Data Sovereignty and Residency
- Can you guarantee that all voice data processing occurs within our specified geographic region without any external API calls?
- Do you maintain complete audit logs of every system that touches our customer voice data, including timestamps and processing locations?
- In the event of litigation requiring data preservation (like the OpenAI-NYT case where OpenAI must preserve all user data indefinitely), how would you handle court orders affecting third-party providers versus your own infrastructure?
Model Behavior and Control
- Can you modify the AI model's behavior within 24 hours if we identify inappropriate responses, without depending on external providers?
- What happens to our custom voice models and conversation data if a third-party provider changes their terms of service or pricing?
- How do you prevent bias injection when third-party providers update their models without your knowledge or consent?
- Can you roll back to a previous model version immediately if an update introduces unacceptable bias or behavior changes?
Hallucination Risk Management
- What specific risk profiling methodologies do you employ to measure and track hallucination rates across different conversation types?
- Can you provide quantitative metrics on hallucination frequency broken down by domain (medical, financial, legal)?
- How do you detect and prevent hallucinations that might not be caught by the LLM's own confidence scoring?
- What is your baseline hallucination rate and how do you ensure it doesn't degrade with model updates?
Non-LLM Verification Systems
- What deterministic, rule-based systems verify that AI responses comply with regulatory requirements before delivery?
- Do you employ any non-AI guardrails (regex patterns, keyword filters, structured validation) to catch problematic outputs?
- How do you verify numerical accuracy and factual claims without relying solely on the language model?
- Can you demonstrate a multi-layer verification architecture that doesn't depend on LLM self-assessment?
Performance Guarantees
- Can you contractually guarantee sub-second response times regardless of OpenAI's or other providers' traffic levels?
- During a third-party service outage (like the OpenAI outages in 2024), how do you maintain service continuity?
Security and Compliance Verification
- Can you provide evidence that our voice data is never used to train models, including at third-party providers?
- How do you ensure HIPAA compliance when patient voice data might contain protected health information?
Cost Predictability and Transparency
- Can you provide a fixed-cost model that doesn't fluctuate based on third-party API pricing changes?
- What hidden costs might emerge as we scale to millions of calls monthly?
Infrastructure Control
- Can you deploy the entire solution within our private cloud or on-premises data center?
- How quickly can you implement custom security controls or encryption methods we require?
Third-Party Dependency Risks
- What is your disaster recovery plan if a critical AI provider permanently shuts down?
- How do you handle situations where third-party providers' values or actions conflict with our corporate policies?
Intellectual Property Development and Differentiation
- How can we build proprietary conversational experiences if we're using the same base model as every other enterprise customer?
- Can you fine-tune models exclusively for our use case, ensuring competitors cannot access our optimizations?
- What prevents another company from replicating our exact conversational agent if they use the same third-party APIs?
- Do you offer exclusive voice actor licensing so our brand voice cannot be used by competitors?
- How do you ensure our training data and conversation patterns remain our intellectual property and don't improve models used by others?
- Can we patent or otherwise protect the unique conversational flows we develop on your platform?
Technical Model Optimization and Hyperparameters
- If you used LoRA for fine-tuning:
- What are the specific Alpha and R values
- How many parameters were unfrozen?
- Which optimizer was employed?
- Can you dynamically prune model parameters to optimize for our specific latency requirements without full retraining?
- How else can you improve or optimize performance / accuracy?
- What is your approach to catastrophic forgetting when fine-tuning?
- Do you use elastic weight consolidation or other regularization techniques?
- Can you add task-specific parameters or adapter layers without affecting the base model performance?
- What are the exact learning rate schedules, batch sizes, and gradient accumulation steps used in your training pipeline?
- Do you support quantization-aware training, and at what bit precision (INT8, INT4) can models run while maintaining accuracy?
- What mixture-of-experts routing mechanisms do you employ, and can we adjust the gating network for our use case?
- How do you handle gradient checkpointing and memory optimization for large-scale model deployments?
- Can you implement custom attention mechanisms or positional encodings specific to our conversational patterns?