Translation Memory (TM) systems are core components of commercial computer-aided translation (CAT) tools. However, traditional fuzzy matching methods often fail to retrieve semantically relevant content when surface similarity is low. We introduce SmartMatch, an open-source interactive demo and evaluation toolkit for TM retrieval that connects modern sentence encoders (including LLM-derived representations) and strong lexical/fuzzy baselines with a vector database, and exposes the end-to-end retrieval pipeline through a web-based UI for qualitative inspection and preference logging. The demo allows users to (i) enter a query segment, (ii) switch retrieval backends and embedding models, (iii) inspect top-k retrieved matches with similarity scores and qualitative cues, and (iv) observe end-to-end latency in real time. We provide a reproducible benchmark on multilingual TM data, reporting retrieval quality using reference-based MT metrics (COMET, BERTScore, METEOR, chrF) together with coverage and latency/throughput trade-offs relevant to real-time CAT workflows. On DGT-TM, encoder-based retrieval achieves full coverage (100%) with millisecond-level latency (p50/p90 ≤ 6–20 ms) and attains the strongest semantic-quality scores on the shared query set (e.g., BERTScore up to 0.91 at k=10), while BM25 remains a strong lightweight lexical baseline with very low latency. SmartMatch targets CAT researchers and tool builders and bridges recent advances in sentence encoders with the real-time constraints of translation memory retrieval.