Overview
Their manual process of sorting applications led to delays, misalignment in matches, and extra staff workload. They needed an intelligent search solution that would improve accuracy and take friction out of the adoption desk.
Challenge
The existing keyword-based search failed to capture adopters’ intent and often returned irrelevant results. Requests for a “calm dog for a senior” and a “low-maintenance dog for a first-time owner” were treated as similar, even though the needs were distinct. A context-aware system was required to improve efficiency and adoption success.
Solution
Using ChatGPT APIs, we built a semantic search model for internal adoption teams. The system analyzes queries by context rather than keywords alone, which allowed:
- Faster match identification — AI-powered recommendations cut search time.
- Reduced manual workload — staff could focus on adopter support instead of filtering applications.
- Improved match accuracy — contextual analysis paired pets with the right adopters.
Approach
- Data optimization — adoption records and historical matching data were processed to train the model.
- Semantic vectorization — pet profiles and adopter requests were converted into AI-readable formats for matching.
- Seamless integration — the model was embedded in the internal dashboard for real-time adoption assistance.
- Continuous learning — the system adapts over time from staff feedback and past adoption outcomes.
Results
Applications
The same matching pattern opened additional uses inside the organization:
- Volunteer placement — assigning volunteers based on skills and availability.
- Donor-sponsorship matching — connecting donors with suitable animal-welfare causes.
Conclusion
By integrating AI-powered semantic search, the platform made internal matching faster and more precise. The work shows how a context-aware model can change day-to-day decision-making in a mission-driven operation.
Want to use semantic search in your own workflow? Talk with our US team, or write to us: