Dataset opportunity
Gaston Schul — 监管记录数据集机会
Gaston Schul 持有的中等规模监管记录数据集，可用于监管 RAG 和合规助手。
Score
48
Score (0–100) blends weighted dimensions — dataset rarity, training value, buyer demand, evidence strength and right-to-license. 70+ is deal-ready. See the scored dimensions below for the breakdown.Confidence
56%
Action
数据共享协议
The recommended deal structure for this dataset: Acquire (full buyout), License (paid usage rights), Data Sharing Agreement (controlled access, no transfer of ownership), Partnership (co-development) or Annotation Program (labeling). Chosen from data ownership, licensing complexity and accessibility.Market size (indicative estimate)
全球贸易管理市场 = 2024 年为 12 亿美元，复合年增长率 8.71%（来源：Data Bridge Market Research）
Lineage
How this lead was derived
The signal-first chain, end to end: recent external signals → qualified niche → resolved data-holder → site verification → scored opportunity. Every lead is explainable.
Profile
Dataset profile
Type
监管记录数据集
Modality
文本
Sector
出行
Volume
中等
Freshness
实时
Rarity
高（专有）
Accessibility
受限
Legal
混合所有权 — GDPR 敏感（PII 审查）
Buyer persona
RegTech 和合规 AI 供应商
Gaston Schul 持有一个全面的监管记录数据集，由客户交易汇总的文本格式的报关单和税务信息组成。数据包括 `event_streams`（事件流）、`geo_data`（地理数据）、`regulatory`（监管）详情和 `transaction_data`（交易数据），使其非常适合训练监管 RAG 模型以应对复杂的国际贸易合规性。
全球贸易管理市场在 2024 年的估值为 12 亿美元，预计到 2032 年的复合年增长率 (CAGR) 为 8.71%。[4] 这个高增长的市场凸显了这一独特数据资产的价值。尽管存在海关保密和需要大量 PII（个人身份信息）匿名化等访问复杂性，但该数据集的稀有性和直接应用于高价值 AI 合规解决方案的特性使其成为一项引人注目的资产进行谈判。⚠ 尽职调查（有价值的数据，可协商访问）：数据涉及敏感的报关单和税务信息；所有权与客户共享，但由 Gaston Schul 汇总；原始记录需遵守严格的监管合规性（海关保密）；需要对 PII（发货人/收货人）进行大量匿名化 · 公司：独立。
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
这些证据共同证明 Gaston Schul 持有一个高稀有度、专有的监管记录和应用贸易数据的数据集，直接源于其核心报关代理业务。该数据集是 RegTech 和合规 AI 供应商构建先进监管 RAG 模型的主要资产。在全球贸易管理市场预计将超过 12 亿美元的情况下，这些数据提供了自动化遵守复杂、不断变化的规则（如CBAM）和管理碳排放数据所需的真实依据，从而提供显著的竞争优势。
See dimension details ↓- Dataset Specificity100
主导的“监管”，出行行业，4 种特定类型
How sharply the data targets a specific, hard-to-substitute domain or task. Niche, well-defined data scores higher than generic. - Dataset Rarity94
专有领域数据
How scarce and proprietary the data is. Unique domain data scores high; openly available data lowers it. - Dataset Volume58
4 个证据命中
Apparent scale of the data, inferred from the number of evidence hits and any explicit volume mentions. - Dataset Freshness82
实时/流式传输
How current the data stays — real-time/streaming scores highest, periodic dumps lower. - Training Value94
适用于监管 RAG
How useful the data is for the target AI use-case — its fit for model training or fine-tuning. - Buyer Demand85
AI 买家需求受全球贸易管理市场强劲增长（复合年增长率 8.71%）的驱动，催生了对专业监管数据以构建先进合规模型的需求。[4]
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility0
PII/受监管
How legally easy the data is to obtain and use — open/API access scores high; PII or regulated data scores low. - Acquisition Feasibility0
高难度，独立
How realistic it is to actually obtain the data, given access difficulty and the holder's corporate structure. - Evidence Strength74
4 种证据类型，4 次命中
How solid the proof is that the company holds this data — diversity of evidence types and number of hits. - Right to License28
