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AI Model Lab

A no-cost glimpse into an enterprise AI interface.

Explore curated examples of how a controlled AI assistant can present technical guidance. This demonstration is static: it makes no API calls, collects no prompts, and creates no model-usage charges.

AI LAB
Demonstration mode: The interface below uses prewritten, provider-neutral examples. It is not connected to OpenAI, Anthropic, Google, Hugging Face, or any other model provider. Do not submit classified, CUI, proprietary, personal, or otherwise sensitive information through public AI services.
Example interaction

Private AI architecture briefing

Convoi AI Architecture Demonstration
Example user promptExplain the core components of a private enterprise LLM platform for a technical program manager.
Illustrative AI responseA private enterprise LLM platform combines five operating layers: infrastructure, orchestration, inference, access, and governance. GPU compute and storage provide the foundation. Kubernetes or another scheduler manages workloads. An inference engine serves approved models through controlled APIs. An authenticated user interface provides access, while monitoring, audit, data-handling rules, and lifecycle processes keep the platform supportable. The architecture should begin with use cases and data boundaries, then size compute and software around validated demand.
Illustrative content created for this website; it is not a captured response from a named commercial model and should not be used as technical or security authorization.
What changes by deployment model

Three common AI service patterns

Hosted Frontier Service

Fast access to highly capable managed models through a provider API.

  • Low infrastructure burden
  • Rapid feature availability
  • External data-processing boundary
  • Usage-based cost and provider dependency
Operational control
Deployment speed

Private Open-Weight Platform

Models run inside infrastructure controlled by the organization.

  • Greater data and platform control
  • Supports restricted environments
  • Requires GPU and operational capability
  • Model quality and sizing must be validated
Operational control
Deployment speed

Hybrid AI Service

Workloads are routed between private and hosted models according to policy.

  • Flexible capability and cost
  • Can preserve sensitive-data boundaries
  • Requires classification and routing controls
  • More integration and governance complexity
Operational control
Deployment speed
Example prompt library

What an AI pilot should test

A useful proof of concept evaluates representative work—not novelty prompts. These examples can be adapted into a controlled validation plan.

Summarize a technical standardExplain an architecture decisionDraft an implementation checklistCompare platform optionsExtract risks and dependenciesAnswer from approved internal sources
Evaluation matters. Measure factual accuracy, source grounding, latency, concurrency, data handling, failure behavior, and operational support—not just whether a response sounds fluent.
No-cost today, extensible later

Designed for a future live demonstration.

If Convoi later enables live model access, this interface can be connected to a protected server-side gateway with rate limits, spend controls, abuse protection, and clear data-use notices. No such connection is active today.

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Considering an AI proof of concept?

Define the workload, security boundary, and evaluation plan before selecting the platform.

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