Aray Maratova@araym

Sarra is an adaptive Q&A system built on a Retrieval-Augmented Generation (RAG) architecture and integrated with IBM watsonx for enterprise-grade orchestration and governed model management. It uses a real-time feedback loop that continuously improves answer quality by learning from user evaluations and refining retrieval relevance over time. Sarra is delivered as a headless API, allowing seamless integration into websites, mobile applications, banking systems, and any service that relies on AI assistants. Many organizations today, banks, government portals, telecom companies, and customer-service platforms, still rely on script-based chatbots. These bots provide predefined, template-style answers that fail to adapt to different user intents or context. As a result, users often receive identical responses to distinct questions, leading to lower trust and poor service quality. The challenge becomes even more significant as enterprise data changes frequently. Regulations, tariffs, service procedures, internal policies, and product details are constantly updated. Traditional fine-tuning approaches cannot keep up: they are expensive, slow, and unsuitable when information changes weekly or even daily. While RAG systems allow dynamic retrieval from updated knowledge sources, existing implementations rarely incorporate user feedback. They do not learn from mistakes, cannot adjust retrieval ranking automatically, and lack mechanisms to validate and integrate feedback into their pipelines. Sarra addresses these gaps by introducing a continuous learning cycle. The system collects user evaluations, analyzes their reliability, classifies the type of correction, and uses validated feedback to refine retrieval results and improve the ranking of relevant documents. This allows the system to adapt immediately to new information without retraining the base model. Over time, Sarra becomes more accurate and aligned with real user expectations.

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