GridSignals reads the physical state of the grid — wind, reserves, generation, battery behavior, transmission constraints — and distinguishes system scarcity, local congestion, and node-level basis risk at your settlement point. It turns that into prepare, hold, act, and resume decisions — reduce load or switch to on-site generation when an event is real — and leaves behind an independent, source-anchored record of what the grid did and how your site responded.
The prediction tells you when to act. The record is what lenders, tenants, and grid operators can underwrite — a defensible account of grid-stress behavior that your own control logs can't be, because no one grades their own homework. Built natively on post-RTC+B ERCOT data, at your settlement point, not the grid average.
Most days, you do nothing. When an event is real, know whether to reduce load, switch to on-site generation, or wait for resolution — mechanism context prevents unnecessary downtime from congestion that resolves in minutes.
Demonstrate operational credibility to enterprise AI tenants and lenders before your site energizes. Grid intelligence as infrastructure, not an afterthought.
Every recommendation, action, and outcome documented. The audit trail that CFOs, lenders, and boards need to underwrite operational risk.
Enterprise AI customers ask one question that separates the tiers: what happens to my workload when the grid gets tight? Tier 1 answers with hardware. Tiers 2 and 3 need a different answer.
Sovereign-scale infrastructure. They acquire nuclear plants, sign 20-year PPAs, and spend billions on redundant power systems. Grid exposure is a rounding error.
Competing for enterprise AI workloads with speed and price. But they operate in deregulated markets where grid volatility is real. They need Tier 1 credibility at Tier 2 economics.
GridSignals targetAdding GPU density and AI tenants. Inheriting grid exposure their customers used to manage. Need operational intelligence they've never had before.
GridSignals targetMost tools answer one question. GridSignals answers three — the three questions an operator actually needs before making a curtailment decision.
Machine learning models trained on post-RTC+B data detect when grid conditions are deteriorating — reserve compression, AS tightening, wind underperformance, load surprises. Distinguishes system-wide scarcity from localized congestion. The physical precursors move before the energy price does; how much lead that yields varies by event.
A proprietary deliverability layer answers the question other tools ignore: even if system-wide reserves look adequate, can marginal generation actually reach your settlement point? When headline reserves mask local risk, deliverability catches it.
Rate-of-change signals track how fast the grid's pricing engine is climbing the supply curve. This distinguishes an evening ramp that resolves at $60 from one that escalates to $200 — before the outcome is determined.
Your bill is determined at your settlement point, not the system average. During congestion, these can be $400 apart.
Scarcity moves the system. When reserves compress grid-wide, everyone's prices rise together.
Congestion moves the hub. Transmission constraints spike one zone while others stay calm.
Basis moves your node vs the hub. Your meter can diverge from the hub average by $30+ during local constraints.
GridSignals monitors all three levels. Most tools stop at the system. We track the corridor constraints, import paths, and local conditions that determine what you actually pay.
Based on observed ERCOT market data. GridSignals classification from shadow system during live operation. Similar congestion dynamics exist in PJM, SPP, and other nodal markets where transmission constraints create localized price separation.
A record you simply have to trust isn't proof. Everything we produce is built to be independently checked — because the whole point is that no one grades their own homework.
Built on ERCOT's public market data — the physical record of what the grid actually did, not a proprietary feed you have to take on trust.
The account comes from outside your operation. That is what makes it something a tenant, a regulator, or a noteholder will accept — precisely because you did not write it.
A fixed, timestamped account of what the grid did and how your site responded — not a verdict you are asked to take on faith.
The standard we hold ourselves to is the one we would want from anyone documenting our grid behavior: claims you can check, not claims you have to believe.
The grid average and your settlement point can be $400 apart during congestion events. We monitor the physical signals specific to your location.
Detects when system-wide conditions are deteriorating — reserve depletion, AS tightening, and supply-demand imbalances that build before the energy price moves. Lead time varies by event: slower-building scarcity gives more, sudden disturbances give little.
Localized price spikes from transmission constraints and weather-driven generation shortfalls. Your settlement point can spike $400 while the rest of the grid is calm. We detect these independently.
