AiDASH wants your utility to stop trimming by the calendar

A tree has no use for a five-year plan. It grows when the soil, water and weather permit. A utility, meanwhile, has budgets, contractors and a maintenance calendar. Put the two beside a power line and the administrative problem can become an electrical one.

AiDASH has built a business around that mismatch. Its proposition is pleasantly specific: look at the vegetation across a utility’s network, work out where trouble is developing, then help the utility send people to the places that need attention. Space supplies the view. Software supplies the priorities. Someone on the ground still does the cutting.

A tree has no use for your calendar

In AiDASH’s account of a Massachusetts trial, National Grid wanted a fuller picture of vegetation around its distribution lines. The familiar alternatives included helicopter surveys, drones and inspections on foot. Each could reveal something useful. The challenge was gathering enough information across a large network to make better decisions about the whole thing.

AiDASH’s analysis helped the utility test a change from a five-year pruning cycle to nearly six years. That small adjustment is the revealing detail. A condition-based program can identify urgent work, but it can also identify work that can wait. A truck and crew dispatched too early consume money that might be needed somewhere less forgiving.

The same case study describes identifying places where falling trees deserved priority. That is a different problem from a branch slowly growing toward a wire. A useful maintenance plan must distinguish the two. Treating every mile alike is convenient for scheduling; it does not mean every mile presents the same risk.

“We’re dealing with an asset that is very dynamic.”Bertram Stewart, vegetation strategy manager, National Grid

This remains a company-published trial account, rather than a promise that any utility can safely add a year to its cycle. The interesting idea is the decision rule: inspect the condition, weigh the consequence, and allocate the work accordingly. The calendar becomes one input among several.

The useful output is a work order

The founders arrived at the problem from a much wider view. Abhishek Vinod Singh and Rahul Saxena describe discussing the 2018 Camp Fire over dinner in San Francisco, with satellite images prompting questions about what could be learned before the next disaster. Nitin Das joined them. In 2019 they formed AiDASH, then researched where remote monitoring could solve a pressing business problem.

Vegetation management became the starting market. Entergy was the first customer. The founders say they operated for 20 months without external funding. Their expertise combined software entrepreneurship, product engineering and machine learning; their opportunity was a recurring utility task with an existing budget and serious consequences.

The Intelligent Vegetation Management System, or IVMS, launched in 2020. Its 2.0 release in September 2023 made the scope clearer: remote imagery, data preparation, vegetation models and an operational workflow. AiDASH procures and processes remote-sensing information, combines it with utility records, and turns the results into plans people can execute.

That last part matters. A risk map can be impressive and still leave a planner with the same afternoon of spreadsheets. IVMS includes planning, assignments and tracking. The buyer is purchasing a route from observation to action, with the satellite analysis embedded in the daily work of managing a network.

One city puts a price on the view

Enterprise software prices tend to disappear behind a request for a demonstration. Municipal records are less coy. In December 2025, Lakeland’s city commission approved an IVMS agreement for Lakeland Electric: $99,900 for a one-year pilot, with four additional one-year renewal options.

The minutes give an estimated total of $499,500 if those options are exercised, including a one-time implementation, configuration and integration fee. The first year was included in the utility’s fiscal 2026 budget; subsequent spending depended on later budget approvals. These are the terms of one deployment, not a price card for every customer.

The purchase describes satellite analytics, multi-year budgeting, mobile workforce workflows and a unified dashboard. That is the SaaS model in practical form: ongoing access to software and intelligence, with configuration and integration to make it useful inside an existing organization.

AiDASH advertises lower vegetation expenses and better reliability. Those results deserve testing against a utility’s own baseline, terrain and work program. For a buyer, the useful questions are plain: which jobs changed, which interruptions declined, and did the savings exceed the cost of the software and its implementation?

Four risks share the same power line

Vegetation was the entry point. The current grid platform brings it together with asset inspection, wildfire risk and storm preparation. The logic is physical: a tree, a conductor, dry ground fuel and tomorrow’s wind can affect the same stretch of line, even if different departments manage their records.

IVMS handles vegetation planning. AIMS, the Asset Inspection and Monitoring System, organizes geospatial asset information and inspection work. CRIS Wildfire addresses ignition and spread risk; its February 2025 upgrade added attention to changing vegetation and fuel conditions. CRIS Storm supports outage forecasting and preparation for severe weather.

In January 2026, Spire Global announced that AiDASH had selected its weather intelligence, including high-resolution forecasts and meteorologist support. The point of combining the inputs is more useful than weather prediction alone: forecast how a storm may interact with the network’s particular conditions, then prepare crews accordingly.

The company also sells Wildfire Mitigation Planning Services, pairing practitioner-written plans with IVMS Core. Its adjacent BNGAI offering serves developers, ecologists and landowners through habitat assessment, biodiversity planning and reporting. These products apply related remote-sensing skills to distinct decisions; buying one does not imply a need for all of them.

The satellite keeps company

AiDASH calls its approach SatelliteFirst. Its own platform shows why the second word should not be mistaken for “only.” Broad satellite monitoring is combined with vehicle-mounted and aerial surveys, then drones and manual inspections when finer detail or verification is needed.

A network-level picture and a close look at a component answer different questions. The platform’s mix suggests a practical limit: use broad observation to guide closer investigation, and preserve the field checks necessary for the decision at hand. A prediction also needs crews, access and resources behind it. Without execution, even an excellent risk ranking remains a list.

Overstory is a direct alternative in vegetation intelligence, also combining AI with remote sensing to support utility work programs. AiDASH’s wider pitch includes asset, wildfire and storm workflows alongside vegetation. That describes product scope, not an established performance advantage. Utilities can also retain internal GIS planning and use survey contractors, with some of those inputs feeding the software.

A grid software buyer looks up

Investors included organizations familiar with the buyer’s problem. AiDASH raised a $6 million Series A in 2020 and a $27 million Series B in 2021. Its Series C closed at $58.5 million in April 2024, bringing reported cumulative funding to $91.5 million. Utility investors participated alongside venture and climate funds.

Schneider Electric’s July 2026 financial release disclosed a June agreement to acquire approximately 90% of AiDASH at an implied enterprise value of $350 million. It proposed adding AiDASH to a grid software portfolio that includes ArcFM, ADMS and DERMS. The release made completion subject to closing conditions and regulatory approvals.

That transaction value is distinct from money raised by the company. Strategically, the proposed combination connects information about external network risks with software used to manage the grid. AiDASH had also earned a 2025 BloombergNEF Pioneers award for climate adaptation, recognition of the same broad problem: existing infrastructure must cope with changing conditions.

The lesson another operator can borrow is modest enough to be useful. Pick a costly decision made repeatedly. Find where a convenient rule hides meaningful differences. Test an alternative locally, then connect it to the people doing the work. In AiDASH’s case, the argument begins high above the ground and ends with a crew choosing the next tree.