We faced multiple challenges in processing over 1 million tenders, such as handling large volumes of unstructured data with varying formats and languages. Extracting accurate features from this data required overcoming inconsistencies, industry-specific terminologies, and distinguishing between relevant and irrelevant content. Categorizing tenders based on industry keywords with precision was difficult, especially in cases where the same keyword could have multiple meanings depending on context. Ensuring the search engine delivered fast and accurate results was also critical, despite the complex data structure and volume.
We approached the project by first developing a robust data ingestion pipeline that could process and normalize large-scale tender data efficiently. Our team utilized advanced natural language processing (NLP) techniques to extract and standardize features, followed by employing industry-specific keyword analysis and categorization models. We implemented a hybrid AI algorithm combining both rule-based methods and machine learning models for more effective classification and search optimization. Our engineers performed rigorous testing to refine the system’s accuracy and speed, ensuring it met the client’s performance requirements.
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Our AI algorithms efficiently processed the massive dataset, extracting features and categorizing tenders with industry-specific precision. Using Retrieval-Augmented Generation (RAG), the system delivered fast and accurate search results. Continuous updates improved keyword recognition, while multilingual support enhanced global accessibility, resulting in a highly efficient, user-friendly tender processing system.