Chatbot

Whether a simple interactive menu or a complex conversational product expert, a chatbot can improve an app or website's user experience by automating routine parts of the communication process with customers.

What Is a Chatbot?

A chatbot is a software tool for automating real-time text or voice conversations between users and applications. Common integrations of this functionality include in-app messaging or website widgets. These bots can handle user interactions without the need for human intervention, so their applications include:

• Answering questions, such as product FAQs

• Guiding users through a process 

• Surfacing help articles

• Escalating requests to human agents

The goal of implementation is primarily to streamline user experiences and reduce friction or assistance wait times. For example, a FinTech app may use a bot for its in-app customer service chat to handle simple onboarding queries.

How Does a Chatbot Work?

User Interface (UI)

The user interface (UI) of a chatbot is the visible, user-facing layer, such as a chat widget on a website or an in-app conversation thread. The interface collects input from the user and provides a response output from the backend in real time.

The UI can also include features like buttons or media attachments. More advanced apps might have UI that can provide contextual suggestions, like personalized product recommendations or prompts from previous interactions.

Backend Logic

The architecture in the backend is the core engine of the bot. In general, the backend systems handle the following:

• Tracking user behaviors and data across multiple platforms and messages

• Interpreting the aim of user messages

• Routing users to the correct channel (if needed) based on input and context

• Executing all rules and API calls

• Pulling and pushing data to and from external systems like CRMs or databases

• Formatting bot responses and implementing guardrails  

• Conditional logic, fallback flows, and error handling

Many production deployments connect to microservices or APIs to handle tasks from this list, like inventory checks or leveraging large language models (LLMs) for more natural interactions.

Developers also often build them with protocols like WebSocket for low latency, so the bot can maintain performance and provide real-time responses to the user. In use cases like live customer support, this is crucial to a positive user experience.

Knowledge Base

The knowledge base is the source (or sources) of information the bot draws from to create responses to user inputs. They use the knowledge base to retrieve answers from specific systems (like a product catalogue) or guide users through predefined workflows. It could be a structured database, an external system, a document store, or a vector database.

Rule-based chatbots typically rely on static mappings between expected or predefined user inputs and bot responses. For example, if the user input says "Can I cancel my order?", the bot may match the question to a command hardcoded in the backend and return a preset, template answer.

Retrieval-based bots, on the other hand, typically use methods like natural language processing (NLP) or embedding search to comprehend the intent of freeform answers from the user. They will then retrieve the most relevant answer, whether from a database or elsewhere.

AI-powered bots might pair an LLM with a vector database that stores embeddings of unstructured data to contextualize the user input. This setup enables retrieval-augmented generation (RAG). First, the bot retrieves relevant information from the knowledge base and passes it into the generative AI model, using the retrieved content to generate a more relevant and detailed answer.

Natural Language Processing (NLP)

NLP is a set of techniques that some chatbots use to analyze, interpret, and process input from human users. Not all of them use NLP, however. Simple rule-based bots normally don't, although some more advanced ones may use certain techniques for understanding the user's input.

NLP is most often a component of AI-powered bots. In these cases, when user input is freeform and, therefore, more ambiguous, NLP helps by:

• Intent recognition: Determining what the user is trying to achieve

• Entity extraction: Identifying key components in messages, such as names or order reference numbers

• Tokenizing and parsing: Breaking down sentences into smaller components (like words, phrases, and grammatical units) that the system can analyze and understand

Types of Chatbots

Rule-Based

These rely on hardcoded responses based on workflows that use decision trees or if/then logic. They are completely predictable and apply to simple use cases like FAQs or basic routing. The UI often presents users with predefined questions or options to select to progress the interaction. Many systems use rule-based bots as a backup layer for more complex hybrid or AI-powered bots.

AI-Powered

AI-powered bots use machine learning (ML) and NLP to handle input and generate output. Many are built on LLMs, like ChatGPT and Google Gemini. They can recognize the users' goals and retrieve responses through RAG or embedding search. For safety and control, fallback logic is a critical component of these bots. 

Conversational

Conversational ones can handle multi-turn dialogue with context across messages, and often across multiple threads and platforms. They often use techniques, like slot-filling, which guides users step by step to collect information needed for complex scenarios. Conversational chatbots can be built with rule-based approaches, retrieval-based systems, or AI models.

Voice-Enabled

Voice-enabled or voice-based bots can process speech input from a user before returning responses in either voice or text format. The backend logic is largely the same as text-only bots, but they often have higher requirements for latency and handling anomalies like interruptions or speech patterns (like accents).

Hybrid

Hybrid bots combine multiple approaches. The term "hybrid chatbot" most commonly applies to AI-powered bots that escalate interactions to human agents. They may also route between engines, such as an AI-powered bot with a fallback to a rule-based approach. 

Use Cases & Examples

Customer Support Automation

In a customer support environment, they can handle queries for resetting passwords, tracking orders, and more. They reduce the volume of these repetitive requests to free up human agents for more complex cases or escalations.

