GKB Labs

GKB Labs architected and deployed a production-grade Retrieval-Augmented Generation (RAG) platform for a multinational corporation. Our client faced a significant challenge: crucial institutional knowledge was trapped in vast, siloed document repositories (PDFs, internal wikis, legal filings), making retrieval slow, inefficient, and prone to error. Standard LLMs could not access this proprietary data. We engineered a high-scale solution that ingests, cleans, and vectorizes massive datasets into a centralized vector database. This solved the critical 'siloed knowledge' problem. Employees across the enterprise can now use a secure AI interface to query the authoritative, internal knowledge base. The system retrieves relevant context first and then generates accurate, grounded answers, reducing research time from hours to seconds and ensuring data-driven decision-making.

• AI Prompt Engineer
• Back-End Developer
• Front-End Developer
• +2

GKB Labs designed and built a robust, fault-tolerant backend core for a cross-border payment processing solution for an NY mid-level company. The central problem was horizontal scalability and data consistency during peak load, the existing system struggled with race conditions and transaction timeouts when processing concurrent requests from multiple global regions. We implemented a microservices architecture using an event-driven design (via Kafka/Redis). This solved the concurrency challenge by decoupling transaction services and ensuring asynchronous, guaranteed message delivery. The distributed architecture now processes thousands of concurrent transactions per second across varying regions with near-zero downtime and strict ACID compliance, providing a reliable foundation for global financial operations.

• AI Prompt Engineer
• Back-End Developer
• Front-End Developer
• +2

GKB Labs engineering took over the front-end development of a crucial analytics engine for a quantitative high-frequency trading desk. Our client’s existing UI was suffering from significant rendering lag, data drops, and an inability to handle the sheer volume of rapid market data updates. We rebuilt the interface from the ground up, implementing a high-performance rendering pipeline and a custom WebSocket implementation that optimized message parsing and state updates. This solved the critical problem of latency-induced trading disadvantage. The new interface provides milliseconds-fast updates across complex visualizations (such as depth charts, correlation heatmaps) and maintains perfect synchronization, even during periods of extreme market volatility.

• AI Prompt Engineer
• Back-End Developer
• Front-End Developer
• +2