Dartboard Energy Is Betting That Battery Operators Need an Analyst, Not a Dashboard

The Macro: The Grid Storage Boom Has an Analytics Gap

Grid-scale battery storage is one of the fastest growing segments in energy. The US alone added over 10 GW of battery storage capacity in recent years, and projections show the market growing at 20%+ annually through the end of the decade. Every solar farm paired with storage, every utility buying frequency regulation services, every independent power producer running an arbitrage strategy on wholesale electricity prices needs to know when to charge, when to discharge, and when to sit still.

The problem is that the tooling hasn’t kept up with the hardware. Most battery operators manage their assets with a combination of SCADA systems, Excel models, and energy management platforms that were designed for conventional power plants. The data exists. Real-time pricing signals, weather forecasts, grid congestion patterns, regulatory filings. But synthesizing all of it into a clear dispatch recommendation at 6am when day-ahead markets are clearing is still largely a manual process. Human analysts do it, and they’re expensive, slow, and don’t scale.

This is the kind of problem that AI should be good at. High-dimensional data, time-series patterns, quantifiable outcomes. The energy analytics space has players like cQuant, REsurety, and the analytics arms of larger firms like Fluence and Tesla. But most of these are either consulting-heavy, focused on project development rather than operations, or bundled with hardware in ways that limit their applicability to independent operators.

Dartboard Energy is going after the operational layer specifically: giving battery owners and fleet managers an AI analyst that handles the day-to-day questions about their assets.

The Micro: Ask It a Question, Get an Answer in Minutes

The product’s core interaction model is surprisingly simple for an energy sector tool. You ask Dartboard a question about your fleet via email, Slack, or Teams. It analyzes your operational data and responds with root-cause analysis, typically within minutes. No dashboard login required. No training on a new platform. Just conversational access to your own data.

That’s a deliberate design choice, and it’s interesting. Most energy software companies lead with dashboards full of charts and real-time visualizations. Dartboard is saying, essentially, that dashboards are the problem. Operations teams don’t need another screen to watch. They need answers to specific questions: Why did Site 7 go offline at 3am? Is our availability below the contractual threshold this month? Does the vendor owe us a payment based on last quarter’s downtime?

The product handles alarm triage automatically, sorting hundreds of alerts into categories to surface the ones that actually matter. It classifies downtime as excused or unexcused for warranty tracking. It monitors availability against contractual guarantees and generates proactive alerts when you’re trending below targets. These are the kinds of tasks that operations teams spend hours on weekly and still get wrong sometimes because the data is scattered across three different systems.

The integration approach is read-only access to existing EMS/SCADA historians, which lowers the deployment risk significantly. Nobody wants to give a startup write access to their battery management system. Read-only integration that takes days instead of months is a much easier sell to operations directors who’ve been burned by six-month software rollouts before.

Founder and CEO Rohun Ati has a background worth noting. He was the 10th employee at Second Measure, the credit card transaction analytics company that was eventually acquired by Bloomberg. At Second Measure, he built the data pipelines for what became a first-to-market credit card transaction dataset now used on Bloomberg Terminal. After that, he spent nine months at AutoGrid in a contract role specifically to understand the energy sector’s pain points firsthand. That’s a deliberate career move: learn the domain before building the company. Dartboard is a YC W25 company with a two-person team.

The Verdict

The energy storage sector is a genuinely good market for AI analytics. The data is structured, the decisions are quantifiable, and the cost of bad decisions is measured in real dollars. Dartboard’s bet that operators want conversational access to their data rather than another dashboard is contrarian in the right way. Most enterprise software adds complexity. This is trying to subtract it.

What I’d want to see is how the AI performs when the grid gets weird. Normal operating conditions are one thing. But energy markets have tail events: polar vortexes, demand spikes, negative pricing, interconnection outages. The value of an analyst, human or AI, is measured in those moments, not during routine operations.

The competitive question is whether Dartboard can build enough domain-specific intelligence to stay ahead of general-purpose AI tools that energy companies might try to build internally. A big utility with a data science team could theoretically wire up Claude or GPT to their SCADA system. Dartboard’s advantage is that they’ve already done it, and they understand the specific data structures and regulatory context of energy markets. That head start matters, but it’s not permanent.

Two-person team, focused market, clean product thesis. This is the kind of company that either gets acquired by a major energy software player or becomes one.