Tomorrow.io Secures $175M for DeepSky Satellites

Tomorrow.io’s $175M raise signals a new phase for weather intelligence

Weather has always been a universal conversation starter, but for enterprises and public agencies it has become something far more consequential: a real-time operational variable that can reshape cost, safety, service quality, and resilience within hours. In recent years, extreme conditions have repeatedly demonstrated how quickly disruptions can ripple through aviation, logistics, supply chains, and energy systems, especially when decisions need to be made minute by minute rather than day by day. That is the pressure-cooker context behind a major new funding milestone for Tomorrow.io, a company positioning itself at the intersection of atmospheric science, space infrastructure, and AI-driven decision tools.

Tomorrow.io has raised $175 million in an equity funding round led by Stonecourt Capital and HarbourVest Partners, with participation from several other investors named in the announcement. The company says the financing will accelerate DeepSky, its AI weather satellite network, while expanding its broader global atmospheric sensing system—essentially strengthening the pipeline from “what’s happening in the sky” to “what should an operator do next.” This is not framed as a simple forecasting upgrade; it is presented as an attempt to tighten the feedback loop between observation, prediction, and action, especially when organizations cannot afford to wait for slower cycles of data refresh.

From the perspective of the ai world organisation, the strategic thread here is familiar: AI is increasingly moving from “nice-to-have analytics” into infrastructure-like roles that determine competitiveness and continuity. That’s also why this story fits naturally into the discussions we host through the ai world summit, ai world organisation events, and the broader set of ai conferences by ai world—because it showcases AI not as a single model, but as an integrated stack that includes data capture, modelling, and distribution into real workflows. If you’re tracking the themes that shaped ai world summit 2025 / 2026, the shift toward AI-native infrastructure is one of the clearest signals, and satellite-enabled weather intelligence is a particularly tangible example of it.

Why “good enough” forecasts break down for modern operations

Most people experience weather through a general-purpose forecast: a temperature range, a probability of rain, and maybe a warning banner during major events. Enterprises experience weather very differently. For a logistics operator, weather can determine whether a fleet arrives on time or gets stuck; for an airline, it can influence routing, ground operations, safety buffers, and cascading delays; for an energy organization, it can affect load, maintenance windows, and asset exposure. The central challenge is not simply whether it will rain—it is where and when a condition becomes operationally meaningful, and how quickly the organization can respond.

Tomorrow.io’s pitch, as described in the announcement, is built around weather resiliency technology that combines its own satellites with generative AI and advanced modelling to deliver predictive, impact-based insights “down to the street level.” The message is that traditional approaches are often too slow and not detailed enough for real-time decision-making, leading to costly disruptions. In other words, it’s not just about accuracy in a scientific sense; it’s about actionability at the time-scale and granularity that operations teams need.

This distinction—forecasting versus decision intelligence—is one of the most important evolutions happening across applied AI. When AI is connected to business workflows, the output has to be interpretable, timely, and tied to decisions that carry financial and safety consequences. It also has to integrate with the systems operators already use, because insight that lives only in a dashboard is rarely enough. Many AI initiatives fail not because models are weak, but because the last mile—getting information to the right person with the right context at the right moment—is where complexity lives.

In weather, that last mile is particularly unforgiving. Conditions can change quickly, and even small shifts can cause large downstream impacts. So the more frequently you can sense the atmosphere, the faster you can update models, and the quicker you can communicate the likely operational outcome, the more value you can create. Tomorrow.io is explicitly leaning into that logic by emphasizing its ownership of data acquisition through satellites rather than relying solely on third-party sources.

DeepSky and the “full-stack” approach: from satellites to insights

Tomorrow.io traces its origin to 2017, when CEO Shimon Elkabetz and co-founders Itai Zlotnik and Rei Goffer identified what they saw as a gap between the needs of commercial weather intelligence and the tools available at the time. The company’s approach is described as vertically integrated, connecting hardware (satellites and sensing) to software (models and insights) and then to end-user outcomes (impact-based predictions). That vertical integration is a recurring theme in many high-performing AI businesses: controlling the data pipeline can be as important as improving algorithms.

According to the details provided, Tomorrow.io’s platform uses 13 active satellites and generative AI models to provide high-frequency monitoring. The company claims its system can produce nowcasts and forecasts with a 30-second delay and meter-level accuracy, positioning the product as closer to real-time awareness than periodic updates. It also highlights “impact-based predictions,” giving examples such as hail risk tailored to specific fleets, which reflects the company’s focus on operational relevance rather than only meteorological outputs.

