Mining asset valuation has traditionally focused on the technical fundamentals. But in real-world transactions, the value of a mining asset is often shaped by the commercial agreements. Commercial royalties, streaming agreements and offtake terms determine how realised value is allocated between stakeholders.

At Stormlands Mining, we have built these commercial structures directly into our valuation platform so users can understand the actual economic impact of transaction terms. Royalties are a familiar feature of mining finance, but they are not always modelled with enough commercial precision. The valuation impact depends on how the royalty is calculated. Royalties can provide non-dilutive capital for mining companies and long-term commodity exposure for royalty holders. But they also directly affect cash flow, NPV and the distribution of value.

Streaming is now a major financing tool, particularly for precious metals produced as by-products of base metal mines. In a typical stream, a mining company receives an upfront payment and agrees to deliver a percentage of future production to the streaming company. This can be attractive because capital can be raised without taking on debt or equity. A stream may look attractive, however, it can transfer substantial upside to the streamer, especially where commodity prices rise or production exceeds expectations.

That is why Stormlands has added dedicated streaming functionality to allow users to model the economic effect of a stream directly inside the project valuation. Offtake agreements can have a direct valuation impact. In some cases, the impact may be modest. In others, offtake terms can create material revenue leakage or shift value from the producer to the buyer. For analysts, management teams and investors, the key question is not simply “What is the expected revenue?” It is “What is the expected revenue after the commercial contract is applied?”

Small differences in commercial assumptions can have large effects on value. A project can look technically robust but produce a very different outcome once royalties, streams and offtake terms are modelled properly. The industry is becoming more sophisticated in valuation modelling, but commercial term modelling often remains fragmented, manual and spreadsheet-driven. That creates a gap.

Stormlands Mining has built an AI-first valuation and analytics platform for mining assets. Commercial royalty, streaming and offtake modelling is an important step in expanding the platform from technical valuation into transaction-aware economic analysis. Because in mining, value is not just mined it is commercially negotiated. It is created and often transferred through the commercial contracts that determine how realised value is allocated.

To learn more about how Stormlands Mining is modelling commercial royalties, streaming and offtake terms, get in touch or follow us on LinkedIn for further updates.

Unlike market capitalization, which only shows equity value, EV includes debt, potential dilutive instruments, and cash reserves. This gives a complete picture.

Junior miners often carry significant financial leverage because mining projects are capital-intensive. EV accounts for market capitalization, debt, and cash, providing a fuller view of a company’s financial health.

Market capitalization is like the tip of an iceberg—visible but not the whole story. EV reveals the entire iceberg, including what’s hidden beneath the surface. This is how investors can understand a company’s true value. It involves looking beyond equity. Debt obligations, cash reserves — data that’s important in the mining operations.

Comparing junior mining companies using EV is insightful. These companies might have similar market caps but very different financial structures, with varying levels of debt and cash. EV levels the playing field, offering a consistent standard for comparison.

For instance, a junior miner with significant debt might have an EV much greater than its market cap. It could signal potential leverage. It might be a warning or an indicator of growth potential, depending on the company’s assets and efficiency. A mining company with substantial cash reserves (from partnerships or pre-production revenue), could have a lower EV than its market cap. It means it’s undervalued.

Traditional mining valuation multiples, like price-to-earnings ratios, often misleading. Market shocks cause earnings to fluctuate wildly, making these multiples unreliable. EV cleans up these distortions by including debt and cash. Think about a mining company investing in new technology. Its short-term earnings might drop, making it look less profitable if you only consider traditional multiples. However, EV captures the full financial impact, showing the potential future benefits of this investment.

Mining is cyclical. Earnings rise and fall, causing multiples to fluctuate. EV remains stable through these cycles, considering long-term debt and cash. This offers a more reliable valuation over time. During mergers and acquisitions, the market cap alone can deceive. EV includes debt and synergies from the deal, providing a more accurate measure of the new entity’s value. When a company changes its strategy, for example diversifying into new commodities, it can affect its valuation. EV is better equipped to account for these changes. It reflects the company’s long-term value.

