AI Voice Agent RFP Template: 50 Questions to Ask Vendors

Selecting an AI voice agent vendor is a decision that will impact your customer experience, operational efficiency, and bottom line for years. Yet many procurement teams approach this decision with generic software evaluation criteria that miss the specific technical and operational nuances of voice AI.

This guide provides a comprehensive RFP (Request for Proposal) template with 50 questions organized by category. Each question is accompanied by guidance on what a strong answer looks like and red flags to watch for. Use this template as-is or adapt it to your specific requirements.

How to Use This Template

1. Start with your use case: Before sending any RFP, clearly define what you need the AI voice agent to do. Lead qualification? Appointment scheduling? Payment collection? Customer support? The questions that matter most will vary by use case.

2. Prioritize ruthlessly: Not all 50 questions carry equal weight for every organization. Mark each question as "must-have," "important," or "nice-to-have" based on your specific needs.

3. Request demonstrations, not just written answers: Vendors will always write compelling RFP responses. Request live demonstrations of the capabilities that matter most to you, using your actual data and scenarios.

4. Evaluate total cost of ownership: The per-minute rate is just the beginning. Factor in integration costs, training time, ongoing optimization, and the cost of switching vendors later.

5. Include a pilot requirement: Any vendor worth considering should be willing to run a paid pilot of 2-4 weeks before a long-term commitment.

Category 1: Core Technology (Questions 1-10)

Question 1: What is the average end-to-end latency of your voice AI system?

Why it matters: Latency -- the time between when the caller finishes speaking and the AI begins responding -- is the single most important technical metric for voice quality. Anything above 800ms creates noticeable, awkward pauses.

Strong answer: Sub-500ms average latency with P95 under 700ms, with data from production calls (not lab conditions).

Red flag: Vague answers like "low latency" without specific numbers, or latency measured only in lab conditions without real-world telephony delays.

Question 2: Which speech-to-text (STT) and text-to-speech (TTS) engines do you use, and can we choose alternatives?

Why it matters: STT and TTS quality directly impacts conversation accuracy and naturalness. Vendor lock-in to a single engine limits your ability to optimize.

Strong answer: Support for multiple engines (Deepgram, Google, Azure, ElevenLabs, etc.) with the ability to switch without re-engineering the solution.

Red flag: Proprietary STT/TTS with no ability to bring alternatives, or no transparency about which engines are used.

Question 3: Which LLM(s) power the conversation logic, and can we bring our own model?

Why it matters: The LLM determines conversation quality, reasoning ability, and cost. The ability to switch models as the market evolves is valuable.

Strong answer: Support for multiple LLMs (GPT-4o, Claude, Gemini, open-source models) with easy switching. Even better if the vendor can optimize prompts across models.

Red flag: A single, proprietary model with no flexibility, or no transparency about which model is used.

Question 4: How does your system handle interruptions and overlapping speech?

Why it matters: Real conversations involve constant interruptions, false starts, and overlapping speech. Poor interruption handling is the most common reason AI calls feel robotic.

Strong answer: Real-time voice activity detection, ability to stop mid-sentence when interrupted, and graceful recovery after interruption. Request a live demo of this capability.

Red flag: "Our system waits for the caller to finish speaking before responding" -- this means it does not handle interruptions at all.

Question 5: What is your word error rate (WER) for speech recognition, and how does it vary by language and accent?

Why it matters: If the AI cannot accurately understand what the caller says, nothing else matters. WER should be benchmarked for your specific languages and accents.

Strong answer: WER below 5% for primary languages with benchmarks provided for specific accents and dialects relevant to your use case. Even better if they can run a benchmark on your actual call recordings.

Red flag: No WER data, or WER reported only for American English in quiet conditions.

Question 6: How many concurrent calls can your platform handle, and what is the degradation pattern under load?

Why it matters: If you plan to run large campaigns, the platform must handle peak load without increased latency or call failures.

Strong answer: Specific numbers (e.g., "10,000 concurrent calls with no latency degradation") backed by load testing data. Clear explanation of auto-scaling behavior.

