Building a chatbot for your business

A look at how conversational agents leverage the power of Gen AI

Why traditional chatbots fall short in real conversations

Chatbots have made significant strides in automating customer support, allowing organizations to provide instant responses to common queries. But despite their efficiency, they struggle to engage in meaningful, dynamic conversations. Most traditional chatbots rely on rigid, rule-based structures, making them ill-equipped to handle complex or unpredictable interactions.

As a result, they often feel like glorified FAQ pages - static, predictable, and incapable of engaging beyond their predefined scripts. While they can deliver quick answers, their responses lack depth, adaptability, and the natural flow of real conversation.

Traditional chatbots operate based on manually defined intents - predefined categories that map user inputs to expected responses. These intents work well for handling structured, repetitive queries but fall short when faced with nuances, ambiguous phrasing, or unexpected questions.

And that’s the core problem: traditional chatbots can only respond to questions they’ve been explicitly programmed to recognize. The moment a conversation veers off-script, they struggle to keep up, often failing to provide a relevant response. This is why so many chatbot interactions ultimately end with the dreaded “Please contact customer support”, which is less than ideal.

What are conversational agents?

The landscape of AI-driven chatbots has evolved dramatically. As we discussed earlier, traditional chatbots had limited capabilities, relying on predefined scripts and rule-based systems. But with the rise of Large Language Models (LLMs), that’s changed completely.

By integrating LLMs, chatbots gain the ability to think on their feet, generating responses that feel far more natural, context-aware, and useful than before. This new generation of chatbots is known as “conversational agents”, and this time, the name is not misplaced, because they really do appear to talk to you as they react intelligently to complex queries that confound the traditional chatbot.

SoftwareOne is at the forefront of developing conversational agents for a wide range of use cases, not just as customer service portals. We shall look at a few examples later. But first, to understand in what ways AI conversational agents are qualitatively different from the traditional chatbot, we need to understand how they work.

How does a conversational agent work?

Unlike traditional chatbots, conversational agents powered by Large Language Models (LLMs) don’t rely on predefined scripts or fixed responses. Instead, they use generative AI to dynamically generate answers based on context and intent, making conversations more fluid and adaptable.

At their core, conversational agents process user input in three key steps:

1. Understanding the query – The agent analyzes user input, identifying intent, key entities (e.g., a product name or a specific request), and overall context.
2. Retrieving relevant information – Instead of pulling from a static database of predefined answers, the agent retrieves relevant data from a variety of sources, such as internal knowledge bases, product catalogs, policy documents, and APIs. These sources can include structured databases, CRM systems, cloud repositories, and real-time data feeds—ensuring the agent can access the most up-to-date and contextually relevant information.
3. Generating a response – Using LLMs, the agent synthesizes a response that adapts to the conversation, handling nuanced phrasing, ambiguous questions, and multi-turn interactions without losing track of context.

By integrating dynamic retrieval with natural language generation, conversational agents engage naturally, maintain context over multiple exchanges, and provide accurate, relevant responses—even in complex or unpredictable scenarios.

What's the difference between a conversational agent and a chatbot?

Not all AI-driven chatbots are created equal. Traditional chatbots rely on predefined scripts and keyword recognition, making them effective for simple, structured queries but rigid and limited in handling complex or evolving conversations. 

Their main limitation is their inability to handle unexpected questions or multi-turn interactions. For example, if a user asks, “Can I change my delivery address after placing an order?” but the chatbot only recognizes “change address,” it may misinterpret the request or prompt the user to rephrase, leading to frustration.

In customer service scenarios, where ambiguity, follow-up questions, or noisy input are common, traditional chatbots often lose track of context, leading to generic or unhelpful responses. When they fall short, users are frequently redirected to human agents, defeating the purpose of automation.

Conversational agents overcome these challenges by understanding intent beyond fixed commands and dynamically retrieving and generating responses based on context. Unlike scripted flows, they adapt in real time, enabling more natural, intelligent interactions across various domains. They can also refine noisy queries, correct typos, and rephrase ambiguous questions—ensuring users receive relevant responses even when their input is unclear or imperfect.

