What is an AI Chatbot and How Does it Work?

What Is an AI Chatbot and How Does It Work?

An AI chatbot is a software application that uses artificial intelligence to understand questions and respond through a conversational interface.

Unlike a traditional chatbot that follows only fixed scripts or button-based paths, an AI chatbot can interpret different ways of asking the same question, search approved information and generate a relevant response.

AI chatbots are commonly used on websites, mobile applications, customer portals and internal business systems.

They can help businesses answer frequently asked questions, guide visitors, capture leads, book appointments, recommend products and support customers outside normal working hours.

This guide explains what an AI chatbot is, how AI chatbots work, which technologies power them and how they differ from traditional chatbots, live chat and virtual assistants.

TLDR: What is an AI chatbot?

An AI chatbot is software that uses artificial intelligence to understand written or spoken questions and provide conversational answers.

It usually works by:

1. Receiving a message from the user
2. Interpreting what the user means
3. Considering the earlier conversation
4. Searching relevant information
5. Generating or selecting an answer
6. Presenting the response through text or voice
7. Completing an action or directing the user to a person

Businesses use AI chatbots for:

• Customer support
• Lead generation
• Website guidance
• Appointment booking
• Product recommendations
• Customer onboarding
• Internal knowledge searches
• Frequently asked questions

A well-configured AI chatbot should use accurate business information, explain its limitations and avoid inventing answers when the required information is unavailable.

AI chatbot explained simply

The easiest way to understand an AI chatbot is to think of it as a conversational layer between a person and a set of information or business systems.

Instead of manually searching through website pages, documents or menus, the user asks a question in their own words.

For example:

Do you create chatbots for businesses that need more leads?

The chatbot interprets the question, identifies that the user is interested in lead-generation chatbots and searches the information it has been authorised to use.

It may then provide a response such as:

Yes. Our AI chatbots can ask visitors about their requirements, recommend a relevant service and collect contact information for your sales team.

The user can then ask a follow-up question:

Can it send those leads to our CRM?

A capable chatbot can understand that “it” refers to the chatbot being discussed and continue the conversation without requiring the user to start again.

What does AI chatbot mean?

The term combines two ideas:

• Artificial intelligence: technology that can recognise patterns, process language, generate content or make predictions
• Chatbot: software that communicates with users through a conversational interface

Not every chatbot uses artificial intelligence.

Some chatbots follow fixed rules and display pre-written responses. An AI chatbot can generally interpret more varied language and produce more flexible answers.

How does an AI chatbot work?

An AI chatbot usually works through a series of connected stages.

The exact process differs between platforms, but most modern systems follow a similar structure.

1. The user sends a message

The interaction begins when a user types or speaks a question.

The message could be:

• A direct question
• A request for help
• A description of a problem
• A product requirement
• An appointment request
• A request to complete an action
• A follow-up to an earlier answer

Examples include:

• What services do you offer?
• Can I book a consultation?
• Which package is best for a small business?
• My account is not working.
• Do you cover Birmingham?
• How much does delivery cost?

2. The chatbot processes the language

The chatbot analyses the message to understand its likely meaning.

This may involve identifying:

• The user’s intention
• Important keywords
• Products or services
• Locations
• Dates
• Account references
• Sentiment
• Urgency
• Relationships between words

For example, the questions below may all have the same underlying intention:

• How much is it?
• What does it cost?
• Can you tell me your prices?
• Is there a monthly fee?
• What are your packages?

The chatbot may classify all of these as pricing questions.

3. The chatbot considers conversation context

Modern AI chatbots can often use earlier messages to understand follow-up questions.

For example:

User: Do you create websites?

Chatbot: Yes. We design and develop websites for service businesses.

User: How long do they take?

The chatbot can infer that “they” refers to websites.

Context allows the conversation to feel more natural, but it should be managed carefully. Businesses should understand how long conversation data is retained and whether personal information is included.

4. The chatbot retrieves relevant information

The chatbot searches the sources it has been configured to use.

These may include:

• Website pages
• Frequently asked questions
• Uploaded documents
• Help-centre content
• Product information
• Service descriptions
• Policies
• Internal documents
• Customer records
• Booking systems
• Order-management platforms

This retrieval stage is important because it helps the chatbot answer from business-approved information rather than relying entirely on broad model knowledge.

5. The chatbot prepares a response

The chatbot uses the retrieved information, its instructions and the conversation context to prepare an answer.