Red flag: Vague claims of "unlimited scalability" without data, or reliance on shared infrastructure where other customers' usage affects your performance.

Question 7: What happens when the AI cannot understand the caller or the conversation goes off-script?

Why it matters: Edge cases are inevitable. The system needs graceful fallback behavior.

Strong answer: Configurable fallback behavior: repeat the question, rephrase, ask for clarification, or escalate to a human agent. Clear escalation triggers that you can customize.

Red flag: The AI either loops endlessly or abruptly ends the call when it encounters something unexpected.

Question 8: Does your platform support real-time sentiment or emotion detection?

Why it matters: Detecting caller frustration, anger, or confusion allows the AI to adjust its approach or escalate to a human before the interaction goes poorly.

Strong answer: Real-time sentiment analysis with configurable triggers (e.g., "if frustration detected, offer human transfer"). Demonstrated accuracy in detecting key emotional states.

Red flag: Post-call sentiment analysis only (too late to help during the call) or no sentiment capability at all.

Question 9: How do you handle multi-turn conversations with context retention?

Why it matters: Many voice AI use cases involve multi-turn conversations where the AI needs to remember what was said earlier in the call, or even in previous calls.

Strong answer: Full conversation context is maintained throughout the call. Cross-call memory is available for returning callers. Context window is large enough for your use case (15+ minutes of conversation).

Red flag: Context is lost after each turn, or the AI frequently "forgets" information provided earlier in the same call.

Question 10: What is your system uptime SLA and historical uptime record?

Why it matters: Downtime means missed calls and lost revenue. Voice AI is often customer-facing and time-sensitive.

Strong answer: 99.9%+ uptime SLA with financial penalties for breaches. Historical uptime data for the past 12 months. Public status page with incident history.

Red flag: No formal SLA, no historical data, or a track record of extended outages. Read more about what to expect from AI voice agent SLAs.

Category 2: Language and Voice (Questions 11-17)

Question 11: Which languages do you support, and what is the quality level for each?

Why it matters: "Supported" does not mean "production-quality." A language may be technically available but sound unnatural or have high error rates.

Strong answer: A tiered language list (Tier 1: production-quality with low WER, Tier 2: good quality, Tier 3: beta) with honest quality assessments. Willingness to demonstrate each language you need.

Red flag: A long list of "supported" languages with no quality differentiation.

Question 12: Can the AI handle code-switching (mixing languages within a single conversation)?

Why it matters: In multilingual markets like India, speakers routinely mix languages (Hindi-English, Tamil-English). If your callers do this, the AI must handle it natively.

Strong answer: Native code-switching support for relevant language pairs, demonstrated in a live call.

Red flag: "We support multiple languages but each call must be in a single language."

Question 13: How many voice options are available, and can we create custom voices?

Why it matters: The voice is your brand's representation. It should match your audience's expectations.

Strong answer: Multiple pre-built voices per language with customization options (pace, pitch, tone). Custom voice creation available for enterprise clients.

Question 14: Can the AI adjust its speaking pace, volume, and tone based on the caller's behavior?

Why it matters: Adaptive delivery -- speaking slower to an elderly caller, more directly to an impatient one -- significantly improves engagement.

Strong answer: Dynamic adaptation based on caller speech patterns and detected sentiment.

Question 15: How do you handle background noise on the caller's end?

Why it matters: Callers are often in noisy environments (driving, office, street). The STT must handle background noise without losing accuracy.

Strong answer: Noise suppression and acoustic echo cancellation built into the pipeline, with demonstrated performance in noisy conditions.

Question 16: Can the AI spell out words, read numbers digit-by-digit, or slow down on demand?

Why it matters: When providing phone numbers, email addresses, account numbers, or confirmation codes, the AI must deliver them clearly.

Strong answer: Configurable delivery for alphanumeric content, with automatic slowing for critical information.

Question 17: How natural does the AI sound in extended conversations (10+ minutes)?