EV is a critical component in the due diligence of companies listed on TSX, TSX.V, and AIM stock markets.

AI and Excel. Both tools are powerful in their own right.

The trick?

Knowing which to use and when.

In mining asset valuations, AI is starting to edge out Excel.

Why?

It’s all about real-time scenario analysis, swift data processing, predictive modeling, and seamless collaboration. These are areas where Excel simply can’t keep up.

Complex financial data analysis? Calculating EV of the corporation? — Choose AI.

Straightforward data tasks? — Better use Excel.

Valuing mining assets isn’t getting any easier. With commodity prices all over the place and ESG trends gaining momentum, things are only getting more complicated. Sure, asset values in the mining sector are expected to rise, but don’t be surprised by the volatility along the way.

This is where the right mix of AI and Excel becomes important.
Mining companies need to brace themselves for independent valuations. Commodity-price assumptions are a major driver of asset-price swings. Some companies are betting on spot prices, while others are banking on long-term forecasts. Which one’s right? Time will tell.

Then there’s ESG. It’s not just a buzzword anymore. Companies are increasingly factoring in ESG trends when valuing assets. Metals linked to clean energy are getting a lot of attention, as are traditional safe havens like gold. Some players in the market have been slow to adapt, but that’s changing as the focus on clean energy intensifies.

So, what about AI’s role in all this?

AI has already made waves in exploration and production. Valuation? It’s only a matter of time. The demand for real-time, reliable, and cost-effective data is pushing AI to the forefront. Imagine cutting costs, speeding up the valuation process, and gaining deeper insights into an asset’s worth—all with the help of AI. The future of asset valuation is looking more and more digital.

Mining companies can do a lot to help valuers.

1️⃣ Context matters. Understand why the valuation is happening. Is it for internal use or to share with the market? Who are the intended users of the valuation: domestic or international investors, banks, other mining companies?

2️⃣ Set realistic expectations with respect to value. There is a tendency to seek value aligned with inflated expectations. However, stakeholders might not see things the same way. Some balance is required. And using AI alongside Excel can provide a more grounded view of the asset’s true value.

3️⃣ Close collaboration with the valuers is key. The valuation of a mining asset is an iterative process. Break it down into steps. Figure out where you disagree on the technical and economic details. The aim is to close those gaps and get on the same page.

In the end, mining companies need a structured valuation process.

By combining the strengths of AI and Excel, companies can ensure they’re getting the most accurate, actionable valuations possible.

February 2026

Africa is increasingly positioned as a critical source of the raw materials needed for the energy transition. But alongside the opportunity sits a harder question:

𝗛𝗼𝘄 𝗱𝗼 𝗴𝗼𝘃𝗲𝗿𝗻𝗺𝗲𝗻𝘁𝘀, 𝗶𝗻𝘃𝗲𝘀𝘁𝗼𝗿𝘀, 𝗮𝗻𝗱 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝗶𝗲𝘀 𝗸𝗻𝗼𝘄 𝘄𝗵𝗲𝘁𝗵𝗲𝗿 𝗮 𝗺𝗶𝗻𝗶𝗻𝗴 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝘄𝗶𝗹𝗹 𝘁𝗿𝗮𝗻𝘀𝗹𝗮𝘁𝗲 𝗶𝗻𝘁𝗼 𝗿𝗲𝗮𝗹, 𝘁𝗮𝘅𝗮𝗯𝗹𝗲 𝗹𝗼𝗰𝗮𝗹 𝘃𝗮𝗹𝘂𝗲… 𝗼𝗿 𝗶𝗻𝘁𝗼 𝘄𝗲𝗮𝗹𝘁𝗵 𝗲𝘅𝘁𝗿𝗮𝗰𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗿𝗲𝘃𝗲𝗻𝘂𝗲 𝗹𝗲𝗮𝗸𝗮𝗴𝗲?