If you’ve used LLMs like ChatGPT, you’ve already seen how generative AI enables chatbots to move beyond scripted interactions. Unlike traditional chatbots, LLMs don’t rely on predefined responses. Instead, they generate answers dynamically by drawing from a vast, general knowledge base built from extensive training data. This makes them highly versatile and well-suited for open-ended conversations.

However, LLMs have a fundamental limitation: they lack inherent knowledge of your organization’s proprietary data unless explicitly provided. While they can generate well-structured responses, their answers may not always be accurate or contextually relevant for business-specific scenarios.

This poses a challenge in fields like law, finance, healthcare, or product support, where precise and reliable information is crucial. Simply put, without access to relevant business data, an LLM risks providing responses that are generic, outdated, or even incorrect. This is where Retrieval-Augmented Generation (RAG) plays a transformative role.

The role of RAG: Grounding AI in business-specific knowledge

RAG enhances LLMs by integrating real-time, domain-specific data into their responses. Instead of relying solely on pre-trained knowledge, RAG-powered conversational agents retrieve relevant information from structured and unstructured sources—such as internal databases, product catalogs, policy documents, CRM systems, or regulatory guidelines—before generating an answer. This approach ensures that responses are grounded in accurate, up-to-date information, making AI-powered interactions far more reliable and business-aware.

By combining retrieval (fetching the most relevant information from trusted sources) with generation (using LLMs to craft a coherent response), RAG significantly improves the accuracy, relevance, and contextual understanding of AI-driven conversations. Whether assisting customers with product-specific queries or supporting employees with policy-based guidance, RAG allows businesses to harness AI without compromising on factual correctness.

This transformation turns them from general-purpose chatbots into powerful, enterprise-ready assistants that support internal teams with knowledge management, compliance, employee self-service, and decision-making. By connecting to business-specific data, these agents help employees quickly find accurate information, streamline workflows, and improve efficiency—whether it’s HR answering policy questions, legal teams navigating compliance, or IT troubleshooting technical issues.

The business benefits of an AI conversational agent

AI-powered conversational agents aren’t just smarter chatbots—they’re a way for businesses to enhance customer interactions, optimize workflows, and unlock new opportunities. By providing context-aware, dynamic conversations, they help organizations improve responsiveness, personalize user experiences, and scale support capabilities.

Higher customer satisfaction & engagement

Conversational agents provide natural, intuitive interactions, reducing frustration and improving overall user experience. Unlike rule-based chatbots that rely on rigid workflows, AI agents understand intent, handle multi-turn conversations, and adapt to varied phrasing, leading to more meaningful and effective interactions.

Faster, move efficient responses

AI conversational agents help businesses respond instantly to customer queries, ensuring that users get relevant answers without long wait times. Whether addressing customer service inquiries, internal employee support, or product-related questions, AI agents streamline access to information, enhancing productivity and responsiveness.

Scalable & consistent support

Traditional chatbots often struggle to handle complex or high-volume interactions, leading to inconsistencies in response quality. AI conversational agents maintain accuracy and reliability across multiple conversations, ensuring that users receive consistent and high-quality responses regardless of demand. This scalability is particularly valuable for businesses that need to provide support across multiple channels and time zones.

More accurate & personalized responses

LLM-powered conversational agents go beyond pre-scripted answers, tailoring responses to the specific query and context. When combined with Retrieval-Augmented Generation (RAG), these agents access real-time, organization-specific data, ensuring their responses are both contextually relevant and factually accurate. This capability is especially important in finance, healthcare, and legal sectors, where precision matters.

Actionable insights through data and analytics

Conversational agents capture valuable interaction data, helping businesses better understand customer needs, identify common pain points, and refine engagement strategies. These insights can be used to enhance products, improve knowledge bases, and optimize communication workflows, driving continuous business improvement.

Enhance accessibility & inclusivity with multi-modal & multi-lingual conversational agents

Modern conversational agents are not limited to text-based interactions - they leverage multi-modal capabilities to create more natural and accessible experiences. With speech-to-text integration, users can engage through voice commands, enabling hands-free interactions that are especially useful in mobile, customer service, or assistive technology contexts. Additionally, image-based inputs allow users to upload or reference visuals, enhancing support for tasks like troubleshooting, document processing, or product inquiries.

