Custom AI Chatbot Reduces Daily Customer Support Workload by 80%

About the Client

A book manufacturer specializing in Print on Demand (POD) and short run book production. Since the 1990s, they have maintained industry-trending equipment to provide digitally printed text pages and bound books to clients, with a focus on exceptional quality and sustainable business practices.

  • Industry: Retail & Distribution
  • Headquartered: Chicago, IL
  • Platforms: Anthropic Claude
  • Team Size: 4
the_client
 

The Challenge

The client, serving a large customer base, would handle all customer inquiries manually through a team of customer representatives. While they already had a portal in place to handle orders and store customer information, repetitive inquiries from customers were still demanding about 5 hours a day of manual effort from each rep.

The client requested a solution that would balance this workload, freeing up their team to focus on other tasks while still maintaining excellent customer service.

 

Our Solution

Here's how we solved the client's challenges:

The Solution

AI Model Selection

For the client’s case, our team chose a chatbot model that delivers natural responses, comprehends questions, and provides accurate answers from a vector-based knowledge base.

  • Qdrant with Ollama for vector database management
  • Anthropic Claude Sonnet 4 for NLP

The Solution

Development Assistant

The team implemented the development assistant Anthropic Claude Opus 4 to accelerate the client’s project. This allowed the team to complete tasks ~40% faster and significantly reduced bugs and errors throughout the process.

The Solution

Chatbot Integration

Using the Qdrant Vector Database to store and feed knowledge to the AI, a hybrid search approach combining vector similarity and keyword matching was implemented to retrieve relevant information. Permission-based database lookups were implemented to retrieve customer-specific information like product and order details.

The Solution

Answer Formatting

To present answers in a natural, human-friendly way, the retrieved responses were reformatted using Anthropic Claude Sonnet for better readability and context. The team also added dynamic prompts to handle incomplete queries, i.e., asking for an order number when customers request order details.

The Solution

Security & Quality

Our team used an event-driven approach for the chatbot, which ensures authentication and user permission anytime it retrieves data from the client’s database. Additionally, the team implemented comprehensive logging, analytics, monitoring, and anti-hallucination measures to enforce strict context adherence and accuracy.

Technologies Used

Our software architects and engineers are experts in using the latest technologies.

  • Anthropic Claude (Sonnet 4.0 / Opus 4.0)
    Anthropic Claude (Sonnet 4.0 / Opus 4.0)
  • QDrant Vector Database, Ollama
    QDrant Vector Database, Ollama
  • PHP 8.4
    PHP 8.4
  • Laravel Framework v11
    Laravel Framework v11
  • Restful APIs
    Restful APIs
  • Bootstrap
    Bootstrap
  • JavaScript
    JavaScript
  • HTML5
    HTML5
  • CSS3
    CSS3
  • MSSQL
    MSSQL
 

Business Impact

Implementing a custom AI chatbot produced transformative results for the client.

Impact 1

Decreased manual effort by ~80%

Impact 2

Resolved issues in real time (99% time saved)

Here’s how we did it

  • business impacts

    AI-Driven Development

    Using an AI development assistant, the team was able to handle nearly 160% of their workload without increasing headcount.

  • business impacts

    Continuous Improvement & Safeguards

    Implemented a Chatbot Response Tracking Mechanism to identify unanswered or inaccurately answered queries.

  • business impacts

    Customer-Centric Model

    Improved customer satisfaction by delivering real-time responses – decreasing turnaround times from 4-8 hours to ~2 minutes.

  • business impacts

    Relevant Inquiries

    Our team worked closely with the client to develop a comprehensive list of FAQs for the chatbot to reference.

we-did-it

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