# How to Choose an Enterprise Voice AI Platform: 11 Evaluation Criteria - Source: https://www.cbot.ai/how-to-choose-enterprise-voice-ai-platform/ Choose an enterprise Voice AI platform by its ability to bring each call to a business outcome. Evaluate voice quality on your own call samples, telephony and enterprise integration, transaction completion, human handover, deployment model and data residency, governance, analytics, pilot design, and cost together. Choosing an enterprise Voice AI platform is less about how natural the voice sounds and more about which business outcome each call reaches. The right platform understands customers in their own words, connects to your existing telephony and enterprise systems, completes the transaction within defined permissions, hands the call to a human with full context when needed, and keeps data inside the boundary you set. ## Where should the evaluation start? A call that looks good in a demo can behave differently under real traffic. Start from the business problem, not the technology. Write down which call type you want to automate, how that call is resolved today, and how you will measure success. Inbound calls (information and status requests, claims notification, identity verification) and outbound calls (payment reminders, collections, appointments) create different requirements. A sound evaluation team brings together the business unit, contact-center operations, IT, information security, and legal. ## Evaluation criteria ### 1. Business outcome and use case Assess the platform against the end-to-end flow of your chosen scenario rather than a generic feature list. What happens at the start of the call, which data is verified, what changes in which system, and with what outcome does the customer hang up? Ask the vendor to demonstrate that flow with your own business rules. ### 2. Language and voice quality Speech recognition accuracy varies with line quality, background noise, accents, and industry terminology. Test with your own anonymized call samples instead of curated demo recordings. Check that brand and product names, amounts, dates, and abbreviations are recognized and spoken correctly, and ask how terminology and pronunciation are customized. ### 3. Integration Voice AI is only as useful as the systems it connects to. Clarify how the platform connects to telephony and SIP infrastructure, your existing IVR, the contact-center platform, CRM, and core systems, and whether any of that infrastructure has to be rebuilt. ### 4. Ability to act A system that answers questions and a system that completes transactions are very different things. Verify whether the platform can authenticate callers, create records, update systems, and trigger workflows within defined permissions. ### 5. Human handover Not every call ends in automation. Look at what reaches the human agent at the moment of transfer. If intent, collected data, transcript, and reason for escalation are not passed on, the customer has to start over. ### 6. Deployment model and data residency Call recordings, transcripts, and model outputs are sensitive data. Ask about cloud, private cloud, hybrid, and on-premise options, where data is processed and stored, and whether the platform depends on an external model service. ### 7. Security and governance Role-based access, separate read and write permissions, human approval for sensitive actions, version control, rollback, and audit trails are baseline expectations in regulated industries. ### 8. Measurement and analytics Continuous improvement requires visibility into completion, escalation, fallback, and per-intent performance, along with the ability to review individual calls. ### 9. Pilot design Run the pilot on a narrow but real scenario. Put success criteria, the measurement period, handover rules, and out-of-scope cases in writing before you start, so the final decision rests on those criteria rather than on demo impressions. ### 10. Cost model Request license, usage, implementation, integration, and infrastructure costs as separate line items, including hardware for on-premise deployment. Clarify how costs change as volume grows and who owns ongoing maintenance. ### 11. References Ask for live voice-channel deployments in a similar industry and at a similar scale. Evaluate chat experience and phone-channel experience separately. ## Checklist - Are the target call type and success metric written down? - Is testing done with your own call samples? - Are industry terms, brand names, and amounts recognized and spoken correctly? - Is integration with SIP, IVR, CRM, and core systems clearly defined? - Does the platform complete the transaction or only provide information? - What context is passed on during human handover? - Where is data processed and where is it stored? - Are role-based access, human approval, version history, and audit trails in place? - Which metrics are tracked, and can individual calls be reviewed? - Are pilot scope, duration, and exit criteria defined? - Are cost items visible separately? - Are there live voice-channel references? For a step-by-step roadmap from design to go-live, see the [Voice AI Agent playbook](/resources/playbooks/voiceaiplaybook/). ## How CBOT addresses these criteria **Speech technology.** CBOT develops its Speech-to-Text and Text-to-Speech technologies in-house under CBOT Speech. Speech recognition is built for contact-center and mobile audio, ambient noise, domain terminology, and Turkish language structure. Domain vocabulary, brand names, and product names are supported through custom dictionaries, model adaptation, and pronunciation configuration. Details are on the [Voice AI page](/cbot-speech-overview/). **Integration and action.** CBOT connects Voice AI Agents to telephony and SIP infrastructure, IVR environments, contact-center platforms, CRM, and core banking and insurance systems through approved integrations, APIs, and custom interfaces. Within defined permissions and business rules, the agents retrieve information, collect and validate data, update systems, and execute workflows. At Halkbank, CBOT's speech recognition and text-to-speech technologies are integrated into the bank's IVR system and used in the Call Center. Connection methods are described on the [Integrations page](/integrations/). **Human handover.** When human support is required, CBOT transfers the call with the identified intent, collected information, full transcript, completed steps, escalation reason, and an AI-generated summary. Warm transfer, silent transfer, and collaborative mode are supported. **Deployment and data.** CBOT supports SaaS, private-cloud, hybrid, and fully on-premise deployment. In a fully on-premise architecture, speech recognition, text-to-speech, model inference, RAG, analytics, and integrations run in the customer's environment. Organizations can use CBOT models, supported open-source models, or customer-hosted models without depending on an external commercial model API. See [Deployment Options](/deployment-options/). **Governance.** CBOT combines role-based access, model and knowledge permissions, tool restrictions, human approvals, version control, and auditability. Read and write permissions can be separated, and prompts, workflows, and AI agent configurations can be rolled back to a previously approved version. See [AI Governance and Safety](/ai-governance-safety/). **Measurement.** CBOT Vision shows call volume, completion and containment, fallback, escalation, and intent performance in real time and historically, and authorized users can review individual calls. The Digital Employee KALYA monitors defined metrics and prepares regular reports. See [Analytics and Observability](/analytics-observability/). **Pilot and go-live.** Timelines depend on the use case, integrations, conversation complexity, deployment architecture, security requirements, and testing scope. The implementation plan is defined after a solution and infrastructure assessment. ## Final word A good choice rests on results measured in your own scenario, not on a demo. If you want to test this checklist against your own call processes, [talk to the CBOT team](/contact-us/). ## FAQ ### How is a Voice AI platform different from a traditional IVR? A traditional IVR asks callers to navigate fixed keypad menus. A Voice AI platform lets callers describe what they need in their own words and determines the right information, transaction, or person to route to. Many organizations deploy Voice AI on top of their existing IVR infrastructure. ### Which use case should a Voice AI pilot start with? A high-volume call type with clear rules and measurable outcomes works well. Status inquiries, appointment changes, and payment reminders are common choices. Out-of-scope cases and handover rules should be defined before the pilot begins. ### Does on-premise Voice AI require GPU infrastructure? GPU requirements depend on the selected models and workloads. Language model and some speech workloads may need GPUs, while other services can run on CPU. Exact sizing is completed during solution design. ### Can a Voice AI platform eliminate every incorrect answer? No enterprise AI platform can guarantee the elimination of every incorrect or unsupported model output. Risk is reduced and managed through grounded knowledge, defined boundaries, validation steps, confidence thresholds, and human review. ### Is a chat-channel reference enough evidence for a voice deployment? Not entirely. The phone channel brings requirements that chat does not have, such as speech recognition, latency, background noise, and real-time handover to a human. It is worth asking separately for a live voice-channel deployment.