Instructions may control:

• Tone of voice
• Response length
• Formatting
• Restricted topics
• Required links
• Calls to action
• Escalation rules
• Information it must not invent
• Situations requiring human support

For example, a business may instruct its chatbot never to estimate prices when an exact quote requires a project assessment.

6. The answer is shown to the user

The response may be displayed as:

• Text
• A list
• A link
• A product card
• A comparison
• A form
• A booking calendar
• A downloadable file
• A spoken answer
• A video or avatar response

A useful chatbot should answer the immediate question first and then offer a relevant next step.

7. The chatbot may complete an action

Some chatbots only provide information.

Others can connect to business systems and complete approved tasks such as:

• Booking an appointment
• Creating a support ticket
• Checking an order
• Collecting a lead
• Sending an email notification
• Updating a CRM record
• Recommending a product
• Routing an enquiry
• Starting an automation

The chatbot should only have access to the systems and permissions required for its purpose.

8. The conversation can be reviewed

Businesses can review chatbot conversations to understand:

• Which questions are asked most often
• Which answers are missing
• Where users become confused
• Which services receive the most interest
• Why visitors abandon a process
• Which conversations produce leads
• When human support is needed

Conversation review is an important part of maintaining and improving an AI chatbot.

How AI chatbots work at a glance

Stage	What happens	Example
User input	The user sends a question	“Do you build ecommerce websites?”
Language processing	The chatbot identifies the meaning	Ecommerce website enquiry
Context	Earlier messages are considered	The user previously mentioned a new shop
Retrieval	Relevant business information is found	Ecommerce service content
Response generation	The chatbot prepares an answer	Service explanation and next step
Action	The user completes a task	Books a consultation
Review	The business analyses the interaction	Identifies a common sales question

What technology powers an AI chatbot?

AI chatbots usually combine several technologies rather than relying on one system.

Natural-language processing

Natural-language processing helps software analyse and work with human language.

It can help the chatbot recognise:

• Meaning
• Sentence structure
• Intent
• Named entities
• Sentiment
• Relationships between words
• Different ways of expressing the same request

Natural-language processing is one reason modern chatbots can understand more than fixed keywords.

Natural-language understanding

Natural-language understanding focuses on identifying what the user is trying to achieve.

For example, the chatbot may distinguish between:

• Asking for a refund policy
• Requesting a refund
• Complaining about an earlier refund
• Checking whether a refund was processed

The same word may appear in each message, but the required response is different.

Large language models

Large language models are AI systems trained to understand and generate language.

They help chatbots create more flexible and natural responses.

A language model can:

• Explain information
• Summarise content
• Rephrase an answer
• Respond to follow-up questions
• Organise information into steps
• Adapt to different tones

However, language models can also generate incorrect or unsupported information. Business chatbots therefore require reliable knowledge sources, instructions and review processes.

Information retrieval

Information-retrieval systems help the chatbot locate relevant content before generating an answer.

This may involve searching:

• Web pages
• Documents
• Knowledge bases
• Databases
• Product catalogues
• Support articles

A common approach is to retrieve information from approved sources and then use an AI model to explain it conversationally.

Machine learning

Machine-learning systems identify patterns in information.

They may help with:

• Intent detection
• Message classification
• Product recommendations
• Conversation routing
• Spam detection
• Sentiment analysis
• Predictive suggestions

Embeddings and vector search

Some AI chatbots convert content and questions into mathematical representations called embeddings.

These representations help the system find information with a similar meaning, even when the exact words are different.

For example, a website page titled Payment options could still be retrieved when a visitor asks:

Can I pay monthly?

The words are different, but the underlying subject is related.

APIs

An application programming interface allows the chatbot to exchange information with another system.

APIs can connect a chatbot to:

• CRM platforms
• Calendars
• Help desks
• Order systems
• Ecommerce platforms
• Payment systems
• Internal databases
• Workflow tools

Speech recognition

Voice chatbots use speech-recognition technology to convert spoken words into text the system can process.

Text-to-speech technology

Text-to-speech technology converts the chatbot’s written answer into audio.

The voice may be:

• A standard synthetic voice
• A brand voice
• An approved clone of a real person’s voice

AI chatbot technology compared

Technology	Role within the chatbot
Natural-language processing	Analyses language
Natural-language understanding	Identifies the user’s intention
Large language model	Generates flexible responses
Information retrieval	Finds relevant business content
Machine learning	Recognises patterns and classifications
Vector search	Finds content with similar meaning
API	Connects other business systems
Speech recognition	Converts speech into text
Text to speech	Converts responses into audio

What information can an AI chatbot use?

