A/B testing compares two different versions of a process, such as call scripts or voice prompts, to see which performs better. It helps teams make data-driven decisions by analyzing user responses, conversion rates, or engagement levels. In voice calls, it can improve call flow design, agent scripts, or AI voice interactions.

An AI agent is a voice bot that can handle inbound or outbound calls using natural language. It simulates human conversation, understands customer intent, responds with helpful information, and performs tasks like booking appointments or qualifying leads. AI agents reduce workload on human teams and improve call efficiency.

An AI copilot is a digital assistant that supports human agents during live calls or workflows. It can surface data, suggest answers, transcribe conversations, or detect intent in real time. Rather than replacing humans, the AI copilot enhances productivity and consistency across customer interactions.

API integration allows your voice calling platform to connect and communicate with other software systems like CRMs, scheduling tools, or databases. It enables data sharing, automation, and customization, so you can tailor the call experience, update records automatically, or trigger workflows based on real-time events.

ASR converts spoken language into written text in real-time. It’s a core component of AI voice systems that allows platforms to understand what callers are saying, respond accordingly, or generate transcriptions. High-quality ASR improves accuracy, responsiveness, and the overall natural feel of automated calls.

An abandoned call happens when a caller disconnects before reaching an agent or AI assistant. This is often due to long wait times, unclear routing, or frustration. High abandoned call rates can indicate issues with call handling or system capacity. Reducing this metric is important for improving customer satisfaction and operational efficiency.

The accuracy rate measures how often an AI system correctly understands or responds to input. In voice platforms, this often refers to how precisely the system recognizes speech, classifies intents, or routes calls. A higher accuracy rate leads to better user experience and more reliable automation.

Active listening is when a human or AI caller pays close attention to what the other person is saying and responds in a thoughtful, relevant way. For AI systems, it involves real-time understanding, tone detection, and context awareness to make conversations feel natural and personalized.

Real-time AI support provided to human agents during live calls.

Agent assistance tools support human agents during live calls by offering real-time suggestions, pulling up relevant data, or automating tasks like note-taking. These tools help agents resolve issues faster, stay focused, and deliver more consistent service, especially in complex or high-volume call environments.

Ambient intelligence refers to AI that operates quietly in the background, sensing and responding to people’s needs without direct input. In voice platforms, this could include automatically adjusting call routing, detecting stress in a caller’s voice, or summarizing conversations—all without interrupting the flow of communication.

An analytics dashboard is a centralized interface that displays real-time and historical data from voice calls. It can include call volumes, agent performance, conversion rates, sentiment scores, and more. These dashboards help teams monitor operations, identify trends, and make informed decisions quickly.

AMD is a feature that detects whether a call has been answered by a human or a voicemail system. This allows the platform to decide whether to connect the call, leave a message, or end it. Effective AMD improves efficiency in outbound calling campaigns and reduces time wasted on voicemails.

AGI refers to highly advanced AI with the ability to understand, learn, and apply knowledge across a wide range of tasks—much like a human. Unlike task-specific AI, AGI could perform open-ended reasoning and handle unpredictable scenarios. This is still a future concept and has not been used yet in practical applications.

AI is the technology that enables machines to simulate human intelligence. In voice calling platforms, AI powers features like voice recognition, intent detection, conversation flow, and learning from past interactions. It helps automate tasks, personalize communication, and deliver more natural, efficient call experiences.

Authentication verifies a caller's identity before allowing access to sensitive information or services. It can be done using PIN codes, voiceprints, one-time passwords, or two-factor methods. Strong authentication helps protect both businesses and users from fraud, especially in industries like finance and healthcare.

An auto dialer is a software that automatically dials a list of phone numbers and connects answered calls to either a human agent or an AI voice bot. It saves time and increases productivity, especially in outbound campaigns like lead generation, surveys, or appointment reminders.

Automated outbound calling uses AI or software to initiate and manage outgoing calls without human agents. It can deliver reminders, follow-ups, surveys, or promotional messages. With personalization and intent detection, these calls feel conversational and efficient, making it easier to scale customer outreach.

