We don't just use AI — we build the connective tissue between foundation models, real-time market data, and proprietary trade intelligence. Here's how the stack works.
我们不只是使用 AI——我们构建基础模型、实时市场数据和专有贸易情报之间的连接组织。以下是技术栈的工作方式。
We don't lock into a single model. Multi-model orchestration — matching the right model to each research task — is itself a competitive advantage.
我们不绑定单一模型。多模型编排——为每个研究任务匹配最合适的模型——本身就是竞争优势。
Primary reasoning engine. Long-context processing for financial reports, causal chain analysis, thesis stress-testing, and bilingual cross-referencing. The backbone of our research workflow.主力推理引擎。长上下文处理财报、因果链分析、论点压力测试、中英双语交叉验证。研究工作流的核心。
Complementary model for multimodal tasks — chart parsing, scanned document analysis, and complex quantitative reasoning where specialized capabilities matter.多模态任务的补充模型——图表解析、扫描文档分析、以及需要专用能力的复杂量化推理。
Model routing and cost optimization layer. Unified API, flexible model switching, budget-efficient allocation — engineering maturity, not just stacking the most expensive models.模型路由和成本优化层。统一 API、灵活模型切换、预算高效分配——展示工程成熟度，而非简单堆砌最贵的模型。
Through Model Context Protocol (MCP), our AI talks directly to data sources — turning "human queries data, feeds AI" into "AI queries data autonomously."
通过 MCP 协议，AI 直接与数据源对话——从"人查数据喂 AI"变成"AI 自主查询数据"。
Global equities, fixed income, derivatives, ESG data. The foundation for cross-border valuation comparisons and global fundamental analysis.全球股票、固收、衍生品、ESG 数据。跨境估值对比和全球基本面分析的基础。
China market data core — A-shares, HK equities, futures, funds, macro indicators. Open-source, API-native, purpose-built for AI integration.中国市场数据核心——A 股、港股、期货、基金、宏观指标。开源、API 原生、为 AI 集成而生。
Research ingestion pipeline — auto-collecting sell-side reports, news, papers, social signals. AI extracts key views and maps consensus vs. our internal thesis.研究内容摄取管道——自动收集卖方报告、新闻、论文、社交信号。AI 提取关键观点，映射共识与内部论点的偏差。
Workflow orchestration — connecting models, data sources, and tools into repeatable automated research pipelines. The glue layer of our entire stack.工作流编排——将模型、数据源和工具串联成可复制的自动化研究管道。整个技术栈的粘合层。
Cross-border finance demands specific cloud architecture: data sovereignty, compliance, latency optimization, and multi-region consistency.
跨境金融对云架构有特殊要求：数据主权、合规、延迟优化和多区域一致性。
Global infrastructure backbone. Bedrock for Claude deployment, Data Exchange for market feeds, cross-region data synchronization.全球基础设施主力。Bedrock 部署 Claude、Data Exchange 市场数据、跨区域数据同步。
Azure OpenAI Service for enterprise-grade model deployment. Microsoft 365 ecosystem integration for research workflow automation.Azure OpenAI Service 企业级模型部署。Microsoft 365 生态集成研究工作流自动化。
BigQuery for large-scale market data analysis. Vertex AI for custom model training. Global network infrastructure for latency-sensitive processing.BigQuery 大规模市场数据分析。Vertex AI 自定义模型训练。全球网络基础设施处理延迟敏感任务。
An end-to-end research pipeline that connects signal detection, context assembly, multi-model analysis, and three-lens review.
端到端的研究管道，连接信号捕捉、上下文组装、多模型分析和三重视角审核。
Automated monitoring scans market data, news, social media, and trade flow data. Pre-defined thresholds trigger alerts.自动化监控扫描市场数据、新闻、社交媒体和贸易流数据。预定义阈值触发预警。
AI via MCP automatically pulls historical data, sell-side consensus, related assets, and macro context into a unified analysis workspace.AI 通过 MCP 自动拉取历史数据、卖方共识、关联资产和宏观背景，组装完整分析上下文。
Claude handles core reasoning (causal chains, thesis stress-testing). Supporting models handle data processing and quantitative analysis.Claude 负责核心推理（因果链、论点压力测试）。辅助模型负责数据处理和量化分析。
Buy-side researcher checks thesis quality. Risk manager evaluates sizing. Trader assesses execution. AI assists the first two; human makes the final call.买方研究员检查论点质量、风控评估仓位、交易员判断执行。AI 辅助前两步，人工做最终决策。
Structured investment memo generated. Monitoring rules updated. Bayesian priors adjusted for the next cycle.生成结构化投资备忘录。更新监控规则。调整贝叶斯先验，为下一轮循环做准备。