Beyond different input modes, multi-lingual support allows conversational agents to understand and generate responses in multiple languages, breaking down communication barriers. Whether assisting global customers, multilingual employees, or international business operations, these agents can dynamically translate, interpret, and respond in a user’s preferred language—ensuring seamless, contextually relevant conversations across regions and cultures

Conversational agents do more than automate responses—they enhance communication, improve information accessibility, and support smarter decision-making. By integrating LLM-powered AI with retrieval-based intelligence, businesses can deliver more relevant, engaging, and effective interactions across their customer and internal support channels.

Introducing a conversational agent to your business – step-by-step

Step 1: Define your business goals & use cases

Before implementing a conversational agent, clarify its role in your organization and the specific challenges it will solve. Consider:

• Who will use it? (Customers, employees, partners?)
• What types of queries should it handle? (Customer support, internal knowledge sharing, lead generation?)
• What business outcomes do you expect? (Faster response times, improved engagement, reduced operational costs?)

Example Use Cases:

• Customer Support: Answering product inquiries, FAQs, and troubleshooting.
• Sales & Marketing: Engaging website visitors, qualifying leads, and answering pre-purchase questions.
• HR & Employee Support: Assisting with HR inquiries, onboarding, and internal knowledge management.

Step 2: Select the right AI model and technology

Conversational agents vary in complexity. Choosing the right AI model depends on your business needs:

• Pre-built vs. Custom AI: Do you need a fully customized solution trained on your company’s data, or will an off-the-shelf model suffice?
• LLM Selection: Options include GPT-based models, open-source alternatives or industry-specific AI models.
• Retrieval-Augmented Generation (RAG): If precision is critical (e.g., finance, healthcare, legal), RAG ensures the AI pulls real-time, accurate data rather than relying on static knowledge.

💡 Tip: Businesses with compliance requirements may need AI fine-tuning, added security layers, or response moderation.

Step 3: Integrate business-specific knowledge

A conversational agent is only as good as the information it has access to. Ensure it understands your company’s policies, products, and processes by:

• Connecting to Internal Knowledge Bases: Upload company FAQs, product catalogs, policies, and training materials.
• Using RAG for Real-Time Information Retrieval: Keep responses accurate and up-to-date by integrating with live data sources.
• Enabling Context Awareness: Ensure the AI maintains conversation history and understands follow-up questions for a more natural dialogue.

💡 Start small. Focus on high-impact data sources first, then expand as needed.

Step 4: Choose deployment channels & user experience

Your AI should be easily accessible where users already engage. Decide which platforms make the most business sense:

• Website Chat: Embedded AI chatbot for customer inquiries.
• Messaging Apps: Integrated with WhatsApp, Slack, Microsoft Teams, or Facebook Messenger.
• Voice Assistants: AI-powered voice bots for phone support or smart devices.
• Enterprise Systems: Embedded into CRM, HR portals, or internal dashboards for employee productivity.

💡 Tip: A multichannel approach ensures wider adoption, but start with the most valuable platform first.

Step 5: Enable multi-modal & multi-lingual capabilities

For broader accessibility and improved engagement, consider:

• Speech-to-Text Integration: Enable users to interact using voice, improving hands-free accessibility and efficiency.
• Image-Based Inputs: Allow users to upload images for troubleshooting, document processing, or product identification.
• Multi-Lingual Support: Support multiple languages dynamically, enabling seamless communication with global customers and employees.

These capabilities make AI-driven interactions more inclusive, flexible, and intuitive across different user preferences.

Step 6: Test, optimize & improve before full deployment

Pilot your conversational agent with a small group before rolling it out broadly. Key testing considerations include:

• Accuracy Testing: Does it understand and respond correctly?
• User Experience: Is the conversation smooth, engaging, and natural?
• Failure Handling: Can it manage ambiguous queries or out-of-scope requests?
• Security & Compliance: Are sensitive data and user privacy safeguarded?

💡 Tip: Gather real user feedback and use continuous learning techniques to refine performance.

Step 7: Monitor performance & continuously improve

Once deployed, regularly track key performance metrics to ensure ongoing success:

• Response Accuracy: Are answers relevant and factually correct?
• User Engagement: Are customers and employees satisfied with the interaction?
• Resolution Rates: How effectively does the AI solve queries without human intervention?
• Escalation Trends: What types of issues still require human support?

Leverage AI-driven analytics to identify common inquiries and optimize both the agent’s responses and broader business processes.

