Questions every enterprise should ask
a voice AI vendor.

01

Data sovereignty and residency

1. Q01Can you guarantee that all voice data processing occurs within our specified geographic region without any external API calls?
2. Q02Do you maintain complete audit logs of every system that touches our customer voice data, including timestamps and processing locations?
3. Q03In the event of litigation requiring data preservation, how would you handle court orders affecting third-party providers versus your own infrastructure?

02

Model behavior and control

1. Q01Can you modify the AI model's behavior within 24 hours if we identify inappropriate responses, without depending on external providers?
2. Q02What happens to our custom voice models and conversation data if a third-party provider changes their terms of service or pricing?
3. Q03How do you prevent bias injection when third-party providers update their models without your knowledge or consent?
4. Q04Can you roll back to a previous model version immediately if an update introduces unacceptable bias or behavior changes?

03

Hallucination risk management

1. Q01What specific risk profiling methodologies do you employ to measure and track hallucination rates across different conversation types?
2. Q02Can you provide quantitative metrics on hallucination frequency broken down by domain (medical, financial, legal)?
3. Q03How do you detect and prevent hallucinations that might not be caught by the LLM's own confidence scoring?
4. Q04What is your baseline hallucination rate and how do you ensure it doesn't degrade with model updates?

04

Non-LLM verification systems

1. Q01What deterministic, rule-based systems verify that AI responses comply with regulatory requirements before delivery?
2. Q02Do you employ any non-AI guardrails (regex patterns, keyword filters, structured validation) to catch problematic outputs?
3. Q03How do you verify numerical accuracy and factual claims without relying solely on the language model?
4. Q04Can you demonstrate a multi-layer verification architecture that doesn't depend on LLM self-assessment?

05

Performance guarantees

1. Q01Can you contractually guarantee sub-second response times regardless of any third-party provider's traffic levels?
2. Q02During a third-party service outage, how do you maintain service continuity?

06

Security and compliance verification

1. Q01Can you provide evidence that our voice data is never used to train models, including at third-party providers?
2. Q02How do you ensure HIPAA compliance when patient voice data might contain protected health information?

07

Cost predictability and transparency

1. Q01Can you provide a fixed-cost model that doesn't fluctuate based on third-party API pricing changes?
2. Q02What hidden costs might emerge as we scale to millions of calls monthly?

08

Infrastructure control

1. Q01Can you deploy the entire solution within our private cloud or on-premises data center?
2. Q02How quickly can you implement custom security controls or encryption methods we require?

09

Third-party dependency risks

1. Q01What is your disaster recovery plan if a critical AI provider permanently shuts down?
2. Q02How do you handle situations where third-party providers' values or actions conflict with our corporate policies?

10

Intellectual property and differentiation

1. Q01How can we build proprietary conversational experiences if we're using the same base model as every other enterprise customer?
2. Q02Can you fine-tune models exclusively for our use case, ensuring competitors cannot access our optimizations?
3. Q03What prevents another company from replicating our exact conversational agent if they use the same third-party APIs?
4. Q04Do you offer exclusive voice actor licensing so our brand voice cannot be used by competitors?
5. Q05How do you ensure our training data and conversation patterns remain our intellectual property and don't improve models used by others?

11

Technical model optimization and hyperparameters

1. Q01For LoRA fine-tuning: what are the specific Alpha and R values, how many parameters were unfrozen, which optimizer was employed?
2. Q02Can you dynamically prune model parameters to optimize for our specific latency requirements without full retraining?
3. Q03What is your approach to catastrophic forgetting when fine-tuning?
4. Q04Do you use elastic weight consolidation or other regularization techniques?
5. Q05Can you add task-specific parameters or adapter layers without affecting the base model performance?
6. Q06What are the exact learning rate schedules, batch sizes, and gradient accumulation steps used in your training pipeline?
7. Q07Do you support quantization-aware training, and at what bit precision (INT8, INT4) can models run while maintaining accuracy?
8. Q08What mixture-of-experts routing mechanisms do you employ, and can the gating network be adjusted for our use case?
9. Q09How do you handle gradient checkpointing and memory optimization for large-scale model deployments?
10. Q10Can you implement custom attention mechanisms or positional encodings specific to our conversational patterns?