A chatbot can use several types of information, depending on the platform and configuration.

Public website content

The chatbot may use:

• Service pages
• Product pages
• FAQs
• Blog posts
• Help articles
• Policy pages
• Location pages
• Pricing information

Uploaded documents

Businesses may upload:

• PDFs
• Word documents
• Text files
• Product guides
• Training material
• Policy documents
• Internal manuals

Complex documents containing scans, images or unusual tables may require additional testing.

Manually entered information

Businesses can often add specific instructions or facts directly through the chatbot dashboard.

This can be useful for:

• Temporary information
• Specific wording
• New services
• Short FAQs
• Restricted topics
• Escalation guidance

Live business data

Through integrations, a chatbot may access current information such as:

• Appointment availability
• Order status
• Product stock
• Account details
• Delivery information
• Customer records

Access to live or personal data should require appropriate authentication and permissions.

General AI knowledge

Some chatbots may also use the broader knowledge of the underlying AI model.

For business use, this should be controlled carefully.

A chatbot should not use general knowledge to invent company-specific prices, policies or service details.

What is chatbot training?

The term training a chatbot is often used broadly.

It may refer to several different processes.

Adding knowledge

The business supplies website pages, documents and information the chatbot can search.

Writing instructions

The business defines how the chatbot should behave.

Creating conversation flows

The business maps structured processes such as lead capture or appointment booking.

Testing and correcting

The team asks realistic questions and updates weak answers.

Improving source content

Website pages and documents are rewritten so the chatbot has clearer information to use.

In many no-code platforms, the business is not retraining the underlying language model itself. It is configuring the chatbot to use approved content and instructions.

Types of chatbots

Chatbots can be grouped according to how they understand and respond.

Rule-based chatbot

A rule-based chatbot follows fixed rules, buttons or decision trees.

It is useful for:

• Simple menus
• Structured forms
• Department routing
• Appointment steps
• Basic FAQs

Its main advantage is predictability.

Its limitation is that users may become stuck when their question does not match one of the planned routes.

Keyword-based chatbot

A keyword chatbot looks for specific words or phrases.

For example, a message containing “refund” may trigger a refund response.

This approach is simple but may misunderstand the context.

Intent-based chatbot

An intent-based chatbot attempts to understand what the user wants rather than matching only individual keywords.

It can recognise different ways of asking the same question.

Generative AI chatbot

A generative AI chatbot uses a language model to create responses.

It can provide more natural and flexible conversations but requires stronger safeguards and testing.

Retrieval-based chatbot

A retrieval-based chatbot searches approved content and returns or explains the most relevant information.

This is useful for customer service, website guidance and internal knowledge.

Transactional chatbot

A transactional chatbot helps users complete specific actions.

Examples include:

• Booking an appointment
• Tracking an order
• Updating account information
• Submitting a request
• Making a reservation

Voice chatbot

A voice chatbot communicates through spoken language.

It may be used for:

• Telephone support
• Voice assistants
• Accessibility
• Appointment booking
• Basic customer service

Hybrid chatbot

A hybrid chatbot combines several approaches.

For example:

• AI answers open questions
• Buttons guide structured actions
• Retrieval provides approved information
• Integrations complete tasks
• Human agents handle complex issues

Hybrid designs are often practical for business use because they combine flexibility with control.

Chatbot types compared

Chatbot type	How it works	Best suited to	Main limitation
Rule based	Follows fixed routes	Structured processes	Limited flexibility
Keyword based	Matches specific terms	Simple FAQs	Weak context understanding
Intent based	Identifies user goals	Support and routing	Requires accurate classification
Generative AI	Creates flexible responses	Natural conversations	Can generate incorrect information
Retrieval based	Searches approved content	Business knowledge	Depends on source quality
Transactional	Completes actions	Booking and account tasks	Requires integrations
Voice chatbot	Uses spoken input and output	Telephone and voice support	Background noise and recognition issues
Hybrid chatbot	Combines several methods	Wider business use	More configuration required

What is the difference between a chatbot and an AI chatbot?

A traditional chatbot commonly uses fixed scripts, buttons or keyword rules.

