Speech analytics is the automated examination of spoken conversations to uncover patterns, trends, or emotions. It’s used to evaluate agent performance, monitor compliance, and improve customer engagement by providing actionable insights from every call.

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.

Batch calling refers to the process of making a large number of outbound calls in groups or "batches" using a predefined contact list. It’s often used in sales, marketing, or surveys to reach many people quickly. The platform can schedule, track, and automate these calls, allowing teams to manage campaigns more efficiently while reducing manual work and ensuring consistent messaging across all contacts.

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.

Biometric authentication uses unique physical traits—like voice, fingerprint, or facial features—to verify identity. In voice platforms, voice biometrics can identify a caller based on how they speak. It offers a secure, frictionless alternative to passwords or PINs, especially useful for banking, healthcare, or any service involving sensitive information.

Blacklist management involves maintaining and updating a list of phone numbers that should not be contacted. This could be due to user opt-outs, regulatory restrictions, or prior negative interactions. Proper blacklist management ensures compliance with privacy laws like Do Not Call (DNC) rules and helps avoid spam complaints and penalties.

A blended call center is equipped to handle both inbound and outbound calls using the same agents or AI system. This setup increases efficiency by automatically adjusting to call volume. When inbound calls are low, outbound calls are prioritized, and vice versa. It’s ideal for businesses wanting to maximize agent or system utilization.

A bot framework is a development toolkit used to create, train, and deploy AI voice bots. It provides the core building blocks like natural language processing, conversation flow design, and integration tools. A strong framework makes it easier to customize bots for different industries or use cases such as lead qualification, appointment booking, or customer support.

Bot training is the process of teaching an AI voice bot how to understand language, respond appropriately, and handle different conversation scenarios. This involves feeding it data, defining intents, labeling responses, and testing for accuracy. Continuous training ensures the bot evolves with user behavior and maintains high-quality interactions over time.

A bridged call occurs when the voice platform connects two separate calls—usually an initial caller and a second party like a sales rep or service provider. It’s common in warm transfer scenarios where an AI handles the first part of the conversation and then hands it off seamlessly to a human.

Business Intelligence refers to the tools and systems that collect, analyze, and visualize data to help companies make informed decisions. In voice calling, BI dashboards might show call volume trends, user sentiment, agent efficiency, and campaign outcomes. This data helps businesses optimize performance and align voice operations with larger goals.

A business rules engine is a system that applies predefined rules to guide decision-making during a call. For example, it can automatically route calls based on location, time, or customer status. It ensures consistent responses, enforces company policies, and lets teams update logic quickly without needing to reprogram the system.

CMS integration connects your voice platform with a Content Management System (like WordPress or headless CMS tools). It allows the AI to fetch or update content, such as FAQs, appointment slots, or promotions, in real time during a call. This ensures users always get current, relevant information.

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

A callback queue holds the information of callers who request a return call instead of waiting on hold. The system calls them back automatically when an agent or bot becomes available. It improves the caller’s experience by saving time and reducing frustration during busy periods.

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.

Chatbot integration connects your AI voice platform with web or messaging-based bots. This allows users to move smoothly between channels—such as from a phone call to a chat window—while preserving context. It creates a more unified and responsive customer experience across touchpoints.

Cold calling automation uses AI and auto-dialers to reach potential customers who haven’t interacted with your business before. It handles repetitive tasks like dialing, greeting, and qualifying leads, letting human teams focus on high-intent prospects. It’s especially useful for sales teams working on large lead lists.

Compliance management ensures your voice platform follows laws and regulations like GDPR, HIPAA, or DNC. It includes data handling, consent recording, secure storage, and call scripting rules. Strong compliance practices build trust, reduce risk, and protect your business from legal and financial penalties.

Computational linguistics is the study of how computers understand and generate human language. In voice AI, it helps improve natural language processing, speech recognition, and dialogue systems. It enables bots to understand meaning, context, and tone better, making conversations smoother and more human-like.

Conditional logic is the set of “if-this-then-that” rules that determine how a voice call progresses. For example, if a caller presses 1, the call goes to support; if they say “billing,” it routes to finance. It powers intelligent decision-making and flexible conversation flows within your voice platform.

A confidence score indicates how sure the AI is about what a caller said or meant. For example, if someone says “schedule appointment,” the AI may assign an 85% confidence score to the intent “Book Appointment.” Higher scores mean more accurate understanding, helping bots respond appropriately or ask for clarification.