A well-implemented conversational agent can enhance customer experiences, improve efficiency, and provide strategic business insights. Start small, iterate often, and let AI enhance - not replace - your customer and employee interactions.

Challenges and limitations of conversational AI

AI-powered conversational agents have transformed how businesses engage with customers and employees. However, to maximize their effectiveness, organizations must address key challenges related to accuracy, security, user trust, and compliance. With the right approach, these risks can be mitigated to ensure reliable and responsible AI adoption.

Accuracy & hallucinations

Conversational AI can sometimes generate incorrect or misleading information, known as hallucinations. This happens when the model fills in gaps with plausible but false details, which is a critical risk in industries like finance, healthcare, and legal services.

🔹 How SoftwareOne can help: Our expertise in knowledge-driven AI solutions enables businesses to enhance AI reliability by seamlessly integrating proprietary data sources, ensuring responses are both accurate and contextually relevant.

Security, privacy & compliance risks

AI agents processing sensitive user data pose potential privacy and compliance risks. Unregulated data access or improper handling of confidential information can lead to security concerns.

🔹 How SoftwareOne can help: We help businesses align AI deployments with security and compliance standards, ensuring that conversational agents meet regulatory requirements while protecting user privacy.

Bias & ethical considerations

LLMs can inherit biases from their training data, which may result in unintended stereotypes or discriminatory responses. These biases can affect decision-making and user trust.

🔹 How SoftwareOne can help: Our AI frameworks emphasize ethical AI deployment, providing businesses with tools to identify, minimize, and manage bias risks effectively.

User adoption & trust

People may hesitate to rely on conversational AI, especially if they have had poor past experiences with chatbots. Gaining user trust requires demonstrating reliability and value.

🔹 How SoftwareOne can help: We work with businesses to design user-centric conversational AI experiences, ensuring AI interactions are intuitive, effective, and aligned with customer expectations.

Conversational agents examples

It’s time we turned to our AI conversational agent examples.

Empowering sales reps with real-time product recommendations: Enhancing efficiency and expertise in the field

The first is for a manufacturer of industrial oils and chemicals that wanted its sales reps in the field to have better oversight of its products.

A sales rep visits a prospective client, a manufacturer of paints and dyes that needs certain types of solvents for its production line. The rep “consults” the conversational agent by inputting the manufacturer’s detailed requirements. Note that the chatbot supports voice input which for a sales rep on a factory floor or in his car will often be the best solution.

Based on the initial scenario the agent is designed to probe further questions to ask before it generates its product recommendation(s). These can be given to the manufacturer there and then, without the usual, “I’ll get back to you”.

In this use case, the conversational agent accelerates the buying process and positions your sales reps as true experts. Conversational tool for an Australian university: Enhancing student and staff interactions with real-time answers.

Introducing a conversational agent into a university setting

The second use case is an AI conversational agent tool that SoftwareOne has developed for a university in Australia. The same agent serves both staff and students but consults discrete data sets to do so. It is critical that students don’t have access to staff documents, and the conversational agent sets restrictions based on your log-in or the subject studied.

For students, the tool finds answers to the type of questions that would traditionally be addressed to teachers. When is my next exam? What is the curriculum of this specific subject? Can I replace one of my modules with another module? Can I do it next year? It is distracting and time-consuming for teachers to reply to these queries – every day, from potentially hundreds of students.

All the information is out there – in the course prospectus, in timetables, on the university or campus website, in SharePoint – but these sources tend to be confusing and unwieldy. The result is that the student messages her teacher who won’t have all the answers at her fingertips either.

The agent achieves two things: students get clear and comprehensive answers to their questions, and staff are left alone to do their real jobs. 

Teachers have their own questions of course – about curriculum updates, marking and exam re-sit criteria, student welfare guidelines, and so on. They can also use the conversational bot to help them plan lessons or prepare course material.

The university which has launched this conversational agent attracts many students who do not speak English as their first language. Conversational agents are towers of Babylon where a multitude of languages is spoken and understood. You can pose your question in Cantonese or Bahasa Indonesia (written or spoken), and the AI agent will reply in the language of your prompt – also in near-real time. Conversely, you can ask the question in English, and set the tool to reply in another language, if this helps the student.