Why it matters: Many AI voices sound good for short phrases but become noticeably synthetic over longer conversations due to prosody patterns.

Strong answer: Provide sample recordings of 10+ minute calls. Natural prosody, appropriate emphasis variation, and no "uncanny valley" repetitive patterns.

Category 3: Integration and Data (Questions 18-25)

Question 18: Which CRM platforms do you offer native integrations with?

Why it matters: Bidirectional CRM integration is essential for logging calls, updating lead scores, and triggering workflows.

Strong answer: Native integrations with your CRM (Salesforce, HubSpot, Zoho, etc.) with real-time bidirectional sync. Pre-built field mappings and customizable data flows.

Question 19: Do you support custom API integrations for systems not in your standard connector library?

Why it matters: You likely have custom or industry-specific systems that need to connect.

Strong answer: Well-documented REST API, webhook support, and professional services for custom integrations. SDKs in relevant languages (Python, Node.js, etc.).

Question 20: Can the AI access external data sources during a live call (e.g., look up an order, check inventory)?

Why it matters: Many use cases require real-time data retrieval to answer caller questions accurately.

Strong answer: Function calling or tool-use capability that queries external APIs mid-conversation with minimal latency impact.

Red flag: All knowledge must be pre-loaded and cannot be retrieved dynamically.

Question 21: How are call recordings, transcripts, and metadata stored and accessed?

Why it matters: Call data is essential for quality monitoring, compliance, and analytics.

Strong answer: Recordings and transcripts stored securely with configurable retention periods. API access for export. Dashboard for search and review.

Question 22: What analytics and reporting capabilities are built in?

Why it matters: You need visibility into call volumes, outcomes, conversion rates, and conversation quality.

Strong answer: Real-time dashboards with customizable reports. Conversion funnel tracking. A/B testing capabilities. Exportable data for external BI tools.

Question 23: Can we export all data (recordings, transcripts, metadata, configurations) if we switch providers?

Why it matters: Vendor lock-in is a real risk. You should own your data.

Strong answer: Full data export capability via API or bulk download. Conversation flows and configurations exportable in a standard format.

Red flag: No export capability, or data export requires a special (paid) request.

Question 24: How do you handle data residency requirements (data must stay in a specific country/region)?

Why it matters: GDPR, India's DPDP Act, and other regulations may require data to remain within specific jurisdictions.

Strong answer: Data residency options for major regions (India, EU, US, Middle East) with documentation on where data is processed and stored.

Question 25: What telephony infrastructure do you use, and can we bring our own SIP trunks?

Why it matters: Telephony costs and quality vary significantly. The ability to use your existing telephone infrastructure can reduce costs and improve reliability.

Strong answer: Flexible telephony with support for your own SIP trunks, plus managed options if you prefer simplicity. Major telecom partnerships in your operating regions.

Category 4: Compliance and Security (Questions 26-33)

Question 26: What security certifications does your platform hold?

Why it matters: Industry-standard certifications demonstrate a systematic approach to security.

Strong answer: SOC 2 Type II, ISO 27001 at minimum. HIPAA compliance if healthcare data is involved. PCI DSS if payment data is processed.

Question 27: How do you handle personally identifiable information (PII) in call recordings and transcripts?

Why it matters: PII management is a legal and ethical requirement.

Strong answer: PII detection and redaction in transcripts. Configurable recording policies. Data minimization practices. Clear data retention and deletion policies.

Question 28: Does your platform support DNC (Do Not Call) list management?

Why it matters: Calling numbers on a DNC list carries significant legal and financial penalties.

Strong answer: Built-in DNC management with automatic scrubbing against national and internal DNC lists before any outbound campaign. Real-time DNC checking.

Question 29: How do you handle consent management and call recording disclosures?

Why it matters: Many jurisdictions require informed consent for call recording and AI interaction.

Strong answer: Configurable disclosure statements at the start of calls. Consent logging with timestamps. Different disclosure templates for different jurisdictions.