Contact center automation uses AI and software tools to streamline tasks like call routing, responses, data entry, and follow-ups. It reduces the load on human agents, shortens resolution time, and improves consistency. Automation allows contact centers to scale operations without sacrificing quality.

Context preservation means the AI remembers key details from earlier in the conversation—or even past interactions—to keep the experience coherent. For example, if a caller already gave their name, the bot won’t ask again. It helps maintain flow and makes conversations feel smarter and more personal.

Contextual understanding allows AI to interpret not just what is said but also what is meant based on the situation. This involves tone, past interactions, and current intent. It helps the system respond more naturally and avoid mistakes, especially in conversations that require empathy or clarification.

Conversation design is the process of creating natural, effective, and goal-oriented dialogues between users and AI voice bots. It involves scripting, tone setting, branching logic, and fallback strategies. Good design ensures that users feel heard and supported while achieving their purpose quickly.

Conversational AI is the technology behind bots that interact with people using natural language. It includes voice recognition, natural language processing, and real-time responses. These systems simulate human-like conversation to help with customer service, sales, or support, reducing the need for live agents.

Conversational analytics extracts insights from voice interactions. It looks at intent, sentiment, duration, keywords, and outcomes to understand what’s working and what isn’t. These insights help improve scripts, train agents or bots, and refine customer experience over time.

Conversational flow is the logical and emotional progression of a voice interaction. It’s how well the conversation moves from greeting to resolution. A smooth flow keeps users engaged, reduces confusion, and improves outcomes. Well-structured flows consider tone, timing, context, and fallback paths.

Conversational intelligence uses AI to analyze voice conversations for meaning, intent, sentiment, and performance. It gives businesses deep insights into customer needs, agent effectiveness, and trends. This intelligence can drive smarter decisions, improved training, and better overall communication strategies.

Conversion rate measures how many calls result in a desired action—like booking an appointment, making a sale, or submitting information. It’s a key performance metric in outbound calling campaigns. Improving this rate means making conversations more persuasive, relevant, and timely.

Custom caller ID allows businesses to display a specific number or name when making outbound calls. It can improve answer rates and build trust. For example, showing “Clinic Line” instead of an unknown number helps people recognize the caller and reduces the chances of being ignored or blocked.

Custom voice refers to a uniquely branded or synthesized voice used by your AI calling system. It reflects your company’s tone and personality. A recognizable custom voice helps build brand identity and makes conversations feel more familiar and consistent to the user.

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.

Data labeling involves tagging pieces of data—such as voice recordings or text transcripts—with specific identifiers like intent, sentiment, or speaker role. These labels are crucial for training AI models to recognize patterns, understand natural language, and make accurate predictions. High-quality labeled data is key to improving performance and reliability.

Data mining is the practice of analyzing large datasets to discover patterns, trends, or insights. In the context of voice AI, this could mean identifying which types of calls lead to conversions or what phrases indicate dissatisfaction. It helps businesses make smarter decisions based on actual customer behavior.

Data privacy refers to how personal and sensitive data is collected, used, stored, and protected. In voice platforms, this includes call recordings, transcripts, and caller information. Privacy policies and technical safeguards must follow legal standards like GDPR or HIPAA to ensure user trust and compliance.

Data residency specifies where your data is physically stored—usually to meet legal or regulatory requirements. For example, some industries or countries require data to remain within their borders. Voice platforms with flexible data residency options allow organizations to comply with local rules without compromising performance.

A data retention policy defines how long data—such as call recordings, transcripts, or user logs—should be stored before being deleted. It ensures businesses balance operational needs with legal compliance and privacy obligations. Clear policies help avoid unnecessary storage costs and reduce exposure to data breaches.

Deep learning is a type of machine learning where algorithms use layered neural networks to learn from large datasets. In voice AI, deep learning powers speech recognition, language understanding, and voice generation. The more data the system is exposed to, the better it gets at mimicking human understanding.

A dialer system automates the process of placing outbound calls. It can dial numbers from a list and connect calls to agents or AI bots. Dialer types include predictive, preview, and power dialers. These systems improve efficiency, reduce idle time, and increase the number of calls made per hour.

Dialogue management controls how a voice AI system handles back-and-forth conversations. It determines when to ask questions, confirm details, or transition topics. Good dialogue management keeps conversations flowing smoothly, even when the caller provides incomplete or unexpected input. It’s critical for delivering a natural, helpful experience.

