These are fictitious examples created to illustrate the potential applications of the new technology and associated services. They do not reflect confidential details or strategies of companies.
Correlating Data Cost to Sales Performance
🔴 Problem: Companies spend money on data, but no one knows if it actually helps sales.
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✅ Solution: ORBintel makes data profitability crystal clear—turning Data Teams from a cost to a profit center by showing exactly how data spending drives Sales Qualified Leads (SQLs) and revenue.
Optimizing Refining & Manufacturing Operations
🔴 Problem: Unreliable equipment in auto parts manufacturing leads to costly delays, missed deadlines, and inconsistent quality—damaging both profits and customer trust.
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✅ Solution: ORBintel brings reliable, data-driven insights by predicting equipment failures before they occur. Manufacturers schedule maintenance with certainty, reducing downtime, and costs. By linking production data to sales, they can also improve responsiveness and reduce lead times for changeovers.
Optimizing AI 'Large Language Model' (LLM) Builds
🔴 Problem: AI language model builders are losing money because they can’t track the real cost of development and risk of litigation due to errors. Extensive development efforts don’t guarantee accurate results. Inaccurate models, such as legal 'hallucinations,' are causing even bigger issues, including loss of insurance coverage.
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✅ Solution: ORBintel helps AI vendors track build costs with precision, improving model quality and mitigating risk. It enables them to demonstrate their ability to achieve auditable quality, proving they can reduce errors and deliver reliable, accurate results, all while regaining insurance coverage.
Data Assets - Mitigating Cyberattack Risk
🔴 Problem: A multinational suffers a $38 billion cyberattack, facing audits, fines, and reputational damage. Shockingly, 33% of the stolen data—218.4 million records—was obsolete.
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✅ Solution: ORBintel helps companies make smart, cost-effective decisions about data storage to reduce cyber risk. By moving obsolete records to secure off-cloud storage for just $80,500/year or deleting unnecessary data, companies can mitigate risk, lower costs, and avoid regulatory penalties.
eCommerce Market - Value-driven Triage of Paper Data Inventory
🔴Problem: A regional retailer has 20 years of customer data stuck on paper, making decision-making difficult. They try OCR scanning but can’t determine which data is valuable, what to keep for tax credits, and what should just be deleted to reduce costs.
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✅ Solution: ORBintel helps retailers triage paper data intelligently—sampling collections to identify profitable data before digitization. Unnecessary records are removed, cutting costs like CapEx, OpEx, and insurance fees, while OCR converts only high-value data into actionable digital insights.
EU Artificial Intelligence Act - Cutting Compliance Cost
🔴 Problem: AI models are expensive to build, and most fail to scale. Even the successful ones struggle with gaps in accuracy and reliability, leading to compliance risks. The EU's Artificial Intelligence Act (AIA) aims to fix this—but compliance could cost companies $417,000 per high-risk AI product (1).
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✅ Solution: ORBintel provides precise metadata metrics to enhance AI accuracy and decision reliability, making compliance cheaper and easier. Over time, companies cut costs further while strengthening AI quality and regulatory trust.
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(1) https://itif.org/publications/2021/07/26/how-much-will-artificial-intelligence-act-cost-europe/
Corporate Operations Analysis
🔴 Problem: Data is an intangible asset. It is difficult to manage on its own because companies lack a standardized way to determine the true value (1). This makes it hard to calculate data's contribution to the bottom line. Investors and financiers struggle to analyze a target company's operations without this key insight.
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✅ Solution: ORBintel creates a unified financial language that helps organizations measure both data and physical assets. For Data-driven analysis, investors can track the link between data operations and bottom-line performance. For Bricks & Mortar, organizations can streamline auditing and inventory control by gaining a more accurate view of their physical assets, eliminating costly errors and improving lifecycle management.
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(1) (2022) https://trellis.net/article/greenfin-interview-open-source-design-meets-climate-finance-data/​​​​​​
Scope 3 Emissions Reporting Market - Using Data to Drive Profitable Sustainability
🔴 Problem: Transitioning to a low-carbon economy is complex. Many investors and asset managers struggle to assess the true value and transition potential of energy resources. Concurrently assets could risk being stranded, creating financial and environmental risk, and challenges to national budgets. The challenge lies in how to align operational decisions with long-term sustainable risk-management goals and to manage assets through their full life cycle; while ensuring profitability during the transition.
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✅ Solution: ORBintel empowers investors and asset managers to track and evaluate the transition potential of underperforming assets. By integrating Life Cycle Analysis with investment metrics (1), ORBintel helps companies make data-driven decisions that align assets with long-term goals.
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This approach reduces financial risk while optimizing the transition toward a profitable, low-carbon future. The result is a clearer path for asset managers to unlock value in underperforming resources while ensuring alignment with profitable 'sustainable finance' goals.
​​​​(1) (2023) Designing for Comparability: a foundational principle of analysis missing in carbon reporting systems; Jimmy Jia, Abrar Chaudhury, Nicola Ranger, 18 July 2023, Oxford Smith School of Enterprise and the Environment | Working Paper No. 23-04. ISSN 2732-4214 (Online): https://www.smithschool.ox.ac.uk/sites/default/files/2023-07/WP_No._23-04_Comparability.pdf