In the fast-paced digital landscape, enterprises relied heavily on efficient image recognition services for numerous tasks—ranging from streamlining workflows to enhancing customer experiences. With several powerful computer vision service providers, such as Blippar, CamFind, Wolfram Language, Clarifai, Microsoft Cognitive Services, and TinEye, organizations faced the challenge of integrating and managing these systems for optimized performance. To address this, we developed an MVP of a federated layer for enterprise image recognition queries.
Federating Computer Vision Services
This federated layer acted as an intelligent intermediary, allowing enterprises to query multiple computer vision services either simultaneously or selectively. Instead of being tied to a single provider, businesses were able to leverage the strengths of different platforms for specific tasks—one might have excelled at object recognition, while another was ideal for facial analysis or reverse image searches.
Platform, Testing, and QA Tools
The MVP was designed to be a flexible and scalable platform. It included:
Core Platform: The core system enabled seamless API integration with top image recognition service providers like Blippar, Clarifai, and Microsoft Cognitive Services. Testing Tools: Automated tools benchmarked each service provider’s performance across various use cases and image types, offering enterprises a clear understanding of which service to use for specific needs. Quality Assurance (QA) Tools: Tools monitored service consistency and accuracy, helping enterprises maintain high standards in image recognition workflows.
Flexibility and Scalability
We adopted a subscription-based business model, where enterprises paid for access to the federated layer and its associated testing and QA tools. The system saved time, reduced integration complexity, and improved the overall accuracy and efficiency of image recognition tasks, particularly in the retail space.