Bringing Visibility to a Hospitality Platform with an AI-Driven Knowledge Graph

Interlace helped accelerate development by making product logic, dependencies, and system relationships easier to explore. Instead of manually searching across repositories and documentation, engineers could ask questions such as:

• What user flows depend on this API endpoint?

• What could break if I change this backend file?

• Where is the business logic for revenue planning implemented?

 

This allowed developers to locate relevant files faster, trace implementations across the stack, assess the impact of changes before coding, uncover hidden dependencies during refactoring, and onboard into complex product areas significantly faster than through manual investigation alone.

The client also found broader applications for Interlace beyond developers, exploring how other technical and business-oriented specialists could gain better visibility into the system through natural-language questions rather than relying on fragmented documentation or constant support from engineers.

 

Using prompts, product managers could scope changes more confidently and assess risks earlier, project managers could better understand rollout dependencies, and support teams could investigate issues faster and improve ticket quality — all while collaborating through a shared understanding of the product architecture. 

 

Interlace showed the potential to revive and scale the client’s automated testing initiative, which had previously stalled. The key value of the graph was its ability to map high-level case descriptions provided by the manual QA team to the corresponding user flows and underlying codebase. This enabled AI agents to automatically generate more than 1,000 tests overnight — all aligned with actual business logic.

 

The process demonstrated that tests could be produced quickly while staying highly relevant to business requirements — even without deep technical specifications or extensive manual input.