Heterogeneous Parsing & Normalization
Parse and normalize data from CSV, Excel, JSON, and more into a unified, clean format ready for processing.
Transform messy tabular data into clean, structured knowledge for AI-powered reasoning and seamless RAG pipeline integration.
BoxLM bridges the gap between raw tabular data and AI-ready knowledge
Parse and normalize data from CSV, Excel, JSON, and more into a unified, clean format ready for processing.
Automatically infer types, enrich with semantic metadata, and align schemas across diverse data sources.
Output structured representations optimized for LLM reasoning, with preserved relationships and context.
Integrate directly with vector databases, LLM inference engines, and your existing data pipelines.
Built for data engineers and ML practitioners who need reliable tabular data processing at scale.
Process millions of rows in seconds with our optimized parsing engine.
CSV, Excel, JSON, Parquet, and more. One tool for all your tabular data.
Battle-tested in production with comprehensive error handling and logging.
Self-hosted deployment ensures your sensitive data never leaves your infrastructure.
Our team works with you to ensure successful deployment and integration.
Output formats designed specifically for language model consumption.
Schedule a demo with our team to see BoxLM in action.