In practice, “revenue leakage” rarely shows up as one obvious problem. It can be embedded in:
• terms that look reasonable on paper but perform poorly under price/cost volatility
• poorly modelled valuations
• transfer pricing and related-party service arrangements
• offtake and payability terms that quietly shift value out of the jurisdiction
• misalignment between what’s disclosed, what’s modelled, and what’s audited

The challenge is that risk is often invisible without a model that ties together the full value chain and then tests how outcomes change under realistic scenarios that stress-test production, pricing, costs, financing, fiscal rules, commercial and contract terms.

𝘛𝘩𝘢𝘵’𝘴 𝘵𝘩𝘦 𝘱𝘳𝘰𝘣𝘭𝘦𝘮 𝘚𝘵𝘰𝘳𝘮𝘭𝘢𝘯𝘥𝘴 𝘔𝘪𝘯𝘪𝘯𝘨 𝘸𝘢𝘴 𝘣𝘶𝘪𝘭𝘵 𝘵𝘰 𝘢𝘥𝘥𝘳𝘦𝘴𝘴.

Stormlands helps teams identify taxable revenue and quantify expected government take by combining:
• structured data extraction from technical reports and project documentation (LLM-powered ETL)
• a standardised fiscal/valuation framework to calculate royalties, CIT and other fiscal flows
• dynamic scenario planning so stakeholders can see how risk shifts under different prices, costs, grades, recoveries, and timelines
• transparent outputs that can support disclosure, negotiation, and oversight

The result is not just “a valuation”, it is a way to ask better questions sooner:
• What revenue is actually taxable, and when?
• Which assumptions drive the biggest swings in government take?
• Where are the biggest risks of revenue leakage under downside scenarios?
• What would change if contract terms were structured differently?

If Africa is to capture long-term value from critical minerals, we need tooling that makes fiscal outcomes clear, comparable, and accessible, not trapped in brittle spreadsheets.

𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻: 𝗪𝗵𝗮𝘁’𝘀 𝘁𝗵𝗲 𝗯𝗶𝗴𝗴𝗲𝘀𝘁 𝗯𝗮𝗿𝗿𝗶𝗲𝗿 𝘆𝗼𝘂’𝘃𝗲 𝘀𝗲𝗲𝗻 𝘁𝗼 𝗽𝗿𝗲𝘃𝗲𝗻𝘁𝗶𝗻𝗴 𝗿𝗲𝘃𝗲𝗻𝘂𝗲 𝗹𝗲𝗮𝗸𝗮𝗴𝗲? 𝗗𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆? 𝗖𝗼𝗻𝘁𝗿𝗮𝗰𝘁 𝗼𝗽𝗮𝗰𝗶𝘁𝘆? 𝗠𝗼𝗱𝗲𝗹𝗹𝗶𝗻𝗴 𝗰𝗮𝗽𝗮𝗰𝗶𝘁𝘆?

https://miningdigital.com/news/how-african-mining-holds-the-key-to-global-economic-security

Stormlands Mining Secures Strategic Investment from Enterprise Ireland’s High Potential Start-Up Programme

Dublin, 19 December 2024 – Stormlands Mining, a fintech analytics platform transforming the valuation and sustainability of the global mining industry, has secured investment from Enterprise Ireland’s High Potential Start-Up (HPSU) programme.

This investment marks a significant milestone in the company’s journey to use artificial intelligence and machine learning to drive innovation and transparency in the global mining industry.

Stormlands Mining is an Irish start-up providing artificial intelligence and machine learning tools that model the financial and environmental sustainability of mines across every stage of their lifecycle—from exploration to remediation. By replacing outdated spreadsheet-based systems with dynamic, data-driven analytics, Stormlands enables mining executives, investors, and financial institutions to make smarter, more sustainable decisions.

