What to ask before you choose a voice AI or CX automation platform

Today’s companies increasingly turn to AI to reduce costs and improve the customer experience. But with so many vendors on the market that claim to be “AI-powered” and “enterprise-ready,” it’s challenging to determine which solution can actually deliver on its promises.

Choosing the right platform can streamline your operations, improve customer satisfaction scores, and drive measurable ROI, while the wrong choice can result in disrupted operations, integration issues, and frustrated customers.

That’s why we’ve put together this checklist of essential questions to ask before you choose a voice AI or CX solution. Use it to evaluate your options so you can find the platform that’ll provide scalability, security, and performance.
Before you begin, check if you’re ready

Before you even shortlist vendors, make sure your organization is ready for AI agents by addressing the following questions:

• Can your CCaaS, CRM, and backend systems talk to each other in real time via APIs?

• Are security, legal, and compliance stakeholders involved from the design phase?

• Have you defined clear use cases and measurable success metrics?

• Do CX, IT, and operations teams share ownership post-launch?

Look at scalability and flexibility

A scalable platform grows with your business and can withstand real-world variability, such as traffic spikes, multilingual interactions, and complex integrations.

• Is the platform cloud-native and API-first?

• Can it support multi-region deployments with localization, high concurrency, and uptime SLAs?

• Does it handle version control and rollback without service interruption?

• How easily does it integrate with your existing tech stack?

• Can non-technical users build agents and update conversation flows without code?

Consider security and compliance

Your AI systems will handle customers’ personal information, so you need to ensure that security and compliance are built in.

• What security and compliance certifications (e.g., SOC 2, HIPAA, GDPR) does the vendor’s integration framework meet?

• What encryption standards are used for data (AES-256, TLS 1.2+)?

• Is customer data isolated in a multi-tenant environment?

• Does the system support role-based access control (RBAC), single sign-on (SSO), and multifactor authentication (MFA)?

• Where is data stored and can you opt for regional hosting for compliance reasons?

• How are audit trails and data retention policies managed?

Assess performance and real-world reliability

Performance isn’t just about a platform’s ability to take calls and answer customer questions — it’s about how customers feel about their experience with it.

• What is the average response latency in live environments?

• How does the platform maintain low latency during peak usage?

• How does it manage accents, noise, and emotional tone in speech AI?

• Is there real-time monitoring for sentiment, containment, and escalation?

• How are models retrained or optimized without downtime?

• Are monitoring and reporting tools included to measure performance?

• Can AI agents read/write to CRMs or ticketing systems in real time?

• Can multiple AI agents coordinate across workflows?

• Can agents “remember” past customer conversations and act accordingly?

• Are multilingual conversations localized — not just translated?

Ask about testing, simulation, and continuous improvement

Many AI deployments fail because testing stops at the demo phase. A true enterprise platform enables real-world simulation before going live and continuous evaluation afterward. In other words, AI agents are treated like human ones — they’re trained, monitored, and improved over time.

• Can you simulate live conversations across languages, accents, and edge cases?

• Does the system include hallucination detection, success rate prediction, and multivariate prompt testing?

• How does it handle hallucinations, factual inaccuracies, or unpredictable LLM responses in a voice channel?

• How natural and human-sounding are the AI’s voice interactions?

• Does the system learn from past interactions to improve over time?

• Can it integrate data and knowledge bases for context-aware responses?

• How smoothly does it hand over to human agents when necessary?

• Can it export logs and analytics, tag critical moments, and set alerts for behavior anomalies?

• Are analytics accessible via real-time dashboards and APIs?

Consider partner ecosystem and support

Even the best technology fails without the right expertise and support, so you need a partner that’s invested in your long-term success — not just implementation.

• What does the vendor’s partner ecosystem look like (CCaaS, CRM, cloud, and AI providers)?

• What integrations are available?

• How responsive is their technical support, and what service-level agreements (SLAs) are offered?

• Do they provide onboarding, training, and strategic support beyond technical setup?

• Are there proven customer success stories in your industry?

How Parloa fits the bill

When you apply this checklist, Parloa stands out as a voice AI and CX automation platform that provides enterprise-ready scalability, security, and performance.

Built on a cloud-native architecture, Parloa empowers enterprises to create conversational AI experiences that sound natural, perform at scale, and meet the highest compliance standards. Our AI agents deliver remarkably human-like interactions with ultra-low latency, and our platform is intuitive and easy to use so you can build and scale AI agents — no coding required.

Ready to see how Parloa can transform customer experience and deliver real ROI?