Overview

A multi-billion dollar American multinational airplane manufacturer, which fulfills orders for several hundred aircraft each year, needed to efficiently navigate and query the Federal Aviation Administration (FAA)’s civil aviation safety regulations and certification processes.

Opportunity

The FAA’s extensive regulatory and certification documentation—spanning 1.5 million continuously updated documents—is critical for aviation safety, yet its sheer volume and iterative nature make it difficult to navigate efficiently. Retrieving relevant information often requires time-consuming manual searches through the FAA Document Repository, leading to delays and potential errors—both of which have a high-stakes impact in an industry like aviation. 

This airplane manufacturer wished to let its compliance teams query FAA documents in natural language and receive accurate, context-aware responses, streamlining access to essential information, improving decision-making and user efficiency, and overall enhancing the company’s regulatory compliance with the FAA’s standards. To address this, the manufacturer turned to AI21 Labs to build a custom solution to quickly query the FAA Document Repository—and receive reliable output.

Solution: Conversational Compliance at Scale

The goal of this project is to develop and implement a Conversational Retrieval-Augmented Generation (RAG) system tailored for FAA regulatory documentation, enabling compliance teams to interact with the 1.5 million-document FAA Document Repository using natural language queries, providing accurate, context-aware responses in real time. In addition to general questions, users can ask about differences in regulations over time and receive comparative responses, offering deeper context on evolving standards.

By streamlining access to relevant information and reducing the manual effort required to interpret regulatory texts, the system helps compliance teams make faster, more informed decisions—minimizing the risk of human error and lowering overall compliance costs.

What it looks like under the hood:

Architecture diagram

AI21 Tech & Services Used

  • AI21 Maestro: AI21 Maestro is an AI system for building enterprise-grade knowledge agents that automate high-value, data-intensive workflows—accurately, transparently, and without a massive R&D team. With built-in, state-of-the-art retrieval tools—such as textual and visual parsers, automatic knowledge graph extraction, and optimized vector search—AI21 Maestro agents are able to intelligently select the best search strategy and retrieve the right answer more often than rule-based or LLM-based knowledge agents.  
  • Jamba Foundation Models: AI21’s Jamba Foundation Models leverage their hybrid Transformer-Mamba architecture and 256K context window to efficiently and accurately process large volumes of data, such as regulatory guidelines and technical manuals. As open models, they can be securely deployed in a customer’s VPC or in air-gapped, on-premise environments. 
  • Custom AI Systems: Dedicated teams of experts from AI21 work hand-in-hand with the client at every step of the way, from use case identification to solution architecture, and from data preparation to post-implementation iteration and troubleshooting.