An AI chatbot can interpret more varied language and create flexible responses.

Traditional chatbot	AI chatbot
Follows predefined rules	Interprets natural language
Uses fixed responses	Can generate responses
Limited conversation context	Can consider earlier messages
Requires anticipated questions	Can handle more varied wording
Highly predictable	Requires additional safeguards
Suitable for simple flows	Suitable for questions and guidance
Usually easy to test	Requires broader testing

A traditional chatbot may still be the better choice for a tightly controlled process.

The most effective systems often combine structured flows with AI-generated answers.

What is the difference between an AI chatbot and live chat?

Live chat connects the user with a human employee.

An AI chatbot provides automated responses.

AI chatbot	Live chat
Automated	Human operated
Can remain available continuously	Limited by staff availability
Handles several conversations at once	Capacity depends on the team
Strong for repeated questions	Strong for unusual situations
Requires business knowledge and instructions	Uses human judgement
Responds immediately	May involve waiting
Cannot fully reproduce empathy	Can understand emotional context

A business can use both.

The chatbot may answer basic questions and collect information before transferring the conversation to a person.

What is the difference between an AI chatbot and a virtual assistant?

The terms sometimes overlap.

An AI chatbot is mainly defined by its conversational interface.

A virtual assistant may have a wider role and complete tasks across several systems.

For example, a virtual assistant might:

• Manage a calendar
• Prepare reminders
• Update a CRM
• Send messages
• Retrieve documents
• Complete workflow tasks

An AI chatbot may provide the conversational interface through which the user controls the assistant.

What is the difference between an AI chatbot and an AI agent?

An AI chatbot primarily answers questions and manages conversations.

An AI agent may be designed to plan and complete a sequence of actions with less direct input.

AI chatbot	AI agent
Primarily conversational	Primarily goal oriented
Responds to individual messages	May plan multiple steps
Often retrieves and explains information	May act across connected systems
Usually waits for user input	May continue until a task is completed
Commonly customer facing	Can be customer facing or internal
Typically limited permissions	May require broader system permissions

The distinction is not always clear because many chatbot platforms are adding agent-like capabilities.

As systems become more action-oriented, security, approval rules and permission controls become increasingly important.

What is the difference between a chatbot and a search engine?

A search engine returns pages or results related to a query.

A chatbot can interpret the question, retrieve information and explain the answer conversationally.

Search engine or search bar	AI chatbot
Returns results	Provides an answer
Often relies on keywords	Interprets natural language
User reviews several pages	Chatbot summarises information
Limited follow-up context	Supports conversational follow-ups
Strong for exploration	Strong for direct guidance

A chatbot should not replace clear website navigation or searchable content.

Users should still be able to access important information without beginning a conversation.

What can an AI chatbot do?

The capabilities depend on the platform, knowledge and integrations.

Answer questions

The chatbot can explain:

• Services
• Products
• Prices
• Processes
• Policies
• Opening hours
• Delivery information
• Support instructions

Guide website visitors

It can direct people towards:

• Relevant service pages
• Product categories
• Help articles
• Contact options
• Booking pages
• Resources

Capture leads

A chatbot can ask about:

• Required service
• Project goals
• Location
• Timing
• Budget range
• Contact details

It can then notify the appropriate team or create a CRM record.

Book appointments

When connected to a calendar, the chatbot may:

• Display availability
• Collect contact information
• Confirm an appointment
• Reschedule
• Cancel

Recommend products or services

The chatbot can ask questions about the user’s requirements and recommend suitable options.

Provide customer support

It can help with:

• Common technical issues
• Account navigation
• Order questions
• Policy explanations
• Troubleshooting
• Ticket creation

Support employees

An internal chatbot can help staff search:

• Policies
• Training documents
• Technical guides
• Product information
• Process instructions

Complete business actions

When connected to other systems, it may:

• Update a CRM
• Send an email
• Create a ticket
• Retrieve an order
• Start an automation
• Record a request

Business AI chatbot examples

Customer-support chatbot

A customer asks:

How do I reset my password?

The chatbot retrieves the approved support instructions and guides the user through the steps.

If the reset fails, it creates a support ticket.

Lead-generation chatbot

A website visitor asks:

Can you build a chatbot for my roofing company?

The chatbot explains the service, asks about the visitor’s goals and collects contact details for follow-up.

Ecommerce chatbot

A shopper asks:

I need a lightweight laptop for design work under £1,000.

