Why Building Chatbots for Customer Journeys Is Harder Than You Think

Introduction

In today’s fast-paced digital landscape, businesses are increasingly recognizing the value of chatbots for improving customer engagement, streamlining operations, and driving growth. However, many companies make the mistake of assuming they can easily create their own chatbot solutions. This misconception often leads to wasted resources, subpar results, and missed opportunities.

It’s a Bigger Job Than You Think

Creating a chatbot isn’t just about implementing a feature—it’s about developing a comprehensive application that demands meticulous attention to detail, a robust infrastructure, and seamless execution. Here’s a glimpse into the complexities involved:

UX/UI Design and Functionality

Designing a chatbot involves crafting potential dialogue trajectories, options, and goals to ensure a coherent and engaging user experience. The best chatbot is one which can strike the delicate balance between deterministic, tree-based logic that mirrors your predefined business processes, and the natural, friendly feel of a regular conversation.

Personalization is key. The chatbot needs to adapt its responses with each specific end-user in mind, responding to their pace and patience level. Additionally, the visual elements—chat-box design, chat bubbles, buttons, streaming text, loading icons, and in-chat widgets like drop-downs options and calendars—must be carefully crafted. The functionality of a mature chatbot application extends to handling documents, images, PDFs, signatures, and picture uploads seamlessly.

Integrations with internal and external systems

A high-functioning chatbot must integrate smoothly with various systems:

1. CRM Integration: Fetching and updating customer data directly from the chat.
2. ERP Systems: Ensuring seamless data flow with enterprise resource planning software. You will need to have a smooth pipeline to plan, budget and predict the application impacts your other business processes.
3. Product Tables: Querying tables accurately using an LLM as your Data Analyst is a major task.
4. Transfer to Human Representatives: Facilitating smooth handovers to human representatives when necessary.

Chat Analytics

To measure and optimize the chatbot’s performance, sophisticated analytics which are readily accessible in some sort of administrator’s dashboard is essential. The following are some of the basic items which any company would want in order to track their chatbot’s effectiveness:

1. User Sentiment: Are users calmer with the immediately available chatbot than they would have been on the phone with an agent after a 20-minute waiting period?
2. Success Tracking: Are they making progress with the chatbot, or are they sending one message and leaving?
3. Cost Analysis: How much does it cost you to support the application?
4. User Persona Analysis: For example, are people mocking your chatbot, or are they actually trying to get business done or sign up for programs with it?
5. A/B Testing: How much more interaction do your web pages have now that your chatbot is in place? Which version of the chatbot is the most engaging for customers?

Security, Compliance, and Hallucinations

Ensuring data security and compliance is paramount. This involves:

• Data Validation: Validating data throughout the conversation to avoid errors and ensure accuracy.
• Data Compliance: Following best practices for data security and compliance.
• User Approval: Robustly gathering user approvals and confirmations to ensure compliance and build trust.

There’s Complex Architecture Behind a “Simple” API Call to OpenAI

Many companies have told us, “we’re just going to draft our own prompts and then use that to make our own chatbot.” However, building a chatbot is much more than “prompt engineering”; building a chatbot is a bona fide engineering feat in and of itself.

Simply making API calls to a large language model (LLM) is not enough. Crafting an application which dynamically builds prompts and generates natural but robust responses involves a sophisticated architecture which encompasses data handling, conversational contextual understanding, error management, and rigorous commitment to the application’s source of truth.

For example, imagine you want a chatbot that handles customer inquiries. Beyond just connecting to an LLM, you need systems for tracking conversation history, managing user profiles, integrating with your existing databases, and ensuring data privacy and security. Without this comprehensive framework, your chatbot will likely fall short of user expectations.

To illustrate this point, here is an example of the flow which a chatbot application may go through when the user sends a message:

Step 1: Get User Message

Okay, fine, this part is a bit of a freebie.?

