The Problem, Climingo Is Trying to Solve

As climate risk moves closer to operational decision-making, more organisations are relying on weather and climate data vendors. But choosing between providers is still surprisingly difficult. Different models, different claims, different formats, and limited comparability make procurement harder than it should be. Climingo was created to make that process more transparent and evidence-based.

Climate and weather data are becoming more important across insurance, agriculture, infrastructure, supply chains, resilience planning, and public-sector decision-making. Organisations increasingly understand that they need better forecast inputs, better climate intelligence, and better risk signals. But once they start looking for providers, a new problem appears.

It is often very hard to know who is actually better for a specific use case.

That challenge sits at the heart of Climingo.

More Providers, More Choice, More Confusion

At first glance, more choice sounds like a good thing. The weather and climate-data market includes a growing number of providers offering forecasts, APIs, analytics platforms, risk layers, and sector-specific products. In theory, that should make it easier for organisations to find a suitable solution.

In practice, it often creates confusion.

Different providers may use different forecast models, different post-processing methods, different update cycles, different spatial resolutions, and different ways of presenting performance. Some emphasise accuracy. Others emphasise coverage, sector expertise, scenario capabilities, delivery speed, or integration flexibility. Many providers describe their products well, but it is still hard for buyers to compare them in a clear, fair, and decision-ready way.

That means the buyer is often left trying to compare things that are not presented in comparable terms.

Why Choosing a Provider Is Harder Than It Should Be

For many organisations, buying climate or weather data is not like buying a standard software subscription. The value of the product depends on how well it performs for a particular geography, lead time, variable, and operational need.

A forecast provider that works well for one region may not perform the same way in another. A provider that is useful for daily operations may not be the best fit for seasonal planning. One product may be strong for precipitation, another for temperature, and another for probabilistic interpretation. The challenge is not only whether a provider is good in general. It is whether that provider is the right fit for the actual decision context.

That is where procurement becomes much more difficult than it first appears.

Buyers often face questions such as:

Which provider performs better for my use case?
How do I compare forecast quality in a fair way?
What do the performance claims actually mean?
Which variables, horizons, or locations matter most for my business?
Are we comparing like with like, or just comparing marketing language?

These are not small questions. They directly affect the value of the procurement decision.

The Transparency Gap

One of the clearest problems in this space is the lack of transparency.

Providers may publish strong claims, impressive dashboards, or technical summaries, but buyers often still struggle to form an independent view. Performance information may be incomplete, presented in different ways, or difficult to compare across vendors. In some cases, the issue is not that information is hidden. It is that it is not standardised or decision-friendly.

This creates a transparency gap.

Buyers may understand that provider quality matters, but still lack a reliable way to assess that quality independently. They may need to rely on sales conversations, selective case studies, limited trial periods, or internal assumptions. That process can be slow, uncertain, and difficult to defend internally.

For organisations making important operational or strategic decisions, that uncertainty is a real problem.

Procurement Friction Is a Product Problem

A lot of people think of procurement friction as an administrative issue. But in climate-tech, it is often a product problem as well.

If buyers cannot clearly compare providers, cannot interpret performance consistently, and cannot connect product claims to their own needs, then the market is harder to navigate than it should be. That slows decision-making, increases uncertainty, and can lead to poor fit between the buyer and the chosen provider.

This friction affects both sides.

Buyers struggle to choose confidently.
Providers struggle to communicate their strengths in a fair and comparable environment.

That is why the problem is bigger than a single company or a single purchase. It is a structural issue in how climate and weather data are evaluated and selected.

Why Independent Benchmarking Matters

This is where independent benchmarking becomes important.

An organisation choosing a weather or climate-data provider should not have to rely only on sales positioning or isolated examples. It should be possible to evaluate providers through more structured, transparent, and evidence-based comparison.

Independent benchmarking can help create that missing layer.

It can make comparisons more grounded.
It can help buyers understand trade-offs more clearly.
It can reduce the distance between technical performance and procurement decisions.
It can support more confident, more defensible choices.

