Describe third-party model involvement accurately
Where a model is routed to a third party, the route should be represented accurately. Public evaluation claims require versioned baselines, measured tasks and reproducible methodology.
ELP GPT / SAFETY
Provenance, truth and transparency
ELP GPT differentiates its proprietary intellectual property from third-party foundation models, open-source components and cloud services.
OUR APPROACH
Trust & transparency: principles in practice
Avoid invented benchmarks and unsupported AGI claims
ELP GPT connects proprietary engineering to responsible, task-specific customer outcomes.
Give users meaningful review and correction paths
ELP GPT connects proprietary engineering to responsible, task-specific customer outcomes.
THE ELP GPT PERSPECTIVE
Trust & transparency — engineering and execution
Model-family branding alone does not establish independently trained foundation weights.
Trust depends on accurate model provenance, controlled execution and measurable results. ELP develops its own application, orchestration and model programme; independent providers retain their model weights, software and licences. Internal evaluations and incident records inform governance.
Technology, ownership & transparency
ELP GPT is developed by ELP Ventures. ELP-owned software and model research are distinct from independently owned inference services, third-party libraries and intellectual property. Specifications, availability and comparative performance depend on the deployed configuration.
Research and safety reference frameworks
Research and governance methods can be assessed against public resources such as the NIST AI Risk Management Framework and the European Commission guidance on general-purpose AI. These references inform evaluation and do not mean ELP GPT is certified or endorsed.