Travel AI Agent Development: How AI Reduced Complex Contract Processing from Days to Minutes
From document-heavy operations to searchable operational intelligence.
Executive Summary
A North American aviation services company with over 240 offices spread across the globe, over 45,000 employees, and operations in the USA, Canada, UK, Ireland, and the Netherlands faced an operational problem due to its legal team having to spend hundreds of hours annually extracting, structuring, and searching information from complex collective bargaining agreements.
These documents could run up to 100 pages, have legal and industry-specific terminology and be in scanned, inconsistent, or poor-quality PDF format.
Following several failed attempts at creating an AI solution through various proof-of-concept projects, the organization used a multi-layered approach that involved the use of Microsoft Copilot Studio, Power Platform, Power Automate, AI Builder, SharePoint, Dataverse, and Power BI.
The end result is an AI system capable of processing contracts with up to 200 pages and 50 MB, extracting relevant data from them, structuring and organizing it into a search-friendly dataset.
When Growth Made Manual Contract Intelligence a Business Constraint
The organization had expanded rapidly. As per the source, after the rebranding in 2020, it went up from an income of about $450 million and 10,000 employees to over $2 billion in income and over 45,000 employees.
The expansion led to a significant increase in the volume and complexity of the contracts information that needed management.
For a travel or aviation organization, this type of problem extends beyond legal teams. Large volumes of agreements, operational documents, supplier information, policies, airport details, service requirements, and other business records can become difficult to search as the organization scales.
This is where a Travel AI Agent can move beyond simple question answering and become an operational knowledge layer.
Why Conventional AI Proofs of Concept Were Not Enough
The organization had already experimented with several AI-driven approaches.
The problem was document complexity.
Its agreements frequently exceeded 100 pages and contained specialized legal and industry terminology that varied between contracts. Early AI projects struggled to consistently interpret the information.
One of the organization’s leaders explained that asking an agent the same question twice could produce different answers, making accuracy and consistency insufficient for production use.
This is one lesson that organizations looking at a travel AI agent development project need to keep in mind: Just linking a large language model to some documents does not suffice.
There is a need for extracting, structuring, enriching, and managing the information before the retrieval process can occur.
A Multi-Stage AI Architecture Instead of a Single Agent
The company deployed a multi-phase AI system using Copilot Studio and Power Platform in the end.
Instead of expecting one AI agent to complete all the tasks, the different models and automations took care of particular phases in the process.
The process begins when a contract is uploaded to SharePoint.
Power Automate then triggers AI models built with AI Builder to:
The extracted information is stored in Dataverse, creating a searchable contract knowledge repository.
The AI-driven agent can then retrieve information through natural-language queries.
For IT leaders, the significance is architectural: the AI agent was not treated as an isolated chatbot. It was connected to an automated data-processing pipeline and structured knowledge repository.
For business leaders, that architecture translated into faster access to information and less manual document work.
Turning Hard-to-Process Documents into AI-friendly Material
One of the major technical difficulties lies in the quality of the documents themselves.
Many of the organization’s PDFs were scanned images, some of which were either blurred or misaligned and therefore difficult to interpret.
According to the source, AI Builder’s text recognition model had perfect accuracy compared with manual extraction in the organization’s tests.
Moreover, the team used prompts from Power Automate to add deterministic elements to the process of extraction. For instance, when geographic data were required, it was clear that a particular location might not need only a city name, but also an airport code.
In other words, here is a useful approach to the creation of custom travel AI agents: domain knowledge can be used along with generative AI.
What Changed After Deployment
The organization moved from a manual document-processing workflow toward an automated knowledge workflow.
Legal employees no longer needed to spend the same amount of time manually extracting information before using it. Contracts could be uploaded to SharePoint, processed automatically, structured in Dataverse, and searched through an AI agent.
The organization also avoided purchasing specialized off-the-shelf legal software that, according to its Director of Asset Management & Infrastructure, would have cost 30 times more while delivering comparable performance.
The solution was completed and brought live in less than two months, using low-code technologies.
The organization subsequently planned to extend the approach to customer contracts and explore additional uses, including manager queries and financial workflows.
Results Snapshot
| Verified outcome | Why it matters |
|---|---|
| Contract processing reduced from days to minutes | Compresses a previously manual document workflow and accelerates access to contractual information. |
| 100% OCR accuracy in testing versus manual extraction | Demonstrated that even inconsistent scanned documents could be converted into usable digital information. |
| Contracts up to 200 pages and 50MB processed | Shows the system was designed for substantial document volumes rather than short FAQ content. |
| Less than two months to build and go live | Demonstrates the speed possible with the selected low-code architecture. |
| 30× lower cost than comparable off-the-shelf products, according to the client | Indicates a significant potential economic advantage for the organization’s specific use case. |
| Hundreds of hours of annual manual processing addressed | Creates capacity for employees to focus on higher-value strategic work. |
What Travel and Aviation Leaders Can Learn
The case provides several practical lessons for organizations evaluating an AI Agent for travel.
If your travel or aviation organization is spending significant time searching documents, answering repetitive operational questions, or moving information manually between systems, a custom travel AI agent development approach can help identify which workflows are suitable for automation.
Start with one measurable process, verify data quality and accuracy of responses, build it into the system that employees are using, and then scale it up as the business case proves itself.
Ready to Build Your Travel AI Agent?
Transform customer support and employee service with a secure, integrated, and scalable AI Agent.
Ready to Build Your Travel AI Agent?
Transform customer support and employee service with a secure, integrated, and scalable AI Agent.