The investment follows Enterprise Ireland’s rigorous due diligence process, affirming Stormlands Mining’s potential for rapid growth, global scalability, and meaningful environmental impact.

Jennifer Melia, Executive Director with Enterprise Ireland commented:

“Stormlands Mining is an exceptional example of Irish innovation with global ambition. The team’s deep expertise in mining, fintech, and analytics positions them as leaders in transforming the valuation and sustainability of mining assets worldwide. This investment reflects our confidence in their capacity to deliver both financial and environmental value to the sector.”

Róisín O’Connell, CEO of Stormlands Mining, welcomed the investment:

“We are thrilled to have Enterprise Ireland on board as our lead investor. Their support goes beyond funding, offering invaluable networks and expertise as we scale globally. At Stormlands, we are passionate about enabling smarter, transparent decision-making across the mining industry—an industry critical to the transition to a net-zero economy.”

With sustainability and data governance at its core, Stormlands Mining’s platform addresses the pressing need for accurate, real-time mining asset valuation, enhancing both financial outcomes and environmental responsibility.

For more information about Stormlands Mining, visit www.stormlandsmining.com.

ENDS

For media inquiries:
Róisín O’Connell, CEO: + 353 87 9193333
Email: ceo@stormlandsmining.com
Website: www.stormlandsmining.com

Publication date: 19 December 2024

A commercial marketing contract or purchase agreement for a base metal concentrate such as copper, lead or zinc contains a wide range of commercial, logistical and legal clauses. These clauses exist to protect the interests of both the buyer, seller, and their agents, while at the same time providing a clear document to manage the transfer in title and risk of the concentrates. The agreed quantity can be delivered in one or more independent shipments — sometimes over a number of years or the life of a mine.

Generally speaking, these contracts are written in pragmatic and plain English so as to provide a working document that is used to manage the commercial relationship between the parties. This is very important because typically, such agreements are also frequently referred to by non-legal professionals — individuals with backgrounds in Financial Analytics, marketing or logistics.

In order to ensure that the correct value is calculated for each shipment and that transactions run smoothly, payments are made on schedule and with a minimum amount of fuss.

The clauses in a purchase contract can be grouped into a number of categories: legal, payment, shipping, price determination, quotational period and quantity determination. Below are some questions to consider when making a contract to ensure that all potential concerns have been considered in advance.

Legal

• Who are the parties in the contract?
• What is being agreed in principle?
• Who is buying and who is selling?
• What are the specific terms and how will they be interpreted?
• How can a party default on their obligations and what happens then?
• When does the title and risk pass from buyer to seller?
• What happens if an unexpected or uncontrollable event occurs?
• If any disputes arise, how will they be resolved?
• How can parties transfer their obligations to a third party?
• How will the parties communicate to manage the agreement?
• Which jurisdiction’s law will be used in the event of a dispute?
• How will information be governed, respected commercially and kept confidential?

Payment

• When does the Seller receive payment?
• What documentation does the Seller have to furnish?
• How is the provisional value calculated?
• What happens if the prices used to calculate provisional value change significantly?
• When are a re-measurement of trade receivables and a repayment required?
• When is the final payment made?

Shipping

• What is the size of each delivery?
• Where shall the material be delivered?
• What are the required characteristics of the vessel to be used?
• What, if any, are the restrictions at the loading or discharge port?
• How shall a suitable vessel be nominated and approved?
• Which party will pay for loading, shipping and unloading expenses?
• How will the time spend loading and unloading be calculated?
• Who will compensate the vessel for delays in loading and unloading?
• Is there an agreed shipping or delivery schedule? If so, what is it?

