Rowfill adopts a localized LLM deployment strategy in its architectural design, which constitutes the core defense of its data security system. The platform supports Llama2, Mistral and other mainstream open source LLMs running in a private environment, ensuring that sensitive data does not leave the corporate intranet throughout. The technical implementation builds a triple protection mechanism: the first is network isolation, sandboxing the data processing environment through Docker containers; the second is memory encryption, the temporary data in the model inference process are encrypted using AES-256; the third is access control, integrated LDAP/ActiveDirectory to achieve fine-grained rights management.
Compared with traditional solutions relying on cloud APIs, Rowfill's localized processing has three significant advantages: processing latency is reduced by 80%, from an average of 800ms to less than 150ms; the risk of third-party data leakage is completely circumvented; and continuous fine-tuning of the enterprise's knowledge base is supported for training. Test data from a financial institution shows that when processing customer credit reports, the local model keeps the response time under 200ms while maintaining 95% recognition accuracy.
The platform has also innovatively developed a data desensitization workflow that can automatically identify and mask 18 types of sensitive information, including ID card numbers and bank card numbers, at the extraction stage. This mechanism has passed ISO27001 certification and is particularly suitable for use in strongly regulated industries such as healthcare and finance.
This answer comes from the articleRowfill: Batch Extraction of Structured Information from Documents and Automated AnalysisThe
