A GPT-Integrated AI Bot for an Aviation Parts Supplier

Intake Analysis and Journey Design

We worked closely with their operations and customer support teams to isolate high-frequency request types and escalation triggers.

• Documented a baseline load of 400+ part inquiries per day, handled by a small group of agents.
• Diagnosed root causes of delays: slow search performance, catalog structure limitations, and lack of dependable real-time inventory sync.
• Mapped airline maintenance workflows end-to-end, including how they request parts, validate fitment, track shipments, and follow up on replacements.

Domain Adaptation and GPT-Model Training

We chose Claude 3.5 Sonnet for strong intent recognition and multi-turn conversation handling.

• Adapted the default 3.5 Sonnet model to aviation terminology so it could accurately interpret part numbers, categories, and common shorthand used by MRO teams.
• Used historical catalog and support data to train the model further and improve accuracy across 3,000+ parts, covering attributes, classifications, and typical customer questions.
• Implemented structured extraction so prompts like “Do you have X123?” translated into actionable fields and triggered a live inventory and order lookup.

Systems Integration and Cloud Setup

We designed a scalable, service-based architecture to ensure the virtual agent could scale.

• Built secure REST API integrations across the model layer, data services, and front-end channels, using Amazon API Gateway to manage and protect API traffic to the IMS.
• Deployed using AWS Lambda for elastic scaling and cost control.
• Engineered a data access layer backed by Amazon RDS/DynamoDB, supported by custom ETL/ELT routines to keep inventory and order signals query-ready for the bot.
• Enforced security with AWS IAM role controls and SSL/TLS encryption to protect sensitive inventory, customer, and order data during interactions.

QA Testing and Continuous Optimization

We stress-tested the bot to confirm it remained responsive during peak maintenance periods and validated performance at up to 1,100 inquiries per day, with no noticeable latency or stability issues.

To continuously improve accuracy, we implemented a structured feedback loop:

• Logged each interaction with extracted intent, part identifiers, and API outcomes.
• Flagged misinterpretations, failed lookups, and incorrect responses for review.
• Used reviewed error patterns to refine prompts and periodically retrain the model, reducing repeat mistakes over time.