Digital Signal Processing involves analyzing and modifying voice signals to improve clarity, reduce noise, and extract features. DSP plays a foundational role in converting spoken language into clean, usable input for voice AI systems. It ensures calls sound clear and the system can understand users accurately.

Direct response technology enables callers to take immediate actions during a call—such as pressing a key to get more information or confirming an appointment. It creates a more interactive and conversion-focused experience, especially useful in sales or promotional campaigns run by AI voice assistants.

Distributed speech recognition splits the task of voice recognition between the caller’s device and the cloud. For example, a phone might process some audio locally and send it to a server for deeper analysis. This approach can speed up response times and improve accuracy in low-bandwidth situations.

DNC compliance ensures your system doesn’t contact phone numbers listed on national or regional “Do Not Call” registries. It's a legal requirement for outbound calling campaigns. A compliant AI voice platform automatically screens numbers against these lists, helping businesses avoid fines and maintain trust.

DTMF is the tone-based input method used when callers press keys on a phone keypad during a call. Each keypress sends a specific frequency combination. Voice systems use DTMF for menu navigation, confirmations, or data entry when speech input is unavailable or not preferred by the user.

Dynamic call routing adjusts the destination of incoming calls in real time based on criteria like time of day, caller history, or call volume. Instead of fixed rules, it uses logic or AI to ensure each call reaches the most suitable bot or agent, improving resolution speed and user experience.

Dynamic IVR is an intelligent version of interactive voice response that adapts menus and responses based on caller behavior or past interactions. Instead of a static script, it personalizes prompts and flow, helping callers get what they need faster and making the experience feel more human and efficient.

Dynamic scripting creates flexible, real-time call scripts that adjust based on caller inputs or context. For example, if a caller says they’ve already paid, the AI skips billing prompts. It improves relevance and flow by avoiding robotic, one-size-fits-all interactions.

Edge AI means running AI algorithms directly on local devices or servers rather than relying solely on the cloud. In voice systems, this enables faster response times, offline functionality, and better privacy. It’s especially useful for devices in remote areas or when low latency is essential.

Embedding models convert words or phrases into numerical vectors that capture their meaning. These models help AI understand context and relationships between words. In voice AI, embeddings allow systems to interpret varied phrases with similar intent—like knowing “book an appointment” and “schedule a visit” mean the same thing.

Emotion detection uses AI to identify feelings such as anger, happiness, or frustration in a caller’s voice. This helps the system adapt its tone or escalate the call when needed. Emotion-aware bots can improve customer satisfaction by responding more empathetically and recognizing when human intervention is necessary.

Enterprise AI refers to large-scale artificial intelligence solutions designed for business use. It combines tools like voice bots, analytics, automation, and integrations with systems like CRM or HR software. Enterprise AI platforms help organizations operate more efficiently, personalize experiences, and scale customer service without increasing overhead.

Entity extraction identifies and pulls out specific details—like names, dates, locations, or account numbers—from spoken input. In voice systems, this helps the bot collect the right information to complete tasks such as booking an appointment or processing a payment without needing a human.

Entity recognition is the broader task of detecting key pieces of information in natural language, such as a city name, date, or product type. It’s a key part of how voice AI understands user intent and provides accurate, context-specific responses. The system learns to identify both standard and custom entities.

Error recovery is how a voice system handles mistakes or misunderstandings. For example, if the AI mishears something or doesn’t understand a request, it might repeat the question, offer alternatives, or escalate to a human. Effective recovery ensures conversations stay on track and users don’t get frustrated.

Event triggering allows the voice system to initiate actions—like sending a message, updating a database, or transferring a call—based on specific inputs or conditions. For example, if a user says “cancel my appointment,” it triggers a backend update. It automates workflows and links conversations to business systems.

Explainable AI refers to systems designed to show how they make decisions in a way that humans can understand. In voice AI, this could mean providing a reason for suggesting an action or clarifying what data influenced a response. It builds trust, especially in industries that require transparency like healthcare or finance.

An extended response is when a voice AI system provides a more detailed answer, often covering multiple points or offering additional information beyond the original question. This helps users get complete answers without needing to ask multiple follow-up questions. It improves efficiency and user satisfaction.

A fallback mechanism is the backup plan for when a voice AI system doesn’t understand a user’s input. It might repeat the question, offer alternative options, or route the call to a human. These safeguards ensure the user can still complete their task, even if the AI encounters an error.