The chatbot asks about software requirements and screen size before recommending suitable products from the available catalogue.

Appointment chatbot

A patient or customer asks:

Can I book for Thursday afternoon?

The chatbot checks the connected calendar and displays suitable times.

Internal knowledge chatbot

An employee asks:

What is our process for approving annual leave?

The chatbot retrieves the relevant company policy and links to the official document.

What are the benefits of AI chatbots?

Immediate responses

A chatbot can answer without making users wait for a team member.

Continuous availability

It can provide guidance or collect enquiries outside normal opening hours.

Reduced repetitive work

The chatbot can handle common questions that would otherwise require repeated manual responses.

Consistent information

A well-maintained chatbot can use the same approved information across conversations.

Better lead capture

Visitors can receive help and submit information while they are actively considering a service.

Scalable conversations

One chatbot can manage several conversations at the same time.

Improved navigation

Users can ask for what they need instead of browsing several pages.

Useful customer insight

Conversation history can reveal common questions, objections and content gaps.

Multilingual communication

Some chatbots can support several languages, although important conversations should be reviewed for accuracy.

What are the limitations of AI chatbots?

They can provide incorrect answers

An AI chatbot may misinterpret a question or generate unsupported information.

They depend on source quality

Outdated, incomplete or confusing business content can produce poor responses.

They lack full human judgement

Chatbots cannot fully reproduce empathy, negotiation or understanding of complicated personal circumstances.

They require maintenance

Knowledge, instructions and integrations need to be reviewed when the business changes.

Users may prefer a person

Some visitors do not want to communicate with an automated system.

Integrations can introduce risk

Access to customer accounts or business systems requires strong permission controls.

Complex conversations remain difficult

An unusual complaint or technical problem may need specialist human support.

They can create extra friction

A poorly designed chatbot can become another barrier rather than a useful support tool.

Benefits and limitations compared

Benefit	Related limitation
Immediate answers	Fast answers may still be wrong
Continuous availability	Human support may be offline
Consistent responses	Source information can become outdated
Scalable conversations	Complex cases still need people
Reduced repetitive work	Setup and maintenance require time
Lead capture	Poor flows can reduce conversions
Multilingual support	Translation quality may vary

Can an AI chatbot understand context?

Many AI chatbots can consider earlier messages within the same conversation.

This allows users to:

• Ask follow-up questions
• Refer to earlier details
• Change or refine requirements
• Avoid repeating information

However, context has limits.

A chatbot may lose track when:

• The conversation becomes very long
• The user changes subject repeatedly
• Several products or people are discussed
• The platform has a limited context window
• Earlier information conflicts with newer information

Important details should be confirmed before an action is completed.

Can an AI chatbot learn from conversations?

The meaning of “learn” depends on the system.

A chatbot may improve through:

• Manual review
• Updated knowledge
• Revised instructions
• New examples
• Better conversation flows
• Model updates from the provider

Businesses should not assume that every customer conversation automatically retrains the chatbot.

Automatically learning from unreviewed user messages could also introduce incorrect or harmful information.

Can an AI chatbot remember users?

Some chatbots can retain information across sessions when they are connected to an account or customer database.

They may remember:

• Preferences
• Previous orders
• Support history
• Saved details
• Earlier conversations

This requires appropriate consent, security and privacy controls.

A general website chatbot should not imply that it remembers a visitor when no persistent memory exists.

Can AI chatbots make mistakes?

Yes.

Common causes include:

• Missing information
• Outdated information
• Ambiguous questions
• Poor instructions
• Incorrect source retrieval
• Model limitations
• Integration errors
• Conflicting documents

A trustworthy chatbot should admit when it cannot provide a confirmed answer.

What is an AI chatbot hallucination?

A hallucination is an answer generated by AI that is incorrect, unsupported or invented.

For example, a chatbot might create:

• A price that does not exist
• A service the business does not offer
• A false delivery promise
• An incorrect policy
• A fabricated source
• An unsupported guarantee

Businesses can reduce this risk by:

• Limiting the chatbot to approved sources
• Creating clear instructions
• Using fallback responses
• Testing difficult questions
• Reviewing conversation history
• Keeping knowledge updated
• Escalating uncertain questions to a person

How does a chatbot know when to contact a human?

