Energy is starving for coordination, and AI will feed it.

The energy transition has driven massive investment—over $25 trillion globally. We now have millions of solar panels, batteries, electric vehicles, and smart devices connected to the grid. The industry is now powered by converter chips, generating terabytes to petabytes of time series data on energy infrastructure. With each one of these new assets, we are now presented with a nearly endless stream of data capable of transforming the operations and effectiveness of our industry. However, we have thus far fallen short in achieving the realizable potential this avalanche of data offers. No longer.

As the availability and diversity of data changes, so does its complexity. What was once vertically integrated data has now been opened up to multi-tenant structures where we must find ways for the grid (and our industry) to operate as one. This is made difficult by the fact that utilities, co-ops, retailers, grid operators, and device makers all run on different software and protocols, further reinforcing data silos. To move forward, we need to break down these silos.

The jobs to be done are clear to us:

We must find ways to ensure ubiquity of data - how can we ensure that the data is consistent and available to all who need it, at the time they need to use it?

We must find ways to optimize the utility of this data - how can we make sure that data is standardized, secure, and streamable for internal and cross-company needs?

We must find ways to put this data to work - how can we leverage best-in-class AI approaches to leverage this data to enable efficiencies and unlock business opportunities?

Legacy systems designed for the energy industry aren’t able to answer these questions. And while other industries have found their solution—Stripe for payments, Epic for health records, ServiceNow for IT— the energy industry has thus far lacked the infrastructure necessary to bring data to life and enable enterprises to act upon it.

Why is now the moment to address this challenge? We have rising electricity costs, decreasing reliability and increasing demand variability. Thanks to distribution and deregulation across the industry and the maturity of AI, we are now seeing a window to rewire how the energy industry orchestrates its data and operates with one another.

#Integrations, data cloud, and agentic workflows all in a single platform

Over the first 2 years of our company, we’ve realized that the solution to this challenge isn’t in isolation, but rather through the coordination of several systems operating together. Inconsistency in standards and data models has made it all too difficult to hear signal through the noise of messy data, creating a need for a solution that can transform raw data for a platform that serves all stakeholders.

We’ve built Texture to do just that — unify all your integrations, make it accessible and interactive in a secure data cloud and build and deploy workflows on top to drive efficiency and unlock revenue. Most importantly, we've designed Texture to build into your existing stack, not replace it.

#1. Integrations

To get the full value of AI and automation, we’ve got to solve the integration problem. In energy, every critical piece of operational data lives in a different system, managed by a different team. We integrate all these inputs — across internal systems, field devices, third-party APIs, and even external stakeholders like grid operators, contractors, and regulators — into a connected, real-time site picture. This includes everything from billing data, customer records, asset and maintenance history, performance trends, meter data, grid and distribution data, geospatial data and device data — all in a single place that can tell you more about the composite picture of a specific device, customer and/or location in relation to the grid.

#2. Data Cloud

Data provisioning can be a challenge, but it rarely drives enterprise value on its own. We built the infrastructure for working with all this data—streaming capabilities, security frameworks, and access. This gives everyone real-time visibility into what's actually happening across their energy operations, both internally and externally. We have customers across every major sub-sector of the energy space now using Texture to get the most value out of their data.

For example, we work with a large CCA. They want to incentivize customers to shift load during peak times, but things break down right away. Their meter data provider receives bulk data but has no easy way to share it, besides to their data warehouse. There is a small engineering team to process it, and no way to easily match meter data to participating customers. They can't connect to the batteries from different manufacturers (Tesla, Enphase, LG, etc.), have no way to monitor or control those batteries during events, and can't communicate performance results back to customers afterward.

With Texture's data cloud, all these broken connections get fixed. The CCA can access real-time meter data, automatically match it to participating customers, connect to and control batteries from any manufacturer, and provide customers with immediate feedback on their participation—all through one unified platform.

#3. AI Workflows

With all your data in one easy-to-use environment, the most frequent request we are receiving from our customers is to build and deploy agentic workflows to draw as much business value as possible. Some of the most commonly requested workflows are customers are looking for are:

Monitoring and Anomaly Detection: We address the fundamental challenge of data overload. With millions of devices generating constant data streams, organizations are drowning in information but starving for insights. Our AI cuts through noise, flagging problems that would otherwise be buried, and delivering them where action can be taken. This builds trust while creating the foundation for more sophisticated capabilities.

Summarization and Pattern Recognition: We transform raw monitoring data into useful intelligence, identifying patterns across programs, assets, and performance that humans couldn't detect at scale. Pattern recognition gets better through learning from coordination outcomes across our multi-sector platform.

Control and Automation: Only after building trust and demonstrating value do we move to automated coordination. AI takes controlled actions within defined boundaries, automating routine coordination while keeping human oversight of strategic decisions.

These workflows ensure that companies are able to prove business outcomes from their investments in data infrastructure modernization and we’ve delivered many of these solutions within weeks of contracting while demonstrating game-changing productivity enhancements for our clients. As energy systems become more complex and distributed, the need for AI coordination will only grow. By building AI that coordinates energy operations rather than just observing them, we're delivering real value where others have struggled. The future of energy isn't just digital—it's AI-enabled.

#The bottom line

At Texture, we’re building the data infrastructure to rewire the energy industry. This requires the right data, the right data infrastructure and the right AI tooling to bring all of this to life. Just like Stripe transformed payments and Epic transformed healthcare, we're building the platform that will transform what data means to energy companies and pull our industry forward into the age of AI.

Come join us and see the tomorrow of the energy industry, today.