所有权=混合，许可=GDPR 敏感
Whether the company can legally license the data out — based on ownership and licensing complexity. - Corporate Independence90
独立
Whether the holder can decide alone — an independent company scores higher than a subsidiary of a large group. - Data Orientation22
0 数据胃口信号（0 类型）
How actively the company invests in data, measured by its data-appetite signals (hires, products, APIs…). - Dormant Data Surplus92
盈余=高，3 个近期外部信号 — 超出已货币化范围的专有数据
Volume and value of proprietary data this company holds BEYOND what it already monetises — the dormant surplus we can unlock. A company can sell some insights AND still sit on a far larger dormant asset. - ICP Audit67
⚠ 审查 — 该公司的核心业务是报关服务，但它已经拥有一个复杂的“报关数据交换”产品，使用 API 和 EDI 来自动化和数字化客户数据，这使其成为一个糟糕的目标，因为它已经销售了从其数据中提取的智能。问题：该公司的核心产品不是销售原始数据，而是明确销售数据驱动的服务和智能，这使其成为“糟糕的目标”类别；“报关数据交换”服务提供构建“EDI 和 API 驱动的
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
Event streams
持有者生成实时事件流，跟踪贸易流程的状态，为专注于风险降低和流程自动化的 AI 应用提供宝贵数据。
Transaction data
这是结构化的表格数据，详细说明了报关单和其他国际贸易文件，对于训练 AI 以自动化复杂的合规和文档工作流程至关重要。
Regulatory records
该数据集包含一个专有的文本记录语料库，详细说明了复杂法规的应用解决方案，包括新兴法规（如CBAM）及其相关的进口商品碳排放数据。
Geospatial data
这些证据指向了跨越多个边境和司法管辖区的贸易活动的结构化数据映射，对于训练能够应对全球物流复杂性的 AI 模型至关重要。
press
- “<figure><div><img src="https://imgproxy.divecdn.com/sxyEB0Qrc32AwU9V3XQQS3lB-wKjhbCljqJHl0fweZs/g:ce/rs:fill:1600:900:1/Z3M6Ly9kaXZlc2l0ZS1zdG9yYWdlL2RpdmVpbWFnZS9HZXR0eUltYWdlcy0yMjgyMDY0ODg0LmpwZw==.webp" /></div></figure><p>The U.S., Mexico and Canada will continue negotiating about potential adjustments to the trilateral free trade agreement, which will remain in place until at least 2036.</p>”
Marketplace
Dataset details
Geographic coverage
Global
Time range
Real-time
Update frequency
Real-time
Delivery
API
Formats
Text
License
One-time license for use in Regulatory RAG model training and operation. Specific usage restrictions apply.
Personal data
Contains PII
Indicative estimate, derived from public signals — not a quote, not contractual, and not agreed with the company. Is this your company? Correct it.
This dataset's high rarity as proprietary regulatory records from customs brokerage, combined with moderate volume and real-time freshness, positions it as a valuable asset for the growing Global Trade Management market, particularly for RegTech AI applications.
Detailed schema & sample available on access request.
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This listing was generated automatically from public signals. It is not verified, and we are not affiliated with this company.
Coverage
Scanned sources
Deliverable
Premium dataset report
Gaston Schul Regulatory Records — a Moderate regulatory records dataset (Text modality) in the mobility domain. Primary AI use-case: Regulatory RAG. Market signal: Global Trade Management market = $1.2B in 2024, CAGR 8.71% (source: Data Bridge Market Research). Investment score 48.0/100 (confidence 0.56). Recommended action: Data Sharing Agreement.
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