High-quality annotated data is crucial for NLP, yet manual annotation is costly and difficult to scale in low-resource settings. Large Language Models (LLMs) have demonstrated strong zero-shot and few-shot generalization in NLP tasks, but existing annotation tools either lack LLM support or use LLMs only as one-off pre-annotation engines, without incorporating collaboration or quality control, compromising data reliability. We present BNLP, a text annotation platform that embeds LLM-assisted labeling into a quality-aware collaborative workflow. BNLP treats LLM outputs as intermediate, revisable states and integrates multi-role collaboration, iterative review cycles, and consistency analysis to enable continuous quality monitoring while preserving efficiency gains. BNLP also natively supports AI-ready formats such as Excel and JSON, ensuring seamless data flow from manual annotation to model training. Experiments show that BNLP reduces annotation time by 74.3% and improves annotation quality by 11.6% over purely manual annotation in LLM-assisted settings.