We implemented an automated spin generation solution for the customer using a straightforward image processing approach. By leveraging a set of pre-captured product images, we were able to automatically generate a 360-degree spin view of the product—eliminating the need for manual intervention in the spin creation process. Rather than relying on complex or resource-intensive machine learning models, we opted for a simpler, more efficient image processing pipeline. This not only reduced the development time but also significantly lowered computational and operational costs. The solution is lightweight, scalable, and effective, providing a seamless experience for both the customer and their end users. Overall, this approach streamlined the product visualization workflow, minimized human effort, and delivered high-quality spin outputs while optimizing for performance and resource efficiency.

Our client faced a time-consuming challenge: manually annotating product images with corresponding dimensions. This traditional process not only consumed hours of valuable time but also increased the risk of human error and inconsistency. Leveraging our expertise in advanced image processing and automation technologies, we developed a custom solution that automatically overlays product dimensions onto images with pinpoint accuracy. The result? A seamless, scalable, and cost-effective system that reduced processing time from hours to mere seconds. Our automated dimensioning solution empowers businesses to streamline product cataloging, enhance visual communication, and significantly cut down operational costs — all while maintaining high precision and consistency across thousands of product images.

To streamline and enhance customer interactions, our client sought a comprehensive AI-driven support solution capable of handling a wide range of customer queries — from general company information to product-specific details and technical issue resolution. In response, we developed a robust multi-agent customer support system, leveraging the power of modular AI architecture. This system comprises three specialized agents, each fine-tuned for distinct functions: General Q&A Agent - Handles company-related inquiries and frequently asked questions with contextual awareness. Product Info Agent - Provides accurate and up-to-date details about the client's product offerings. Issue Resolution Agent - Assists users in troubleshooting and resolving technical or service-related problems. The entire solution was built using open-source LangGraph orchestration, combined with Gemini LLM APIs for natural language understanding. The deployment was carried out on the client’s existing Kubernetes infrastructure, ensuring scalability, flexibility, and full control over the environment. A major focus of the implementation was minimizing hallucinations and enforcing strict security guardrails. We implemented custom filters, verification layers, and fallback mechanisms to ensure that the AI agents deliver only accurate and verified information, enhancing customer trust and system reliability. This multi-agent system not only reduces response times and improves resolution rates but also elevates the overall customer experience through intelligent automation — all while maintaining enterprise-grade security and accuracy.

A customer needed a scalable, low-cost solution to process AI jobs whenever new products were added to their portal. These jobs were infrequent, so a traditional infrastructure would be too costly. Additionally, they wanted to offer some AI features to their clients as a SaaS product. Our Solution: We designed a fully serverless SaaS extension on AWS that is both scalable and cost-effective. By leveraging AWS Lambda, API Gateway, DynamoDB, and S3, we created an event-driven architecture that processes jobs on-demand, eliminating idle resource costs. Key Features: Event-Driven Processing: New product events trigger AI job workflows using AWS services. Serverless & Scalable: No servers to manage; resources scale automatically with usage. Multi-Tenant SaaS Design: Secure and isolated support for multiple customer tenants. Pay-per-Use: Minimal costs during inactivity, with automatic scaling during demand spikes. Outcome: The customer now benefits from an efficient, low-maintenance solution that supports AI job processing and generates new revenue through a SaaS model — all with minimal operating costs.