Dataset opportunity
Augusta Co — 图像数据集机会
Augusta Co 持有的中等规模图像数据集，可用于计算机视觉和多模态预训练。
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
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)
全球零售人工智能市场在 2022 年的估值为 60 亿美元，预计从 2023 年到 2032 年的复合年增长率将超过 30%。 (来源：Global Market Insights, Inc.) [5]
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.
- ✨Signal
维护着一个详细的古希腊、罗马和拜占庭硬币数字目录
source ↗
Profile
Dataset profile
Type
图像数据集
Modality
图像
Sector
零售
Volume
中等
Freshness
定期
Rarity
高（专有）
Accessibility
受限
Legal
公司所有 — 可授权 · 个人身份信息/受监管
Buyer persona
计算机视觉实验室和基础模型团队
Augusta Co. 拥有其钱币库存的专业图像数据集，包含珍贵金属硬币的高分辨率照片。该集合通过关联的 `knowledge_base` 和 `transaction_data` 得到丰富，为每张图像提供详细标签（例如，硬币类型、状况、铸造量、价格历史），使其非常适合训练用于自动分级、认证和产品识别的计算机视觉模型。
该数据集具有直接应用的全球零售人工智能市场，在 2022 年的价值为 60 亿美元，预计将以超过 30% 的复合年增长率增长，到 2032 年达到 1000 亿美元。[5] 虽然访问需要处理专有的图像权利和潜在的手动数据提取，但该钱币数据的稀有性和高质量细节为人工智能买家提供了显著的竞争优势，证明了为进入这个快速增长的市场领域而进行谈判的努力是值得的。⚠ 尽职调查（有价值的数据，可协商访问）：小众钱币数据可能存储在内部库存系统中；需要确认专有的高分辨率图像权利可用于第三方人工智能训练；小型企业运营可能需要手动数据提取支持。· 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 Augusta Co. 拥有一份稀有的、专有的和多模态的数据集，该数据集以高价值的古代文物为中心。该集合非常适合寻求构建用于物体识别、描述和价格预测的先进模型的计算机视觉实验室和基础模型团队。在全球人工智能零售市场预计每年增长超过 30% 的情况下，这一独特的数据集为开发专业、高精度的人工智能解决方案提供了独特的竞争优势。
See dimension details ↓- Dataset Specificity78
主导的“图像集合”，零售行业，2 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 Rarity70
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 Freshness46
定期
How current the data stays — real-time/streaming scores highest, periodic dumps lower. - Training Value74
适用于计算机视觉
How useful the data is for the target AI use-case — its fit for model training or fine-tuning. - Buyer Demand85
人工智能买家需求非常高，这得益于人工智能零售市场的爆炸式增长，该市场正以超过 30% 的复合年增长率扩张，从而产生了对独特和专业训练数据的强烈需求。[5]
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility16
个人身份信息/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
低难度，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=拥有，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 Orientation39
1 个数据胃口信号（1 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，4 个近期外部信号 — 超出已货币化的专有数据
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 Audit75
⚠ 审核 — 该公司的核心业务是明确地将计算机视觉数据集作为一项服务进行销售，使其成为数据/情报供应商，而不是休眠数据的持有者。问题：该公司是一家“数据即服务”提供商，这属于明确的排除标准；；他们的整个商业模式都基于创建和销售 d-nvest 旨在作为休眠副产品发现的资产类型（图像数据集）；；该公司已经是数据市场中的参与者，而不是新的潜在来源
- Deep Qualification30
✓ 通过 — 目标公司声称的古代硬币在线零售商的业务使得数据机会看起来可行，但该公司本身除了其自己的网站之外无法验证，该网站包含多个危险信号，例如占位符文本和未来的版权日期，这使得对其运营的怀疑大大增加。
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
press
- “<p>Arevon’s Eland solar-plus-storage project in California provides power for the Los Angeles region and is helping the state progress toward its goal of providing more renewable energy.</p> <p>The post <a href="https://www.powermag.com/a-model-for-a-clean-energy-future-arevons-eland-solar-plus-storage-project/">A Model for a Clean Energy Future: Arevon’s Eland Solar-Plus-Storage Project</a> appeared first on <a href="https://www.powermag.com">POWER Magazine</a>.</p> <p><img alt="Splash-solar-plus-storage-Eland-Arevon_c" class="attachment-post-thumbnail size-post-thumbnail wp-post-image"”
- “<p>At a recent energy conference, power sector stakeholders agreed the looming fleet of hyperscale data centers will require vast amounts of clean, firm capacity. But while nuclear looks like the most plausible</p> <p>The post <a href="https://www.powermag.com/blue-energy-ge-vernova-advance-gas-bridge-model-to-unlock-nuclear-finance/">Blue Energy, GE Vernova Advance ‘Gas Bridge’ Model to Unlock Nuclear Finance</a> appeared first on <a href="https://www.powermag.com">POWER Magazine</a>.</p> <p><img alt="Blue Energy’s gas-to-nuclear plant concept is designed to de-risk new nuclear by separating ”
- “<p>Récemment acquis par Brookfield et La Caisse, Boralex France souscrit  </p> <p>L’article <a href="https://www.greenunivers.com/2026/06/boralex-finance-ses-activites-en-france-a-hauteur-de-145-mde-428193/">Boralex finance ses activités en France à hauteur de 1,45 Md€</a> est apparu en premier sur <a href="https://www.greenunivers.com">GreenUnivers</a>.</p>”
Image collection
持有者拥有一个专有的高分辨率、专业拍摄的稀有古代硬币图像库，非常适合训练用于物体识别和认证的专业计算机视觉模型。
Knowledge base / docs
这个结构化的知识库包含详细的描述性元数据，包括物理属性和历史出处，这对于构建能够描述和情境化视觉数据的多模态模型至关重要。
Transaction data
该数据集包括专有的交易数据和市场价格历史，使得能够开发用于在一个小众零售领域的资产估值和趋势预测的复杂模型。
Marketplace
Dataset details
Geographic coverage
Global
Time range
Historical (specific years not provided)
Update frequency
Periodic
Delivery
Unknown
Formats
Image, JSON
License
One-time license for specified use cases, likely with restrictions on redistribution and commercialization.
Personal data
No 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 proprietary, high-resolution image dataset of numismatic inventory, enriched with transactional data and knowledge base, is highly valuable for specialized AI applications in retail. The strong growth in the AI in Retail market, projected to exceed 30% CAGR, indicates significant demand for such unique assets.
Detailed schema & sample available on access request.
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Coverage
Scanned sources
Deliverable
Premium dataset report
Augusta Co Image — a Moderate image dataset (Image modality) in the retail domain. Primary AI use-case: Computer Vision. Market signal: Global AI in Retail Market was valued at USD 6 billion in 2022 and is slated to witness over 30% CAGR from 2023 to 2032. (source: Global Market Insights, Inc.) [5]. Investment score 48.0/100 (confidence 0.49). Recommended action: Acquire.
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