Step 2: Prepare Prompt

Preparing the prompt for the LLM is a multifaceted process:

• Security Checks: The user’s message undergoes rigorous security checks to confirm compliance and prevent issues like jailbreaking or prompt injection. If the user’s message is flagged, your chatbot’s behavior is changed.
• Database Interaction: The message is shipped to the database for record-keeping and to begin the pipeline of user-message analytics.
• System Context Reformatting: The system context is dynamically reformatted. Depending on the broad conversational context and the local context of the user’s message, the system context of the API call should be very malleable. This could involve changing the bot’s persona, adapting to newly identified user details, or transitioning to the next stage of data collection.
• Automated Validation and Preprocessing: If the user has provided new data, the data is validated, preprocessed, and shipped to relevant systems such as databases or CRMs. If the down-payment the user suggested for their loan falls short of 20% of the principal amount, your chatbot will have to know how to deal with that.
• Function Calls: Specific functions may be called to append summaries of API responses to the LLM prompts, such as updating the user’s reward points, eligibility status for some deal, or simply validating that the user does actually have an account with your company.
• Instruction Reformatting: The current instructions to the bot are reformatted based on the ongoing conversation, ensuring the bot’s responses remain relevant and contextually accurate.

Step 3: Send the Prompt to OpenAI

With the prompt meticulously prepared, it is finally sent to the OpenAI API. This step leverages the LLM’s powerful capabilities to generate a response based on the comprehensive and contextually rich prompt.

Step 4: Send the User a Simple Message

The generated response is then sent back to the user. Despite the complexity of the behind-the-scenes processes, the user experiences a seamless and engaging interaction, highlighting the sophistication of the underlying architecture.

Beauty and complexity truly come alive here, as the simplest process of telling an LLM how to respond becomes intricately detailed when fine-tuned for each and every message in a conversation. Building a chatbot involves far more than simple API calls. It requires a robust framework designed to handle security, compliance, dynamic context adaptation, and seamless integration with various systems, ensuring a high-quality user experience.

Trust the Experts

There’s no need to reinvent the wheel.

Insait's team has the experience, knowledge, and proven track record to deliver high-quality chatbot solutions tailored to your specific needs. We understand the nuances and challenges of chatbot development and have refined our processes to ensure success. By partnering with us, you gain access to our wealth of experience, allowing you to focus on what you do best—growing your business—while we handle the complexities of creating a powerful, effective chatbot application.

Cost and Time Efficiency

Developing a proof of concept (POC) for a chatbot can take months, but with our expertise, we can get it up and running in just a month. This rapid time-to-market (TTM) saves you valuable time and resources, allowing you to focus on your core business objectives rather than diverting them towards complex chatbot development.

Unique Features

Our chatbots come with unique features that set us apart from competitors, including integration with a tailored admin page, visibility and update-ability of your knowledge base, and multiple chatbot variants to suit different needs.

Security Expertise

Insait's team includes security experts who ensure that user data is handled with the utmost care, following best practices and compliance standards.

Addressing LLM Issues

Large language models (LLMs) come with their own set of challenges, such as hallucinations and integration with knowledge bases (KB). Designing and maintaining these integrations is not easy. It involves ensuring visibility of what the bot doesn’t know, updating the bot’s knowledge, and handling multiple types of KBs, from product tables and FAQs to industry-specific data like job sub-categories and relevant contact information. Our experience allows us to navigate these complexities effectively.

Cross-Industry Insight

Our broad exposure to diverse use-cases across different industries enables us to provide innovative solutions and best practices learned from various contexts and edge-cases. This experience helps us anticipate potential scalability issues and adapt the application to new use-cases within your company.

Diverse Expertise with Chatbots

We offer a broad skill set that spans business decisions, UX/UI design, and development. Insait's team can handle the DevOps work for you, whether on-premises or as a SaaS solution. This diverse expertise ensures that we can meet all your chatbot needs efficiently and effectively.

Scalability and Flexibility

As your business expands, our AI agent can grow with you. We have experience building versatile agents that can be reorganized and restructured like Lego pieces to suit evolving requirements. Our scalability and flexibility mean we can adapt our solutions to meet your changing needs.

Comprehensive Solutions

We provide end-to-end services, from designing the conversation from a business and UX perspective to seamless integration and ongoing support. Our holistic approach ensures that every aspect of your chatbot is optimized for quality and performance. We have robust testing in place, a dedicated QA team, and extensive experience with best practices, ensuring a high-quality product.

By partnering with us, you gain access to our wealth of experience, comprehensive solutions, and commitment to excellence, allowing you to focus on what you do best while we handle the complexities of creating a powerful, effective chatbot application.

Give your customers the experience they deserve

As we have seen, building chatbots for customer journeys is much harder than you think. And to make it into a mature AI agent like Insait's product

The question is: Do you want to spend time building a complex chatbot application from scratch, or do you want to spend your time perfecting the customer journeys you’re trying to create.

The ball is in your court.?