Client Success Story

A GPT-Integrated AI Bot for an Aviation Parts Supplier

50%

Reduced Support
Calls

40%

Faster Response
Times

98%

Matching
Accuracy

Service

  • GPT Integration Services
  • AI/ML Development Services

Platform

  • AWS
  • AI/ML
  • Claude 3.5 Sonnet
The client

A Leading UK-based Aviation Parts Supplier

Our client is a UK-based aviation parts supplier, serving over 150 airlines and maintenance facilities worldwide. They deal in critical components for both commercial and private aircraft, providing everything from high-demand engine parts to specialized avionics. Their inventory spans thousands of such components across a vast logistics network.

THEIR CHALLENGE

Delays in Delivery, Frequent Shortages, and Huge Volume of Support Calls

Our client was facing increasing pressure from airlines due to delays in delivery, inaccurate part availability statuses that were resulting in outages and lost revenue, and a high volume of support calls. Their maintenance teams were struggling with this surge in customer service inefficiencies and inventory mismanagement.

  • Overwhelming volume of support calls: Airlines bombarded the support team with routine inquiries on part availability, compatibility, and order status.
  • Manual inventory checks: Mapping part requests to inventory for availability confirmation was time-consuming and error-prone, resulting in frequent supply shortages.
  • Delayed part replacements: Slow response times in matching with inventory and dispatching replacement parts led to client dissatisfaction and increased downtime for airlines.
  • Scaling operations: As demand grew, the client struggled to scale their operations with the existing headcount or operational costs.
THE REQUIREMENT

An Intelligent System to Streamline Both Customer-Facing Support and Internal Inventory Management

To address these challenges, our client needed a solution that could:

  • Provide an instant, self-service option for airlines to check part availability, compatibility, and order status.
  • Integrate with their IMS, customer-facing website, and inventory systems to provide unified access to data (requests, inventory levels, replacements, and other inquiries) for process automation.
  • Handle increasing customer inquiries without requiring a proportional increase in support staff.
OUR SOLUTION

A Claude 3.5 Sonnet-Integrated Bot

We designed and implemented an intelligent, multi-channel GPT-integrated solution powered by Claude 3.5 Sonnet. This AI solution was integrated with the client’s existing CRM as well as inventory management and order tracking systems, addressing both customer-facing inquiries and internal inventory challenges.

Workflow

Workflow
1

Gathering Requirements

We spoke to the client’s operations and support teams. They were handling an average of 500+ part inquiries per day, with only a few support agents managing the entire load.

  • Identified some delays in their inventory management systems: slow query processing, an unoptimized catalog, and no real-time access to accurate data.
  • Additionally, inventory updates were not reported in real-time, so parts could appear available in the system even if they were already out of stock or awaiting restock.

Based on this groundwork, we mapped customer interaction flows, detailing how airline maintenance teams typically receive requests, search for parts, track orders, and inquire about part compatibility.

2

AI Model Selection & Fine-Tuning

  • Our team selected Claude 3.5 Sonnet for its advanced natural language processing (NLP) capabilities. We trained the default model on aviation-specific terminology and part descriptions.
  • To further enhance accuracy, we fine-tuned it using historical data from the client’s catalog, with over 3,000 parts and associated details like part numbers, categories, and typical customer queries. When a request is received (e.g., "Is part X123 available?"), The system identifies key details (part number, category) and matches the request to inventory in real-time.
3

Bot Design & Integration Planning

  • Scalable Architecture Design: We designed a multi-layered architecture to ensure the bot could handle increasing loads efficiently.
  • API-First Development & Integration: Used RESTful APIs to connect the bot to back-end layers (Claude 3.5 Sonnet, data layer, UI, response layer, etc.). Utilized Amazon API Gateway to securely manage API calls between the bot and the client’s information management system (IMS).
  • Serverless Cloud Deployment: Deployed the bot on Lambda (AWS) for scalable, serverless performance.
  • Real-Time Data Fetching: We integrated the existing IMS with Amazon RDS/DynamoDB for targeted data engineering. This data was made accessible to the bot using custom-built data layers and ETL/ELT pipelines.
  • Guided Access: Implemented AWS IAM for role-based access control and SSL/TLS encryption to ensure safe data transfer and safeguard sensitive information during API calls and user interactions.
4

Testing & Continuous Improvement

  • We conducted load testing to ensure the system could support high-traffic periods (e.g., the peak maintenance season) and handle up to 1,000 inquiries per day without degrading performance.
  • Our experts also implemented a structured feedback loop to track and analyze user interactions, thereby improving performance. For example, if a part request was frequently misinterpreted or led to incorrect responses, the system logs this as feedback data. After reviewing the misinterpretations and errors, this data was used to retrain the model for enhanced accuracy and precision.

Key Features and Future Enhancements

FAQ Bot

We trained the bot on an additional set of FAQs, like:

  • “Do you have part Y456 for the Boeing 737?”
  • “Will part B234 fit in my engine model C567?”
  • “How long will it take to ship part X999 to London?”

Voice-to-Chat Transition

For on-call inquiries, the system smoothly transitions voice queries to chat, enabling the bot to process them.

Multi-Channel Access

The bot was available on multiple channels, including the client’s website, their dedicated mobile app, and popular messaging platforms such as WhatsApp.

Future Enhancements

The solution was designed with scalability in mind and can easily incorporate Agentic AI capabilities in the future. This will enable the bot to take on more proactive, autonomous tasks. For example, the bot could automatically trigger restocking requests and dispatch orders once inventory levels hit certain thresholds. Additionally, it could autonomously place part orders on behalf of customers (based on their historic purchase patterns and requirements), further streamlining the process and reducing manual intervention.

Technology Stack

Conversational AI

  • Claude 3.5 Sonnet

Serverless Architecture

  • AWS Lambda

API Management

  • Amazon API Gateway

Data Layer

  • Aws Rds Streamline Icon: https://streamlinehq.com
    AWS RDS / DynamoDB

Security

  • Aws Iam Streamline Icon: https://streamlinehq.com
    AWS IAM
  • ssl
    SSL/TLS Encryption

Project Outcomes

50% reduction in routine support calls within 3 months of deployment

40% faster response times for part requests and availability checks.

98% sourcing accuracy in inventory checks and order tracking

CONTACT US

Looking to Enhance Customer Support with Conversational AI Capabilities?

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