Human escalation can be triggered by:

• A direct request for a person
• Restricted topics
• Low-confidence answers
• Repeated failed responses
• Complaint language
• Sensitive information
• High-value sales opportunities
• Account-security concerns
• Emergency or urgent terms

The handover route may be:

• Live chat
• Email
• Telephone
• Contact form
• Support ticket
• Meeting booking
• Callback request

The system should clearly explain whether a person is currently available.

Are AI chatbots safe?

AI chatbots can be used safely when businesses apply appropriate controls.

Important safeguards include:

• Accurate knowledge sources
• Clear system instructions
• Restricted topics
• Human escalation
• Secure integrations
• Access permissions
• Data minimisation
• Conversation monitoring
• Appropriate retention settings
• Clear privacy information

A chatbot should not ask users to share passwords, full card details or unnecessary sensitive information.

AI chatbot privacy considerations

Before deploying a chatbot, businesses should understand:

• What information is collected
• Why the information is needed
• Where it is stored
• How long it is retained
• Who can access it
• Whether it is used for model training
• Which providers process it
• How it can be deleted
• Whether users can request human support

The amount of information collected should be proportionate to the task.

What makes an AI chatbot trustworthy?

A trustworthy chatbot should:

• Clearly identify itself as AI
• Explain what it can help with
• Use current and approved information
• Avoid pretending to know unavailable details
• Link to official sources
• Protect customer information
• Provide access to a person
• Use clear and professional language
• Confirm important actions
• Be tested and maintained regularly

How are AI chatbots created?

Creating a business chatbot usually involves the following stages.

Step 1: Define the objective

The business identifies what the chatbot should achieve.

Examples include:

• Answer support questions
• Generate leads
• Book appointments
• Recommend products
• Guide website visitors
• Search internal documents

Step 2: Identify user questions

Review:

• Emails
• Contact forms
• Support tickets
• Sales calls
• Website searches
• Social media messages

This reveals the language customers actually use.

Step 3: Prepare the knowledge

Gather accurate information such as:

• Service pages
• Product details
• Prices
• Policies
• Support guides
• FAQs
• Contact routes

Step 4: Choose a chatbot platform

Select a platform based on:

• Ease of use
• Knowledge management
• Accuracy controls
• Integrations
• Design
• Security
• Analytics
• Pricing

Step 5: Add instructions

Define:

• Purpose
• Tone
• Response length
• Restricted topics
• Escalation rules
• Calls to action
• Information boundaries

Step 6: Create structured flows

Build flows for important actions such as:

• Lead capture
• Appointment booking
• Support routing
• Human handover

Step 7: Customise the design

Match the chatbot to the website while maintaining readability and accessibility.

Step 8: Add integrations

Connect the required:

• CRM
• Calendar
• Help desk
• Ecommerce system
• Automation platform

Step 9: Test the chatbot

Use realistic questions, mistakes, edge cases and restricted requests.

Step 10: Launch and review

Publish the chatbot gradually and monitor conversations.

AI chatbot creation process

Stage	Main activity	Result
Define	Establish the purpose	Clear use case
Research	Identify user questions	Conversation requirements
Prepare	Organise accurate information	Approved knowledge
Configure	Add instructions and flows	Chatbot behaviour
Design	Customise the interface	Branded experience
Integrate	Connect business systems	Actions and data
Test	Review answers and journeys	Reliable deployment
Improve	Analyse conversations	Ongoing optimisation

Do you need coding skills to create an AI chatbot?

Not always.

A no-code AI chatbot maker allows businesses to configure a chatbot through a visual dashboard.

Users may be able to:

• Add website pages
• Upload documents
• Write instructions
• Create lead forms
• Customise colours
• Test responses
• Install a website widget
• Review conversations

Coding may be needed for:

• Custom interfaces
• Complex authentication
• Proprietary systems
• Advanced integrations
• Specialist security
• Unusual workflows

Nertia’s AI Chatbot Maker is designed to help businesses create, customise, test and deploy chatbots without building the entire system from the beginning.

How is an AI chatbot added to a website?

Most chatbot platforms provide an installation script or plugin.

The process commonly involves:

1. Creating the chatbot
2. Customising its appearance
3. Testing the responses
4. Copying an installation code
5. Adding the code to the website
6. Publishing the changes
7. Testing the live widget

A chatbot can be added through:

• Website code
• WordPress
• Google Tag Manager
• A platform plugin
• A website builder
• A custom integration

Where should a chatbot appear?

