provider intent, not around it.

Features that rely on:

• Undocumented behavior
• Misrepresentation of usage
• Circumvention of safeguards

are not permitted.

Questions regarding this policy may be directed to: support@seekle.io.