Dataset opportunity
Agrovegetal — 监管记录数据集机会
Agrovegetal 持有的中等监管记录数据集，可用于监管 RAG 和合规助手。
Score
74.7
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
49%
Action
收购
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)
全球农业人工智能市场 = 2024 年为 47 亿美元，复合年增长率为 26.3%。
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
监管记录数据集
Modality
文本
Sector
其他
Volume
中等
Freshness
实时
Rarity
高（专有）
Accessibility
部分
Legal
公司所有 — 可干净地许可
Buyer persona
监管科技与合规 AI 供应商
Agrovegetal 持有一个专门的监管记录数据集，由文本模态数据组成。这些信息通过专有的研发和种子育种计划生成，并整合了工业数据和物联网数据。它独有来自西班牙不同地区的纵向田间试验结果，使其非常适合监管 RAG 用例，为产品性能和合规性提供详细证据。
该数据服务于全球农业人工智能市场，该市场在 2024 年的价值为47 亿美元，预计复合年增长率为 26.3%。[5] 尽管由于该公司的合作结构和数据的专有研发来源，访问需要协商，但其稀有性和直接适用性使其成为寻求在该高增长行业中获得竞争优势的 AI 买家的宝贵资产。[5] ⚠ 尽职调查（有价值的数据，可协商访问）：数据通过专有的研发和种子育种计划生成；数据集包括跨多个西班牙地区的纵向田间试验结果；公司是合作社联盟，这可能会影响数据许可的决策 · 公司：独立。
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
这些证据证明 Agrovegetal 拥有一份专有数据集，详细说明了注册种子品种的监管合规性和实际性能。该数据包括独特的基因图谱和官方 DUS 测试结果，这是 RegTech 和合规 AI 供应商高度追捧的稀有资产。对于这些买家而言，该数据集直接支持开发复杂的监管 RAG 模型，以应对农业知识产权，这在预计 2024 年将达到47 亿美元的市场中是一项关键能力。
See dimension details ↓- Deep Qualification80
✓ 通过 — Agrovegetal 是一个数据持有者，其核心业务是开发和销售认证种子，而不是数据。其广泛的研发和田间试验数据是一个有价值的副产品，但由于其合作社联盟的结构，所有权很复杂，在未直接协商的情况下，许可权不明确。
- Dataset Specificity74
主导的“监管”，行业其他，3 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 Rarity82
专有领域数据
How scarce and proprietary the data is. Unique domain data scores high; openly available data lowers it. - Dataset Volume52
3 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 Value84
适合监管 RAG
How useful the data is for the target AI use-case — its fit for model training or fine-tuning. - Buyer Demand90
买家需求异常高，这得益于农业人工智能市场的快速扩张（复合年增长率为 26.3%），这产生了对专有监管和研发数据的强烈需求，以建立竞争优势。[5]
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility50
受限/未知
How legally easy the data is to obtain and use — open/API access scores high; PII or regulated data scores low. - Acquisition Feasibility30
中等难度，独立
How realistic it is to actually obtain the data, given access difficulty and the holder's corporate structure. - Evidence Strength62
3 种证据类型，3 次命中
How solid the proof is that the company holds this data — diversity of evidence types and number of hits. - Right to License92
所有权=公司所有，许可=干净
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 Orientation56
2 个数据需求信号（2 种类型）
How actively the company invests in data, measured by its data-appetite signals (hires, products, APIs…). - Dormant Data Surplus92
盈余=高 — 专有数据超出已货币化的部分
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 Audit100
✓ 良好目标 — Agrovegetal 是一个绝佳的目标，因为它是一家创新的中小企业，其核心业务是开发和销售认证种子，而不是其作为副产品产生的海量专有研发和田间试验数据。
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
Industrial data
这些证据包括详细的作物产量数据，跟踪产量、质量和抗病性，这对于对农业风险和商业可行性进行建模的买家至关重要。
IoT / sensor data
这代表了来自传感器的精细数据，关于种子品种如何响应特定的环境压力，为构建精准农业和气候适应模型提供了地面实况证据。
Regulatory records
这是核心的专有数据集，包含种子注册所需的官方DUS 测试结果和基因图谱，使其成为专注于农业知识产权和监管合规性的 AI 模型不可或缺的来源。
Marketplace
Dataset details
Detailed schema & sample available on access request.
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Coverage
Scanned sources
Deliverable
Premium dataset report
Agrovegetal Regulatory Records — a Moderate regulatory records dataset (Text modality) in the other domain. Primary AI use-case: Regulatory RAG. Market signal: Global AI in Agriculture market = $4.7B in 2024, CAGR 26.3% (source: Precedence Research). [5]. Investment score 74.7/100 (confidence 0.49). Recommended action: Acquire.
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