Most importantly, it helps move the conversation away from vague claims and toward measurable suitability.

That does not mean every provider must be judged by exactly the same criteria in every context. The opposite is true. Different organisations have different needs. But those needs still deserve a clearer evaluation framework than many buyers currently have.

Climingo’s Starting Point

Climingo was created in response to that problem.

The core idea is simple: organisations need a better way to compare, benchmark, and choose weather and climate-data providers. Not in theory, but in a way that is practical for real procurement and real operational use.

That means helping users move from broad market noise to clearer evidence.

It means asking questions such as:

What is being compared?
Under which conditions?
For which variable?
At what lead time?
For which geography?
Against what benchmark?
And how does that connect to the user’s actual decision needs?

These are the kinds of questions that should sit at the centre of climate-data procurement, but they are often not handled in a consistent way.

The Need for Better Market Infrastructure

Another reason this matters is that climate-data markets are maturing.

As more organisations depend on climate intelligence, there is a growing need for supporting market infrastructure around that ecosystem. It is not enough to have more providers. The market also needs better mechanisms for evaluating and selecting them.

In many sectors, comparison and benchmarking tools emerge naturally as markets grow. Buyers need ways to assess options. Vendors need fairer ways to show value. Intermediary layers appear to reduce friction and improve market clarity.

Climate-tech needs more of that infrastructure.

Climingo is part of that broader need. It is not trying to replace weather or climate-data providers. It is trying to make the process of understanding and comparing them more transparent and more useful for buyers.

Why This Problem Will Only Become More Important

The importance of this challenge is likely to increase, not decrease.

Climate risk is moving into more operational decisions. Organisations are asking harder questions about weather exposure, resilience, supply-chain vulnerability, agricultural planning, infrastructure risk, and adaptation strategy. As that happens, they will depend more on external data and model providers.

That makes provider choice more consequential.

A poor fit can mean wasted budget, weak operational signals, or a false sense of confidence. A strong fit can improve planning, strengthen resilience, and support better long-term decisions. The difference matters.

As demand grows, the ability to compare providers properly becomes more valuable.

From Data Access to Decision Confidence

One way to think about this is that the market challenge is not only about access to data. It is about confidence in decision-making.

Many organisations can access weather and climate information in some form. But access alone does not solve the harder question: which source should we trust for our needs, and why?

That is the question Climingo is trying to help answer.

The aim is not simply to add another layer of information. It is to support a more confident and evidence-based decision process. That matters because climate-data procurement is often tied to decisions that affect operations, risk management, and resilience strategy.

A Founder’s View of the Gap

From my perspective, this is one of the most interesting gaps in climate-tech.

A lot of innovation has gone into producing forecasts, models, APIs, analytics products, and sector-specific tools. But less attention has been given to the question of how buyers should navigate this landscape with confidence. The market has grown faster than the comparison layer around it.

That gap is exactly the kind of problem that can become the basis for a venture.

Climingo comes from the belief that the climate-data market needs more transparency, better benchmarking, and a more decision-oriented way of connecting buyers with the right providers.

The Broader Vision Behind Climingo

The broader vision is to help make climate-data procurement more mature, more transparent, and more useful.

That means reducing unnecessary friction.
It means helping organisations compare providers more fairly.
It means creating more confidence around selection decisions.
And it means supporting a market where evidence matters more.

In the long run, this is about improving not only procurement, but also trust and usability across the climate-data ecosystem.

A better comparison layer can help buyers make better decisions and help providers compete more clearly on what they actually do well.

Closing Thoughts

Choosing a weather or climate-data provider should not feel like navigating a black box. Yet for many organisations, that is still the reality.

Different models, different claims, different metrics, and limited comparability create a process that is harder and less transparent than it should be. As climate intelligence becomes more important to real-world decisions, that gap becomes harder to ignore.

Climingo was created to help solve that problem.

Its starting point is simple: climate-data buyers need a more transparent, evidence-based way to compare providers and make better decisions. That is the problem Climingo is trying to solve.

To learn more, visit Climingo.