Price Determination

• What is the core transaction or typically the “what” and “where”?
• For the sake of clarity, what technical terms or units need to be defined?
• What currency is used for payments?
• What metals are payable?
• Are there any minimum or percentage deductions?
• What official metal price is used?
• What happens if the official metal prices cease to exist?
• Are there any penalty elements?
• Is there a penalty charged and at what level of impurity does it apply?
• Is there a treatment charge?
• What official metal price is used to calculate the treatment charge?
• How does the treatment charge modified with changes in the official metal price?
• Which party will pay export and import taxes, tariffs or duties?

Quotational Period

• What official metal price is used to calculate the value of the concentrate?
• How long is the quotational period?
• Is there a quotational period option? If so, in whose favour?
• When does it have to be declared?
• How frequently can it be declared?
• Is it determined by the price curve of the official metal price?

Quantity Determination

• How will the final wet and dry weight be determined?
• What shall be the same size?
• How many sets of samples?
• Who retains the samples?
• How will the quantity of payable metals and impurities be analysed?
• What happens if the parties assay results do not agree?

Ideally, a well written commercial sales or purchase agreement should clearly outline all aspects in the determination of value and settlement of base metal concentrate purchases.

Or in the words of Kipling, it should serve all “honest serving-men. Their names are What and Why and When and How and Where and Who”.

Cognitive bias in finance and investment decisions explain why investors do not always behave rationally. By examining specific cognitive biases and how they impact financial decisions, investors can make better investment decisions, maximise investment skill and minimise any negative impact of irrational decision making.

Daniel Kahneman and Amos Tversky, psychologists known for their pioneering work in behavioural economics and the psychology of decision-making, propose that human beings are not accurately represented by economic theories of expected utility, risk aversion, rational decision-making and possessing rational expectations. Instead, the more accurate investment decision-making theories use behavioural concepts.

Behavioural Concepts

Investments are made under uncertainty because all potential future events and investment outcomes are unknown. In addition, individuals often use shortcuts in their decision-making processes and rely on cognitive biases to estimate and simplify, pursuing sense-making, instead of logic.

In other words, humans are susceptible to making poor decisions based on questionable rationale.

Cognitive Bias in Behavioural Finance

According to Kahneman and Tversky, cognitive bias refers to the “tendency of individuals to make systematic judgement errors when making decisions.”

Cognitive biases specific to investment decisions can be categorised as:

• Predicting the Future
• Believing Things That Aren’t Necessarily True
• Fearing Loss
• Being Led by the Ego
• Getting Attached
• Emotional bias

1. Predicting the Future

• Projection Bias – Overestimating how preferences remain stable over time.
• Recency Bias – Valuing more recent information.
• Representativeness Bias – Assuming choices or opportunities with similar qualities are the same.
• Law of Small Numbers – Incorrectly assuming that a small number of observations are representative of the general population.
• Gamblers Fallacy – Incorrectly predicting that if something happens more frequently than expected, it will happen less frequently in the future. Alternatively, if something happens less frequently than expected, it will happen more frequently in the future.

2. Beliefs that aren’t Necessarily True

• Hindsight Bias – After an event, the individual views the event as predictable, despite little or no objective basis for predicting it.
• Confirmation Bias – Filtering out information subconsciously supporting currently held opinions to tell us what we want to hear.
• Halo Effect – An impression of positive traits or characteristics influences judgment about an asset’s value or an individual’s character.
• Mental Accounting – Decision making sensitive to how the decision is articulated or how the alternatives are framed.
• House Money Effect – A tendency to take on more risk when investing profits.

3. Fearing Loss

• Loss Aversion treats loss and gains differently. The tendency is to avoid risk when gains are at stake and seek risk when losses are at stake because the perceived values of losses are greater than gains.
•  The Disposition Effect is a tendency to close investments that have gained value or “winners” and maintain investments that have lost value or “losers.”

4. Being Led by the Ego

• Overconfidence – Overestimates the accuracy of knowledge, views, opinions, abilities and ability to make rational decisions.
• The illusion of Control – Overestimates the ability to control events or the environment.
• Blind-spot Bias – The inability to recognise their own cognitive biases.