Regulated industries (like finance) can use them to assist with product tutorials and retain logs for compliance-mandated audit trails.

eCommerce Assistants

They can help retail brands drive eCommerce outcomes, like reducing cart abandonments or upselling. By integrating with CRMs and inventory systems, eCommerce chatbots can leverage purchase history and user segmentation to further contextualize responses.

One example is conversational commerce, where bots can guide users with tailored product recommendations to boost conversion rates.

Lead Qualification and Sales

For B2B apps or more complex products, bots can help  prequalify leads, so sales teams can focus their attention on valuable contracts with the highest probability of closing. For instance, the bot can walk users through a pricing assessment on a website or tag leads to route them to the appropriate salesperson. 

In-App Onboarding Workflows

Onboarding is an extremely common workflow in SaaS apps and is one area where products can lose new users.

They can provide assistance to reduce drop-off rates, such as by guiding them through feature configuration or identity validation. Slot-filling or conditional logic can help bots segment users through onboarding and dynamically provide more tailored guidance. 

Candidate Screening in Recruitment

Job postings generate large volumes of applications, and bots can prescreen candidates to reduce the workload for human recruiters. They can collect resumes and engage in prescreening questions with the candidate before application submission, integrating with applicant tracking systems (ATS) or HR platforms to streamline workflows.

Benefits of Using Chatbots

Instant Answers 24/7

Chatbots enable apps and websites to respond to queries instantly at any time. For brands operating across multiple regions and time zones, this means customer support and revenue-generating interactions can happen outside standard working hours for human agents.

It also helps with consistency. Even if the size of teams varies from one country or region to the next, support bots can ensure routine queries receive the same level of responsive service.

Reduced Customer Support Costs

Since bots can handle routine customer queries at scale, human agents have more time to focus on complex support cases and customer interactions.

While the bot takes care of account or order lookups and basic troubleshooting, humans can address complaints, complicated product questions, and more. During seasonal or unexpected spikes, the bots can reduce the impact on human teams and user wait times.

Personalized Guidance

One key benefit of chatbots is the ability to provide highly personalized responses by integrating with other systems. Personalization improves UX and shortens resolution times.

For example, if a customer previously submitted a ticket related to a purchase, they won't need to repeat their question or order details when reengaging with the bot because it can access the conversation history.

Multilingual Support

AI-powered ones can support users across multiple languages without the need for additional headcount. For global businesses or those expanding into new regions, this adds to the cost savings.

However, multilingual automated support requires close attention to error-handling and translation logic, as edge cases like localization of tone and dialect can impact accuracy and user experience.  

Best Practices for Development

Define Boundaries Early

You must begin with tightly-scoped applications, such as password resets or rule-based FAQ responses, and build out capabilities from there. This approach allows for the testing and early monitoring of edge cases, along with better conversation design and contingency planning.

Build Smooth Escalation Logic

Carefully assess escalation protocols by clearly defining when and why interactions should escalate to human agents. With AI assistants, for example, this means deciding on intent confidence thresholds, keywords, or other signals to smoothly trigger hand-off and avoid user frustration.

Plan for Context

Use metadata, session tracking, or slot filling to enable the bot to retain context across turns. This reduces customer frustration during complex workflows or hand-offs to human agents. Without context handling, the bot can repeat itself or ask the user to do so.

Test Edge Cases and Safety Nets Extensively

Interactions can become complicated fast, and bots that don't know what to do with edge cases can easily frustrate the user or damage trust. Simulating potential edge cases (like typos or incomplete user inputs) ensures users don't reach a dead end with the bot.

Log and Monitor Interactions

Capturing usage data provides insight into user friction and areas for improvement. Events like unresolved requests, drop-offs, and fallback rates can help pinpoint whether an adjustment is needed in conversation design, routing logic, or somewhere else.

Challenges in Development and How To Overcome Them

Compliance Gaps

When chatbots collect personal or sensitive data in highly regulated industries, they must comply with regulations, such as HIPAA, GDPR, or CCPA. That means ensuring consent flows and encryption are in place, or you risk fines and reputational damage.

Compliance-ready infrastructure reduces this risk and typically retains logs for audit purposes. Some AI chatbot integrations, for example, encrypt messages in transit and at rest, and provide audit-ready logs for HIPAA compliance. 

Complex or Fragile Integrations

Bots can connect with CRMs, customer experience platforms, order tracking systems, and more. But building these integrations in-house can result in issues like long-term technical debt. Instead, modular APIs and offloading real-time messaging layers isolate these capabilities to reduce overhead and outsource maintenance. 

Real-Time Performance Constraints

Users expect instantaneous responses. High latency erodes UX and increases drop-off rates. Purpose-built tools that use WebSocket-based architecture ensure low latency and scalable, real-time messaging.

Post-Launch Maintenance

Treating chatbots as a one-off build can lead to quality degradation as user behaviors or business conditions change. Flows that were once highly effective can become confusing or incorrect, which means higher failed intents and escalations. Monitoring logs closely helps identify these areas for improvement before they become a significant issue.