Question 30: Can the platform enforce calling hour restrictions by time zone and jurisdiction?

Why it matters: TRAI in India, TCPA in the US, and similar regulations restrict when commercial calls can be made.

Strong answer: Automatic calling hour enforcement based on the recipient's time zone and applicable regulations. Configurable windows per jurisdiction. Hard blocks (not just warnings) to prevent out-of-hours calls.

Question 31: How do you handle data breach notification?

Why it matters: You need to know promptly if a breach occurs that affects your data.

Strong answer: Documented breach notification process with specific timeframes (e.g., within 72 hours as required by GDPR). Named security contact. Incident response plan.

Question 32: Is the AI disclosure compliant with emerging AI transparency regulations?

Why it matters: An increasing number of jurisdictions require disclosure when callers are interacting with AI.

Strong answer: Configurable AI disclosure at the start of each call. Awareness of and compliance with current regulations (EU AI Act, US state laws, India's proposed regulations).

Question 33: Can you provide a penetration test report or independent security audit?

Why it matters: Self-reported security is insufficient. Independent validation is the standard.

Strong answer: Recent penetration test report available under NDA. Regular third-party security audits.

Category 5: Pricing and Commercial Terms (Questions 34-40)

Question 34: What is your complete pricing structure, including all fees?

Why it matters: Many vendors advertise a low per-minute rate but add platform fees, telephony charges, LLM costs, and other surcharges.

Strong answer: A clear, comprehensive pricing breakdown showing every cost component. No surprises on the first invoice.

Red flag: Pricing that requires a sales call to understand, or "starting at" language without full transparency.

For a detailed comparison of how major platforms price their services, read the AI Voice Agent Pricing Comparison 2026.

Question 35: What is the minimum commitment (term and volume)?

Why it matters: Long lock-in periods are risky when you are still evaluating the technology.

Strong answer: Month-to-month options available. Volume commitments only for discounted rates, not as a requirement.

Question 36: How are failed calls, voicemails, and unanswered calls billed?

Why it matters: If you are billed per minute for calls that never connect, campaign economics change significantly.

Strong answer: Billing only for connected minutes. Clear definition of what constitutes a "connected" call. No charges for failed connections or busy signals.

Question 37: What are the costs for adding new languages, voices, or integrations?

Why it matters: Your needs will evolve. Understand the cost of expansion before you commit.

Strong answer: Language and voice additions included in the platform. Integration costs clearly stated upfront. No per-language surcharges.

Question 38: Do you offer volume discounts, and what are the tiers?

Why it matters: If your usage will grow, understanding the discount structure helps you forecast costs.

Strong answer: Published volume discount tiers. Automatic tier advancement as usage grows. Retroactive discounts on the full month when you cross a tier.

Question 39: What is the cost of professional services (custom integration, script development, optimization)?

Why it matters: Many implementations require professional services beyond self-service setup. These costs can be substantial.

Strong answer: Published professional services rates. Scope and deliverables for common service packages. Option to use your own developers instead.

Question 40: What are the contract exit terms?

Why it matters: You need to know how to leave if the platform does not work out.

Strong answer: 30-day notice for month-to-month. Clear data export provisions. No early termination penalties beyond the current billing period.

Category 6: Implementation and Support (Questions 41-47)

Question 41: What is the typical time from contract signing to first production call?

Why it matters: Time to value matters. A platform that takes three months to deploy has a very different ROI profile than one that goes live in two weeks.

Strong answer: Self-service setup in days for simple use cases. 2-4 weeks for complex implementations with CRM integration. Dedicated implementation manager for enterprise deployments.

Question 42: What training and onboarding resources do you provide?

Why it matters: Your team needs to be able to use the platform effectively.

Strong answer: Documentation, video tutorials, live training sessions, and a dedicated onboarding specialist for enterprise accounts.

Question 43: What does ongoing support look like (channels, hours, SLA)?

Why it matters: Issues will arise. Response time and support quality matter.

Strong answer: Multiple support channels (email, chat, phone). Published response time SLAs by severity. Named account manager for enterprise clients. 24/7 support for critical issues.