Benchmarking is the practice of comparing your system’s performance—such as call success rates, response time, or engagement levels—against industry standards or past performance. It helps identify what’s working, what needs improvement, and how your AI calling platform stacks up to competitors. Regular benchmarking supports better goal-setting, performance tracking, and continuous improvement of call operations.

Call abandonment rate is the percentage of callers who hang up before speaking with a live agent or AI assistant. It often reflects long wait times or poor call routing. A high abandonment rate can hurt customer satisfaction and lead to missed opportunities. Reducing it requires optimizing IVR flows, staffing, or AI responsiveness to keep callers engaged.

Call analytics involves collecting and analyzing data from voice calls to gain insights into customer behavior, call outcomes, agent performance, and system efficiency. It can track metrics like duration, sentiment, intent, and conversion rates. These insights help businesses improve call strategies, personalize interactions, and identify trends or pain points in the customer journey.

Using technology to make or handle calls without human intervention.

Call deflection is when a caller is guided away from a live agent to a self-service option like an AI voice assistant or chatbot. It’s used to reduce agent workload, speed up issue resolution, and lower support costs. Effective deflection still ensures the caller’s issue is addressed without compromising experience.

A Call Detail Record is a log of metadata for each call handled by the system. It includes information such as call time, duration, direction, caller ID, and call result. CDRs are used for performance monitoring, compliance tracking, billing, and analytics. They provide a detailed trail of every interaction handled.

Call disposition refers to the outcome or status assigned to a call after it ends. Examples include “answered,” “voicemail left,” “no answer,” or “lead converted.” Dispositions help categorize calls for reporting, trigger follow-up actions, and track the effectiveness of outbound campaigns or customer service workflows.

Call flow is the step-by-step path a caller takes through a voice interaction system. It includes prompts, routing rules, AI responses, transfers, and resolutions. A well-designed call flow makes the experience intuitive and efficient, guiding users to the right information or department without frustration or dead ends.

Call forwarding automatically redirects incoming calls to another number or destination. It’s used to ensure no call is missed during agent unavailability, outside business hours, or high-volume periods. The system can forward calls to a human, voicemail, or another AI bot, depending on the rules you set.

Call penetration Rate

Call quality monitoring is the process of reviewing voice interactions to assess clarity, compliance, sentiment, and overall performance. It can be manual or automated using AI to detect issues like poor audio, inappropriate responses, or long silences. Monitoring helps improve training, compliance, and customer satisfaction.

Call recording captures and stores voice calls for quality assurance, training, compliance, or analysis. In AI-powered systems, these recordings can also be transcribed and used to improve bot performance. Proper consent and data handling are essential when recording calls, especially in regulated industries.

Call routing is the logic that directs incoming calls to the right agent, department, or AI bot. Routing can be based on factors like time, caller input, location, or customer type. Intelligent routing ensures faster resolutions, less frustration, and more personalized service.

Call scoring is the practice of rating calls based on factors like quality, engagement, and adherence to guidelines. AI can score calls automatically by analyzing speech, tone, and keywords. It helps teams evaluate agent or bot performance and identify training needs or system improvements.

Call scripting provides predefined conversation guides that human agents or AI bots follow during calls. It ensures consistency, compliance, and smoother conversations, especially for sales, support, or survey calls. Dynamic scripts can adapt to caller responses to keep the interaction feeling natural.

Call surge management involves handling sudden spikes in call volume, whether due to marketing campaigns, emergencies, or seasonal demand. AI voice systems can absorb surges by automating responses and prioritizing urgent calls. Proper planning helps maintain service quality even during high-traffic periods.

Call transcription converts spoken words from a voice call into written text. It enables better analysis, training, compliance, and accessibility. AI-driven transcription tools work in real time and can highlight key terms or phrases to improve recordkeeping and follow-up actions

Caller ID displays the phone number or name of the person making the call. Businesses can use custom caller IDs to show branded numbers, improving pickup rates and trust. AI platforms may also analyze caller ID data to help route or prioritize calls intelligently.

Technology that enables machines to simulate natural human conversations.

Data enrichment is the process of enhancing raw customer or caller data with additional details from internal or external sources. For example, a phone number might be enriched with a name, location, or job title. In voice AI, this helps personalize conversations, improve routing, and increase conversion rates by giving the system more context to work with.