Conversation intelligence RFP: 20 questions for operational scale

A successful conversation intelligence (CI) RFP must move beyond surface-level feature checklists to interrogate the vendor’s data architecture, model transparency, and operational scalability. The goal is to identify platforms that can analyze 100% of interactions with high accuracy rather than those that only provide highlights from a small sample. To find a partner that survives the transition from pilot to production, your RFP must force vendors to explain the mechanics behind their claims.

Key Takeaways

• Validate the 'How' Over the 'What': Do not ask if a vendor has AI; ask how their models are trained and how they handle domain-specific jargon.
• Prioritize 100% Coverage: True CI value comes from analyzing every call for compliance and sentiment, not just a random sample.
• Demand Integration Depth: A CI platform is only as useful as its ability to pass data to your CCaaS and CRM systems.
• Focus on Compliance: Ensure the vendor has native PII redaction and data residency controls that meet your industry's specific standards.

The Architecture and Accuracy Gap

Most CI demos look identical because they use the same generic underlying models from providers like Google Cloud or AWS. The difference between a tool and a solution lies in how that raw data is refined. According to Gartner’s Customer Service & Support practice, the maturity of support technologies depends heavily on domain-specific AI and data protection. If a vendor cannot explain how they reduce Word Error Rate (WER) for your specific industry terms, the insights will likely be unreliable.

1. How does the platform handle industry-specific terminology and acronyms? General models often struggle with technical jargon or product names. Look for vendors that allow for custom vocabulary or fine-tuning without requiring a six-month professional services engagement.

2. What is your documented Word Error Rate (WER) for noisy environments? Contact center audio is rarely pristine. Ask for WER benchmarks specifically on compressed telephony audio, not just high-fidelity recordings.

3. Does the platform support multi-speaker diarization for all channels? Knowing who said what is the difference between measuring agent performance and customer frustration. The platform must accurately distinguish between the agent, the customer, and any automated IVR systems.

4. Is transcription performed in real-time, post-call, or both? Real-time is necessary for live agent coaching, while post-call is often more accurate for deep analytics and QA. Understand the latency trade-offs for each.

Moving Beyond Manual QA

Traditional QA programs often only review 1-2% of calls, leaving a massive blind spot for compliance and customer experience. Forrester’s Customer Experience practice emphasizes that tracking how customers rate their experiences across brands requires a comprehensive view of every touchpoint. When moving from manual processes, as discussed in our Conversation Intelligence Buyer’s Guide: Moving Beyond Manual QA, the RFP must address how the machine handles the heavy lifting.

5. Can the platform automate 100% of the existing QA scorecard? Ask the vendor to demonstrate how they map qualitative questions (e.g., "Did the agent show empathy?") to quantitative data points.

6. How does the system flag compliance violations or high-risk behavior? Teams often pair a CCaaS platform like Five9 or Genesys with a specialized conversation intelligence layer such as Hear.ai (powers our network) to ensure every conversation is analyzed for compliance risk and QA coverage. This prevents the