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AI chatbot for an eLearning SaaS platform

A SaaS eLearning platform wanted to automate their customer support. We developed a chatbot that takes care of routine tasks and helps support specialists handle complex tickets.

Data science & AI
Icon 1USA
Icon 2eLearning
Icon 3Since 2024

  • Summary

    This case study explains how we built an AI chatbot for an eLearning SaaS platform to automate customer support. The solution uses a large language model trained on support documentation and historical inquiries to answer routine questions and assist support agents with complex cases. In this material, we describe the challenges the client faced, how the chatbot was implemented, and what results it delivered.

Challenge

At the beginning of the school year, there are huge spikes in customer support workload. During summer, there’s little work for the support department. The client had to keep inflated staff year-round because it takes time to train the support specialists. They wanted a scalable solution, instead.

The platform encountered several support tickets:

  • Level 1 support

    The client had lots of routine inquiries, many came from users who needed basic guidance. Such tasks took too much time.

  • Level 2 support

    Complex cases without pre-scripted responses, often dealing with unexpected issues.

Requirements

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    Decrease case resolution time for both support levels.

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    Minimize escalation risks by providing comprehensive responses to customer queries.

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    Reduce training duration for support reps.

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    Develop a scalable solution that can handle a high query during peak times.

Solution

Here’s how we built the LLM-based chatbot as part of our AI chatbot solutions development services:

  • Requirements assessment

    To find automation opportunities, we dived into the customer service workflow. We found the pain points and looked at the most common inquiries.

  • Deployment & monitoring

    We deployed the chatbot across the platform and integrated it into the customer service workflow. With user feedback, we continue to improve the model after launch.

  • Design & development

    We trained the model on platform manuals and real-life support inquiries. As a result, the chatbot can handle complex eLearning issues.

  • Level 1 support

    The chatbot takes care of routine questions. We automated the first line of support. When human input is needed, the model helps them with the answer.

  • Level 2 support

    Even in complex cases, the chatbot assists the support specialists. It guides them even when there are no scripted answers. This helps minimize escalation to the developers.

Screenshots

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Tools & Technologies

Python
Node JS
Flask
LlamaIndex
Mistral 7B
Bash
Gitlab
Docker
AWS API

Team

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    Data scientist x1

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    Back-end developer x1

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    Business analyst x1

Results

Here’s how our client benefited from AI chatbot solutions implementation services:

  • Drastically reduced resolution time

    for Level 1 and Level 2 support cases.

  • Faster training period

    for support staff.

  • 24/7 support with instant assistance

    leads to reduced customer churn.

  • No need to keep extra staff

    year-round just for the spikes in several months. The customers get instant assistance even in the highest spikes.

  • Minimized conflict escalation

    The model generates detailed and contextually relevant answers, so there’s little risk of inadequate support responses.

Project insights

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    Automation worked best for repetitive support questions

    Many users asked similar questions about platform navigation, login issues, and course access. Once the chatbot was trained on documentation and previous tickets, it quickly handled most of these requests without human involvement.

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    The chatbot became a support assistant, not just a support replacement

    Instead of replacing the support team, the system helped specialists respond faster to complex tickets. Agents used the chatbot’s suggested responses and troubleshooting steps when dealing with unusual cases.

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    Early responses were sometimes too generic

    At first, the chatbot occasionally produced answers that were correct but not specific enough. Improving the training data and adding real support cases helped the model generate more accurate and practical responses.

Key takeaways

Key takeaways

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Train AI on real support conversations.
Historical tickets and real user questions improve response quality.

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Use AI to assist agents, not only customers.
Chatbots can speed up work for support specialists by suggesting answers and troubleshooting steps.

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Plan for continuous improvement.
User feedback and monitoring help refine the model after deployment.

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Design for scalability.
Support automation should handle sudden traffic spikes without affecting response times.

Contact our expert

If you have any questions regarding the case study or have similar projects to be discussed, do not hesitate to contact us.

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