Enterprise AI search which connects to various source systems and internal knowledge base to provide search with better accuracy and relevancy.
- Built new indexing and search system from scratch which connects to various source systems including newly adopted Kontent.ai CMS and stores vectorized documents in Elasticsearch.
- Used SBERT and advanced NLP models for text embedding, text classification, spell check and other NLP tasks.
- Optimized code of indexing and searching using concurrency and multi-threading concepts which reduced the latency of the AI search API by 30-40% and indexing jobs by 50%.
- Increased the overall search relevancy and accuracy by 40% by following recommended strategies and fine tuning data which helped the users find accurate information in search results.