Question 44: Do you offer conversation design and script optimization services?

Why it matters: Designing effective AI voice agent conversations is a specialized skill. Most businesses benefit from expert guidance.

Strong answer: In-house conversation design team. Script optimization based on call data and A/B testing results. Regular review cadence included in enterprise plans.

Question 45: How do you handle platform updates and new feature releases?

Why it matters: Voice AI is a rapidly evolving field. You want a vendor that keeps improving.

Strong answer: Regular release cadence (monthly or quarterly). Release notes. Opt-in for major changes. Non-breaking updates deployed automatically.

Question 46: Can we run a paid pilot before committing to a long-term contract?

Why it matters: A pilot with real data is the only reliable way to evaluate platform performance.

Strong answer: Standard pilot program of 2-4 weeks with agreed success criteria. Pilot cost credited toward the contract if you proceed.

Red flag: Refusal to offer a pilot, or insistence on a long-term commitment before any testing.

Question 47: What is your product roadmap for the next 12 months?

Why it matters: You want a vendor that is investing in the capabilities that matter to your future needs.

Strong answer: Willingness to share the roadmap under NDA. Clear investment in areas relevant to your use case. Track record of delivering on roadmap commitments.

Category 7: References and Track Record (Questions 48-50)

Question 48: Can you provide three reference customers in our industry with similar use cases?

Why it matters: Nothing validates a vendor like feedback from customers who have already walked the path you are considering.

Strong answer: Willingness to connect you with reference customers who can speak candidly about their experience, including challenges they encountered.

Red flag: No references available, or references only from very different industries or use cases.

Question 49: What is your customer retention rate and average contract duration?

Why it matters: High churn suggests customer dissatisfaction. Long contract durations suggest customers are getting value.

Strong answer: Retention rate above 90%. Average contract duration of 12+ months. Willingness to discuss why customers who left chose to do so.

Question 50: How many production minutes does your platform handle per month, and what is the growth trend?

Why it matters: Volume is a proxy for market validation and platform maturity. Rapidly growing volume indicates strong product-market fit.

Strong answer: Millions of minutes per month with strong month-over-month growth. Transparency about the split between inbound and outbound, and across industries.

Scoring Template

Use this scoring matrix to evaluate vendor responses objectively:

Adjust the weights based on your priorities. For regulated industries, increase the compliance weight. For budget-constrained deployments, increase the pricing weight. For global deployments, increase the language weight.

Additional Evaluation Steps

Beyond the RFP, take these steps before making a final decision:

1. Live Demo with Your Scenarios

Ask each vendor to demonstrate their platform using your actual use case, your data, and your scripts. A generic demo tells you very little about how the platform will perform for your specific needs.

2. Call Quality Blind Test

Have each vendor make test calls to your team members without revealing which vendor is calling. Rate the calls on naturalness, accuracy, and overall experience. This eliminates brand bias from the evaluation.

3. Technical Deep Dive

If you have a technical team, schedule a session with the vendor's engineering team to discuss architecture, scalability, reliability, and security in detail. Sales teams often cannot answer these questions accurately.

4. Contract Review

Have your legal team review the contract carefully, paying special attention to: data ownership, liability for AI errors, indemnification, and termination provisions.

Conclusion

Evaluating AI voice agent vendors is a significant undertaking, but the investment in a thorough evaluation process pays dividends. The difference between the right vendor and the wrong one manifests in call quality, customer satisfaction, conversion rates, and operational efficiency -- every day, on every call.

Use this template as your starting point. Customize it for your specific industry, use case, and requirements. And remember that the best vendor for your needs today may not be the best vendor in 18 months -- so prioritize flexibility and data portability alongside performance and price.

For a comparison of the leading platforms in the market today, see the Top 7 AI Voice Agent Platforms for Outbound Sales and the AI Voice Agent Pricing Comparison 2026. To evaluate Edesy specifically against your requirements, book a demo or explore the features page.