DeepSky is framed as the next expansion phase, with a planned network of more than 100 satellites intended to support worldwide coverage. The funding is expected to accelerate DeepSky deployments, with an aim to achieve full constellation coverage by 2028 for continuous global sensing. In the near term, Tomorrow.io plans to double satellite launches in 2026, broaden its customer footprint beyond logistics and energy into areas like agriculture and defence, and launch climate resilience APIs. The company has also publicly described the financing as supporting the deployment of DeepSky and the expansion of its space infrastructure and intelligence platform.

It’s worth pausing on what “AI-native” means in this context, because it can be misunderstood. In practical terms, “AI-native” infrastructure usually implies that the system is designed from the start to feed AI models with the right frequency, coverage, and sensor diversity, rather than trying to bolt AI onto data that arrives too slowly or inconsistently. Tomorrow.io’s narrative is that legacy satellite infrastructure does not provide the real-time signals needed for the best algorithmic forecasting, and that more frequent, richer observation can unlock better model performance and better operational decisions. Whether you work in enterprise AI, climate tech, mobility, or defence-adjacent innovation, the underlying lesson is similar: the model is only as strong as the data engine behind it.

The company also positions itself against well-known forecast providers by drawing a line between broad public forecasting and proprietary enterprise-grade sensing. It contrasts its approach—owning satellites and producing proprietary data—with organizations that rely heavily on government data sources. That competitive framing is important because it shifts the debate from “whose forecast is better” to “who controls the observational backbone and can innovate on top of it fastest.”

Where this kind of capability matters most: enterprise use cases and societal resilience

Weather intelligence at this level is not only a business advantage; it can also become part of critical infrastructure thinking. When operators can anticipate disruptions earlier and with more location-specific detail, they can potentially reduce waste, protect people and assets, and maintain service continuity. In aviation, that could mean fewer surprise ground holds and more efficient rerouting. In logistics, it could mean proactive rebalancing of inventory or dispatch. In energy, it could mean improved planning for demand spikes or storms that threaten assets. These are examples of how operational resilience is increasingly data-driven.

Tomorrow.io’s announcement highlights industries like supply chain, aviation, logistics, and energy as areas where extreme weather has tangible operational impact. It also signals the company’s intention to expand into sectors such as agriculture and defence. Those expansions are logical because agriculture is highly weather-sensitive, and defence organizations often require high-confidence situational awareness across regions where public data coverage may be uneven.

There is also a broader societal frame that frequently accompanies climate and weather technology: adaptation. Even without making predictions about future climate trajectories in a single article, it is clear that preparedness, warning systems, and resource allocation benefit from faster and more granular information. The closer the world gets to real-time sensing and rapid prediction updates, the more feasible it becomes to treat weather risk like any other operational risk: measurable, monitorable, and manageable.

At the ai world organisation, we see this as part of a bigger story about how AI systems are becoming “decision layers” for the physical world. That’s why topics like AI-driven climate resilience, satellite data pipelines, and operational AI show up repeatedly in conversations around the ai world summit and our ai world organisation events, alongside more familiar themes like generative AI and enterprise adoption. For teams building real-world AI products, weather intelligence is a powerful case study in how to connect advanced models to real constraints—latency, coverage gaps, integration, and accountability.

This is also why the timing of such investments matters. A $175 million raise is not only about capital; it signals confidence that the market for weather intelligence is large enough and urgent enough to support the build-out of specialized infrastructure. It suggests that investors believe enterprises will pay for improved risk management and performance, and that the path to differentiation may be through proprietary data capture rather than purely software competition.

What’s next—and why it’s a headline for the AI ecosystem

Tomorrow.io’s roadmap, as described, is ambitious: accelerate DeepSky deployments toward full coverage by 2028, increase launch cadence in 2026, broaden industry reach, and offer new APIs focused on climate resilience. Execution will be the deciding factor, because building and operating satellite networks introduces complex engineering, launch logistics, and long-term maintenance considerations that many software-first companies never face. At the same time, if the company can deliver the frequency and quality of observations it promises, it could strengthen the data advantage that drives better model outputs and stronger enterprise outcomes.

For the broader AI ecosystem, this story reinforces a key pattern: the next wave of AI winners may be those who own or deeply shape the data backbone in their domain. In some sectors, that means sensors and edge devices. In others, it means partnerships and privileged access. In weather intelligence, it can mean satellites—an expensive, high-stakes path, but one that can create defensible differentiation if executed well.

This is also a timely topic for professionals and leaders who engage with the ai world summit community. If you want to understand where AI is heading, it’s worth tracking domains where AI is tightly coupled to physical infrastructure, because that’s where “AI for real-world outcomes” becomes impossible to ignore. The AI World Organisation continues to convene practitioners, builders, and decision-makers through global summits and programs, and our upcoming events are designed to spotlight exactly these kinds of practical, tactical lessons across industries.