Glossary

Definitions and concepts for AI in customer service: AI agents, deflection, AHT, FRT, knowledge bases, RAG, hallucination and more.

71 terms

AI fundamentals

Agentic AI

Agentic AI describes systems that pursue multi-step goals autonomously, taking actions and adapting to results rather than responding turn-by-turn.

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AI agent

An AI agent is a software system that perceives a goal, plans steps toward it, takes actions in tools or APIs, and adapts based on results.

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AI guardrails

AI guardrails are policy and verification layers that constrain what an AI system will say or do, reducing hallucinations and unsafe outputs.

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AI hallucination

An AI hallucination is when a model produces a confident, fluent, but factually incorrect or fabricated response.

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Context window

The maximum amount of text, measured in tokens, that an AI model can read and consider at one time when generating a response.

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Conversational AI

Conversational AI is the category of software that uses natural language to interact with users, voice, chat, or messaging, going beyond scripted responses.

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Embeddings

Embeddings are numeric representations of text or other data that place items with similar meaning close together so machines can compare them.

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Fine-tuning

Fine-tuning is the process of further training a pretrained model on a focused dataset so it performs better on a specific task or domain.

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Function calling

Function calling lets an AI model trigger external tools or actions, like looking up an order, by returning a structured request the system can run.

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Generative AI

Generative AI is software that produces new content, text, images, code, audio, rather than classifying or scoring existing data.

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Human in the loop

Human in the loop is a design where people review, approve, or correct an AI system's outputs so humans stay in control of important decisions.

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LLM (Large Language Model)

A large language model is a neural network trained on massive amounts of text that predicts the next token given context.

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Machine learning

Machine learning is a branch of AI where systems learn patterns from data and improve at a task without being explicitly programmed for every case.

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Multi-agent system

A multi-agent system orchestrates several specialized AI agents that collaborate to solve problems too complex for a single agent.

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Natural language processing

Natural language processing is the field of AI that lets computers read, understand, and produce human language in text or speech.

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Neural network

A neural network is a machine learning model made of layered, connected units that learn to map inputs to outputs by adjusting weighted connections.

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RAG (Retrieval-Augmented Generation)

RAG is a pattern where an AI model retrieves relevant documents from a knowledge base and uses them as context to generate a response.

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Semantic search

Semantic search finds results based on meaning rather than exact keywords, so a query returns relevant content even when the wording differs.

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Tokens

Tokens are the small chunks of text, often words or parts of words, that an AI model reads and generates when processing language.

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Transformer model

A transformer model is a neural network design that uses attention to weigh how words in a sequence relate, and it underpins most modern language models.

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Vector database

A vector database stores embeddings and finds the items most similar in meaning to a query, making search by meaning fast at large scale.

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AI for support

AI agent assist (Copilot)

AI agent assist (or Copilot) is a real-time helper that suggests replies, surfaces knowledge, and recommends next-best actions while a human agent works on a ticket.

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AI chatbot

An AI chatbot is a conversational interface, usually web or mobile chat, that uses AI to interact with users in natural language.

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AI copilot

An AI copilot is an assistant that works alongside a support agent, suggesting replies, surfacing answers, and handling routine steps in real time.

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AI customer service

AI customer service is the use of AI agents, copilots, and automation to resolve customer issues across channels.

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Automated resolution

Automated resolution is when a support request is fully solved by software, without a human agent stepping in.

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Automated ticket triage

Automated ticket triage uses AI to read incoming support requests and sort, prioritize, and route them to the right team or agent.

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Customer support automation

Customer support automation is the use of software (AI agents, workflows, rules) to handle support tasks without human intervention.

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Intent recognition

Intent recognition is the process of classifying what a user wants from a natural-language input, the foundation of routing and resolution.

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Multilingual support

Multilingual support is the ability to help customers in their preferred language across chat, email, and voice channels.

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Natural Language Understanding (NLU)

NLU is the field of AI focused on parsing meaning from natural-language input, intents, entities, sentiment, and context.

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Omnichannel support

Omnichannel support delivers a unified customer experience across all channels, email, chat, voice, social, in-app, with shared context.

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Proactive support

Proactive support reaches out to customers before they ask for help, addressing likely issues based on signals and known patterns.

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Prompt engineering

Prompt engineering is the practice of designing inputs to LLMs to elicit reliable, useful outputs in production.

