Our core team members have years of experience in large language model (LLM) pre-training, fine-tuning, and engineering deployment. We are proficient in efficient parameter fine-tuning techniques such as LoRA and QLoRA, and skilled in combining RAG (Retrieval-Augmented Generation) to address hallucination and knowledge timeliness issues in long-text generation, ensuring the accuracy and logical rigor of output content.
We possess complete delivery capabilities from underlying model selection and private deployment to application development. We have independently developed a document structure parsing engine and a template variable engine, capable of precisely handling professional documents with complex layouts, dynamic tables, and cross-references, ensuring that the agent generates both "quickly" and "accurately," truly unleashing content productivity.
We have successfully provided development services to enterprises in the finance, healthcare, energy, and manufacturing industries, supporting integration with enterprise systems such as CRM and ERP to enable intelligent filling and version iteration based on real-time data.
Our custom development service for document review agents aims to build a dedicated AI review platform for enterprises, enabling automated risk review of unstructured documents such as contracts, bids, financial reports, and compliance documents.
Based on large language models (LLM) and RAG (Retrieval-Augmented Generation) technology, we deeply customize review rule engines for you. Our services cover three core capabilities: the semantic understanding layer, which precisely identifies ambiguous clauses, missing elements, and logical contradictions; the compliance comparison layer, which performs real-time cross-validation of embedded data against internal corporate policies and external legal and regulatory databases; and the dynamic learning layer, which continuously optimizes review granularity based on feedback from business experts.