为 AI 系统提供新鲜网页上下文同时保留明确的质量控制。
为 RAG、智能体、分析和模型工作流获取并结构化公开网页数据，同时保留数据源范围、验证和失败状态。
WORKFLOWS
围绕真实业务问题构建可验证的数据工作流。
从明确的数据源、Schema、新鲜度和验收标准开始，而不是从夸大的通用成功率开始。
Retrieval & RAG
Collect source content, convert it into clean Markdown or structured records, and preserve provenance for retrieval.
Agent context
Give agents fresh, source-linked web context without promoting unverified acquisition as a successful result.
Structured research
Turn approved public sources into repeatable schemas for comparison, monitoring, and downstream reasoning.
METHOD
把范围、采集、结构化、验证和交付分成可检查步骤。
每一步都保留上下文与失败状态，便于复核和持续改进。
Scope
Define approved sources, fields, freshness, and acceptance criteria.
Acquire
Collect source content through an appropriate bounded path.
Structure
Convert accepted content into Markdown or a task-specific schema.
Validate
Check required fields, coverage, consistency, and false-success conditions.
Deliver
Send approved output into retrieval, analytics, or agent workflows.
QUALITY
用证据衡量质量，而不是用一个通用百分比。
当有带日期和样本范围的基准证据时，再发布对应质量指标；否则明确保留不确定性。
Fail closed
Blocked, empty, malformed, or inconsistent output should not become confident-looking AI context.
Source aware
Keep URLs, timestamps, provenance, and source assumptions close to the records they produced.
Acceptance driven
Measure the fields and decisions that matter to the actual workflow rather than a generic success score.
从一个可测量的试点开始。
明确数据源、Schema、频率和验收标准，再根据真实结果扩大范围。