LLM-based Chatbot

Implementing a solution to address high operational costs and human errors in customer support, we leveraged large language models (LLMs) and generative AI. By ingesting and structuring data, fine-tuning models like Llama and Claude, and applying reinforcement learning, we developed an automated pipeline that generates personalized responses. The result was a 350% increase in service capacity with only a 15% cost rise, significantly enhancing customer interactions while optimizing efficiency.

Challenge

The company was managing a large volume of customer and client interactions, relying heavily on a 24/7 support department to answer inquiries, many of which were repetitive and sourced from manuals. This setup led to high operational costs and potential human errors due to work pressure. The company sought an intelligent solution that could provide customized answers to customers based on their inputs while reducing costs and minimizing human error.

Solution

With the rapid development of large language models (LLMs) and generative AI libraries, we leveraged these technologies to build a custom solution. We began by organizing and ingesting their data, including scraping relevant documents in a structured manner. Given the proprietary nature of the data and strict privacy requirements, we conducted extensive evaluations of open-source LLMs, such as Meta's Llama, Mixtral87, and Claude.

To optimize performance, we implemented fine-tuning using state-of-the-art Parameter-Efficient Fine-Tuning (PEFT) models and applied Reinforcement Learning from Human Feedback (RLHF) to further enhance accuracy. We developed an automated pipeline that handled both the training and evaluation of models, dynamically adjusting based on token usage.

The outcome was a highly effective system capable of generating tailored responses for customers based on historical interactions. This solution enabled the company to expand their customer service capacity by 350% with only a 15% increase in costs.

a man riding a skateboard down the side of a ramp

Discover Our Approach

Data Ingestion & Organization

We structured and ingested the company’s proprietary data, ensuring compliance with data privacy standards.

STAGE 1

STAGE 2

LLM Evaluation & Experimentation

After assessing several open-source LLMs (e.g., Llama, Mixtral87, Claude), we identified the best model fits for the company’s needs.

Fine-Tuning and RLHF Implementation

We enhanced model accuracy through fine-tuning techniques and applied RLHF to continuously improve responses based on real-world feedback.

STAGE 3

STAGE 4

Automated Pipeline for Training and Evaluation

An end-to-end pipeline was developed to automate model training and evaluation processes, ensuring efficient and scalable operations.

Customized Response Generation

The system was designed to learn from historical customer interactions and deliver personalized responses, significantly boosting service efficiency.

STAGE 5

Interested in automation?

TALK TO AN EXPERT