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Self-service support

Self-service support is when customers resolve their own issues through documentation, chatbots, or community, without contacting an agent.

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Sentiment analysis

Sentiment analysis uses AI to detect the emotional tone of a message, such as positive, negative, or neutral, from the customer's words.

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Ticket summarization

Ticket summarization uses AI to condense a support conversation into a short, clear recap of the issue, context, and resolution.

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Voice AI

Voice AI is the application of AI to spoken-language interfaces, voice agents, contact-center automation, and IVR replacement.

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Workflow automation

Workflow automation uses software to run multi-step support processes automatically, moving tickets and data between systems without manual handoffs.

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Support metrics

Abandonment rate

Abandonment rate is the share of customers who leave a queue before reaching an agent, typically while waiting on call or chat.

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Agent occupancy

Agent occupancy is the share of an agent's logged-in time spent actively handling contacts rather than waiting for work.

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Average Handle Time (AHT)

AHT is the average minutes a support agent spends handling a ticket from open to close, the operational cost-per-contact metric.

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Average resolution time

Average resolution time is the mean time it takes to fully resolve a ticket, measured from creation to closure.

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Containment rate

Containment rate is the share of contacts fully handled by automation without ever being escalated to a human agent.

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Cost per contact

Cost per contact is the total support spend divided by the number of customer contacts handled in a given period.

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CSAT (Customer Satisfaction Score)

CSAT is the score customers give after an interaction, usually a 1-5 scale rating overall satisfaction with the support experience.

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Customer Effort Score

Customer Effort Score measures how easy it was for a customer to get their issue resolved, based on an ease-of-resolution rating.

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Deflection rate

Deflection rate is the percentage of customer issues resolved without human agent involvement. The most-quoted metric in AI-for-support sales.

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First Contact Resolution (FCR)

FCR is the share of tickets resolved in the customer's first interaction, without follow-ups, re-opens, or repeat contacts.

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First Response Time (FRT)

FRT is the time from when a customer submits a ticket to when they receive the first meaningful response. The customer-facing speed metric.

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Net Promoter Score

Net Promoter Score measures customer loyalty by asking how likely customers are to recommend a company, then subtracting detractors from promoters.

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SLA compliance

SLA compliance is the share of tickets handled within the response and resolution targets set in a service level agreement.

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Ticket backlog

Ticket backlog is the count of open, unresolved tickets that have accumulated and still await action at a point in time.

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Ticket deflection

Ticket deflection is the share of customer issues resolved without human agent involvement, through AI, self-service, or automation.

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Voice of the customer

The practice of gathering and acting on customer feedback across channels to understand their needs, expectations, and experience.

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Support operations

Canned responses

Canned responses are pre-written reply templates agents reuse to answer common questions quickly and consistently.

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Escalation management

Escalation management is the process of moving a support case to a higher tier or specialist when it cannot be resolved at the current level.

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Helpdesk software

Helpdesk software is the platform support teams use to receive, track, and resolve customer tickets across channels.

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ITSM (IT Service Management)

ITSM is the discipline and software category for running IT operations as a service to the business, incidents, changes, problems, assets.

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Knowledge base

A knowledge base is the repository of articles, FAQs, and documentation that supports self-service and AI-driven resolution.

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Knowledge management system

A knowledge management system is the platform plus operating loop that keeps a knowledge base current, accurate, and useful.

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Knowledge-Centered Service

A support methodology where teams capture and improve knowledge as a natural part of solving tickets, so answers are reused instead of rebuilt each time.

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Queue management

The practice of organizing and prioritizing incoming support tickets so the right work reaches the right agent in the right order.

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Service desk

A service desk is the internal IT support function, and the software platform that runs it, handling employee tickets and IT operations.

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Service Level Agreement

A documented commitment between a support provider and its customers that defines target response and resolution times and the consequences of missing them.

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Shift-left support

A strategy that moves issue resolution closer to the customer through self service and frontline tools, reducing the need to escalate to specialists.

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Support quality assurance

The process of reviewing support interactions against a defined standard to measure quality, give feedback, and improve how agents serve customers.

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Ticket routing

Ticket routing is the process of directing each incoming support ticket to the right agent, team, or queue for handling.

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Tiered support

A support model that organizes agents into levels, where simpler issues are handled first and harder ones escalate to more specialized teams.

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Workforce management

The discipline of forecasting support demand and scheduling the right number of agents so service targets are met without overstaffing.

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