Dataset opportunity
Gastonschul — Regulatory Records Dataset Opportunity
Large regulatory records dataset held by Gastonschul, usable for Regulatory RAG and Compliance Copilots.
Score
73.9
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
63%
Action
Data Sharing Agreement
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
Global RegTech market was valued at USD 19.06 billion in 2025 and is projected to grow to USD 105.23 billion by 2034, at a CAGR of 20.00% (source: Fortune Business Insights). [8]
Recent dated external facts that triggered this opportunity — auditable provenance.
- 📰press2026-06-12
Federal court temporarily upholds Trump’s 10% global tariff
supplychaindive.com ↗ - 📰press2026-06-11
Tariff refunds may soon cover more entries — but not without a fight
supplychaindive.com ↗ - 📰press2026-06-10
Razor reshapes supply chain to weather Trump-era China tariffs
manufacturingdive.com ↗
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.
Concrete evidence this company actively cares about data — why it's ripe for the deal room.
Profile
Dataset profile
Type
Regulatory Records Dataset
Modality
Text
Sector
mobility
Volume
Large
Freshness
Real-time
Rarity
High (proprietary)
Accessibility
Restricted
Legal
Largely customer-owned — GDPR-sensitive (PII review)
Buyer persona
RegTech & compliance-AI vendors
Gastonschul holds a Regulatory Records Dataset in Text modality, comprising detailed customs declarations, transactional data, event streams, and geo-data from its mobility and logistics operations. This rich combination of structured and unstructured information provides authentic, real-world evidence of complex international trade movements, making it an ideal asset for developing and fine-tuning a Regulatory RAG system to answer nuanced compliance and customs queries.
The business value of this data is reflected in the booming RegTech market, which was valued at approximately USD 19.06 billion in 2025 and is projected to grow at a CAGR of 20.00% between 2026 and 2034. [8] While access is subject to strict customs secrecy regulations and requires data anonymization, the rarity and depth of this real-world transaction_data offer a significant competitive advantage for AI buyers aiming to build high-value solutions in the lucrative trade compliance sector. ⚠ Diligence (valuable data, access to negotiate): Data is subject to strict customs secrecy and fiscal representation regulations.; Primary data ownership resides with importing/exporting clients.; Requires anonymization of PII (names, addresses) and sensitive commercial values. · corporate: independent.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
This evidence collectively proves Gastonschul possesses a proprietary, operational dataset of European customs and regulatory compliance activities. The data originates from their core business of managing thousands of cross-border declarations, including text related to emerging regulations like the Carbon Border Adjustment Mechanism (CBAM). This high-rarity dataset is a prime asset for RegTech and compliance-AI vendors seeking to train and power Regulatory RAG models, a critical need in a global RegTech market projected to exceed USD 100 billion by 2034.
See dimension details ↓- Dataset Freshness82
real-time/streaming
How current the data stays — real-time/streaming scores highest, periodic dumps lower. - Dataset Specificity100
dominant 'regulatory', sector mobility, 4 specific types
How sharply the data targets a specific, hard-to-substitute domain or task. Niche, well-defined data scores higher than generic. - Dataset Rarity94
proprietary domain data
How scarce and proprietary the data is. Unique domain data scores high; openly available data lowers it. - Dataset Volume80
5 evidence hits, explicit data-volume mention
Apparent scale of the data, inferred from the number of evidence hits and any explicit volume mentions. - Training Value94
fit for Regulatory RAG
How useful the data is for the target AI use-case — its fit for model training or fine-tuning. - Buyer Demand82
The global RegTech market is projected to grow at a CAGR of 21.1% from 2026 to 2033, driven by the increasing demand for automated compliance processes and the adoption of AI, indicating very high demand for regulatory data in sectors like
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility0
PII/regulated
How legally easy the data is to obtain and use — open/API access scores high; PII or regulated data scores low. - Acquisition Feasibility0
high difficulty, independent
How realistic it is to actually obtain the data, given access difficulty and the holder's corporate structure. - Evidence Strength86
5 evidence types, 5 hits
How solid the proof is that the company holds this data — diversity of evidence types and number of hits. - Right to License0
ownership=customer_owned, licensing=gdpr_sensitive
Whether the company can legally license the data out — based on ownership and licensing complexity. - Corporate Independence90
independent
Whether the holder can decide alone — an independent company scores higher than a subsidiary of a large group. - Data Orientation73
3 data-appetite signals (3 types)
How actively the company invests in data, measured by its data-appetite signals (hires, products, APIs…). - Dormant Data Surplus92
surplus=high, 3 recent external signals — proprietary data beyond what's already monetised
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
⚠ review — Gaston Schul's core business is selling customs-related services and digital solutions, including data exchange platforms, making it a seller of intelligence and thus not a good target. Issues: The company's core business is providing customs services and digital solutions, not a non-data-related operational business. [7, 13]; They actively sell digital solutions, including 'Customs Data Exchange' via API/EDI, an 'Export Portal', and a 'Control Tower' platform, which are forms of sell
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
Geospatial data
This is geospatial logistics data from a centralized 'Control Tower,' proving the holder manages and consolidates global customs activities across multiple European countries.
Event streams
This is real-time event data from digital customs platforms, offering a time-series view of declaration processes valuable for modeling risk and operational efficiency.
Transaction data
This is granular customs transaction data, containing essential fields like HS codes, origin, and valuation that are foundational for training any trade compliance AI.
Regulatory records
This is proprietary regulatory diligence text directly related to new, complex European rules like the CBAM and Deforestation Regulation, representing a unique source for training next-generation compliance models.
Data-volume signal
This evidence demonstrates significant data volume, confirming a production-level scale of thousands of declarations processed across key European markets including the UK, Germany, and France.
Coverage
Scanned sources
Deliverable
Premium dataset report
Gastonschul Regulatory Records — a Large regulatory records dataset (Text modality) in the mobility domain. Primary AI use-case: Regulatory RAG. Market signal: Global RegTech market was valued at USD 19.06 billion in 2025 and is projected to grow to USD 105.23 billion by 2034, at a CAGR of 20.00% (source: Fortune Business Insights). [8]. Investment score 73.9/100 (confidence 0.63). Recommended action: Data Sharing Agreement.