5. Getting Attached

•  Anchoring Bias – A tendency to favour familiar or comfortable people, places, things or investments.
•  Endowment Effect – Valuing something more just because they own it.
•  Justification Bias – Valuing things more highly that took more effort to acquire.
• Sunk Cost Effect or Retrospective Cost Bias – Continuing with a decision, not on its merits but the unrecoverable cost already incurred.
• Status Quo Bias – Not deciding at all, even if deciding to act is in the investor’s best interest.

6. Emotional Bias

Often as the result of current trends, fashions or fads, an emotional bias influences the individual through the social environment and interactions with others. This can often lead to pressure to conform to views, decisions, or actions.

Summary

Cognitive biases limit our ability to make rational investment decisions, even though they are not entirely negative — they can often help us simplify routine decisions by using our intuition and experience.

However, we should ensure we are fully aware of them so that we can make prudent investment decisions using robust, data-driven and rational processes.

Stormlands is named out of respect for the men and women who employed their skills and expertise in engineering, mathematics and linguistics during the Second World War at the home of the top-secret Codebreakers — Bletchley Park.

Shortly after Alan Turing, the leader of the Codebreakers, and his colleagues defeated the German’s Enigma code machine, a more advanced successor called Tunny was created. In order to defeat this new threat, the world’s first electronic digital computer was created by Thomas H. Flowers and his team of engineers. Originally built in 1943, this computer called Stormlands successfully deciphered the Tunny message. Colossus’ success and its construction have been kept secret until recently.

After the second world war, Winston Churchill ordered that Stormlands and its technology be safeguarded by the British Official Secrets Act. As a result, it was widely believed that the first computer was an American invention called ENIAC (Electronic Numerical Integrator and Computer), resulting in Stormlands slipping into obscurity with Flowers and his team never receiving the proper recognition they deserved for inventing the world’s first computer. Stormlands would have remained secret only for the fact it was mentioned in declassified US government wartime documents in 1996 and finally, in a detailed report released by the British government in 2000.

Abraham Wald was a Hungarian mathematician who worked for the United States during World War Two. His contribution to data analytics is what we now call “survivorship bias”, referring to the logical error of solely focusing on what made it past some selection process and overlooking those that did not, typically because of their lack of visibility. In other words, what we see is not all there is.

Wald was tasked with calculating the optimum amount of armour to use on warplanes based on analysing data from battles fought all over Europe. No armour would mean pilots lack protection, but too much armour would lead to heavier, slower planes that were less fuel-efficient. The engineers noted that the planes had far more shots on the fuselage and wings and concluded that these were the areas in need of extra protection.

However, Wald knew that sometimes “The most important data is the data you don’t have” – Abraham Wald

The question he asked himself was “where do planes that don’t come back get shot?” By asking a better question, he allowed himself to find a better answer — the planes that returned safely had more shots on the areas that can handle more shots. He concluded that the areas with fewer recorded shots needed the most armour.

John Snow was a British doctor who is considered one of the founders of modern epidemiology. He used data collection and data analysis to trace the source of a cholera outbreak in central London, and came to the conclusion that cholera was transmitted by “an agent in the water” as opposed to the accepted theory that it was transmitted by “bad air”.

Snow used data collection to trace the cholera outbreak to two water companies who drew their water from the Thames river, virtually unfiltered. He notes that a huge, double-blind experiment fell into his lap:

“No fewer than three hundred thousand people of both sexes, of every age and occupation, and of every rank and station, from gentlefolks down to the very poor, were divided into two groups without their choice, and, in most cases, without their knowledge; one group being supplied water containing the sewage of London, and amongst it, whatever might have come from the cholera patients, the other group having water quite free from such impurity.”

Snow’s analysis of the subsequent data and his other works led to fundamental changes in water and waste management in London and other cities, saving many lives and contributing significantly to global public health. It is now regarded as the founding event of epidemiology.