A chatbot may appear as:

• A floating launcher
• A full-page assistant
• An embedded support panel
• A dashboard feature
• A help-centre search tool
• A product recommender
• A booking assistant

The placement should match the user’s needs.

A chatbot should not cover important website controls, especially on mobile devices.

How should an AI chatbot be designed?

A good chatbot interface should include:

• A clear AI identity
• A short explanation of its purpose
• Readable messages
• Suggested starting questions
• Free-text input
• A visible close button
• Mobile-friendly controls
• An obvious human-support route
• Accessible colours and typography

For deeper design guidance, read The Ultimate Guide to AI Chatbot Design and UI.

How do you choose an AI chatbot platform?

Important factors include:

• Intended use
• Ease of setup
• Knowledge sources
• Answer controls
• Conversation flows
• Design customisation
• Integrations
• Analytics
• Security
• Pricing
• Scalability
• Support

Read How to Choose the Best AI Chatbot Maker in 2026 for a complete comparison framework.

How much does an AI chatbot cost?

Costs depend on:

• Platform
• Number of chatbots
• Conversation volume
• AI usage
• Number of websites
• Team access
• Integrations
• Data storage
• Support
• Custom development

Common pricing models include:

• Monthly subscription
• Per conversation
• Per message
• Per chatbot
• Per user
• Custom enterprise pricing

Businesses should also consider setup, content preparation, testing and maintenance costs.

How do you measure an AI chatbot?

Useful performance measures include:

• Engagement rate
• Conversation starts
• Resolution rate
• Fallback rate
• Lead conversion
• Booking completion
• Human escalation
• Abandonment
• Customer satisfaction
• Answer accuracy

A high number of conversations does not automatically mean the chatbot is effective.

The conversations should lead to useful outcomes.

AI chatbot performance metrics

Metric	What it shows
Engagement rate	How many visitors use the chatbot
Resolution rate	How many questions are answered
Fallback rate	How often information is unavailable
Lead conversion	How many chats produce enquiries
Booking rate	How many users complete a booking
Escalation rate	How often human help is needed
Abandonment rate	Where users leave
Satisfaction	Whether users found it helpful
Accuracy rate	How often reviewed answers are correct

AI chatbots and digital twins

An AI chatbot manages the conversational part of an experience.

A digital twin or AI avatar provides a visual or spoken representation.

Together, they can create an experience in which:

1. A visitor asks a question
2. The chatbot interprets the request
3. Approved information is retrieved
4. A digital avatar presents the response
5. The visitor continues or requests a person

Explore Nertia’s Digital Twin service to learn about high-fidelity AI avatars, voice modelling and multilingual video.

AI chatbots and website design

A chatbot should support a clear website rather than replace one.

Essential information should remain available through:

• Service pages
• Product pages
• Navigation
• FAQs
• Contact pages
• Help content
• Calls to action

Nertia’s Website Design and Development service helps businesses create clear, accessible websites that can be strengthened by chatbots and other digital tools.

The future of AI chatbots

AI chatbots are likely to become more closely connected to business data and operational systems.

Future developments may include:

• More accurate answers from live business information
• Better voice conversations
• Improved multilingual support
• More personalised experiences
• Deeper CRM and ecommerce connections
• Stronger AI-agent capabilities
• Better conversation summaries
• More natural human handovers
• Visual responses through AI avatars
• Stronger permission and privacy controls

As chatbots gain the ability to complete more actions, businesses will need to apply stricter approval, authentication and oversight processes.

Learn more about AI chatbots for business

An AI chatbot can help a business answer questions, guide visitors and complete repeatable tasks.

Its effectiveness depends on the quality of its information, instructions, design and ongoing management.

For a broader look at business applications, costs, implementation and performance, read The Complete Guide to AI Chatbots for Business in 2026.

Build an AI chatbot around your business

Nertia’s AI Chatbot Maker gives businesses a clear way to create, customise, test and deploy AI assistants.

You can control:

• The information the chatbot uses
• How it responds
• Its visual design
• Lead-generation flows
• Testing
• Website installation
• Conversation history
• Performance monitoring

Explore Nertia’s AI Chatbot Maker

You can also explore:

The Complete Guide to AI Chatbots for Business in 2026

How to Choose the Best AI Chatbot Maker in 2026

The Ultimate Guide to AI Chatbot Design and UI

Who Uses AI Chatbots the Most and Which Are the Most Trusted?