Rapid detection of sudden grid disturbances — generator trips and rapid imbalances that can't be predicted, only detected fast.
Every alert tells you why — scarcity, congestion, or load surprise. The mechanism determines your response. Scarcity means act — curtail, or switch to on-site generation. Congestion often means wait. Knowing which prevents unnecessary downtime.
Most days, nothing happens — and you hear nothing. The product is for the handful of moments that matter: each event produces at most three messages — prepare, act, resume — telling you what's happening physically, not just where the price is. No alert spam.
Multiple independent signals are converging at your settlement point. The alert tells you the mechanism — scarcity, congestion, or load-driven — and the expected severity. Time to stage your response — pre-position to reduce load or start on-site generation before the event.
Physical confirmation that the event is real — time to reduce load or switch to on-site generation. The decisive moment, not a false alarm.
Prices can dip during an ongoing event and rebound minutes later. We hold the all-clear until the underlying stress has genuinely resolved — reserves rebuilding, generation recovering, grid returning to normal.
The traditional answer to grid exposure is hardware — heavy UPS systems, switchgear, and battery buffers costing tens of millions per site. For a Neocloud operating hundreds of megawatts across ERCOT, that capital is prohibitive.
GridSignals provides the same operational credibility through software. Instead of buffering grid volatility with hardware, we read the physical precursors and orchestrate the response before the event reaches the facility. The enterprise customer gets timestamped receipts proving their workload was never at risk.
The first answer requires capital. The second requires intelligence. Both deliver credibility. Only one scales at Tier 2 economics.
Lenders and project finance teams underwrite operational risk. A facility that can demonstrate predictive grid intelligence — with timestamped receipts showing every event was anticipated, every response was documented, and every outcome was measured — is a lower-risk asset than one without.
GridSignals produces the audit trail that CFOs, lenders, and board members need: what happened on the grid, what the system recommended, what action was taken, and what cost was avoided. That documentation turns operational intelligence into a financing advantage.
Most operators can identify obvious candidate days. The hard part is deciding which candidates are real, which are false alarms, and how tightly to operate around them.
The harder layer is what survives at settlement once storage behavior, forecast changes, and collective response are in the mix. That's the layer we focus on.
Candidate positions update throughout the day as conditions evolve. When the day tightens or loosens, your ranking and action zone shift with it.
Optimistic, midpoint, and pessimistic scenarios instead of a single overconfident alert. You see how much uncertainty is in the adjustment.
Every candidate ranking, every adjustment, every decision documented. When the season ends, the record shows what happened and why.
ERCOT's RTC+B co-optimization went live in December 2025. Under the new market rules, batteries co-optimize across energy and five ancillary service products every five minutes. This fundamentally changed how prices form, when stress events develop, and which signals lead.
GridSignals is built natively on post-RTC+B data and market structure. Every model, every detection layer, every alert threshold was developed on the current market — not retrofit from pre-RTC+B assumptions.
Meanwhile, the ERCOT large-load interconnection queue has reached roughly 380 GW — against only ~2 GW approved to energize in the past twelve months. The gap is the point: sequential study could not move that throughput, which is why ERCOT moved to batched study. Large flexible loads are increasingly settling at points where localized congestion can spike prices $400+ while the rest of the grid is calm. System-wide monitoring tools don't see these events.
And the rules are catching up to the loads. ERCOT's Batch Zero framework moved through its board in June 2026 (PUCT review next), and ride-through requirements (NOGRR282) are becoming part of the interconnection case — with energization increasingly tied to demonstrating how a load behaves under grid stress. At the national level, NERC issued a rare Level 3 alert in May 2026 after repeated events where 1,000+ MW of computational load dropped off the grid in seconds, and is moving toward registration and reliability standards for large loads. The direction is unambiguous: flexibility is shifting from an operating preference to a registered, studied, provable attribute of how you connect — and a defensible record of how your site behaved becomes something lenders, tenants, and operators can underwrite.
The system improves after every event. When grid conditions produce an outcome our models haven't seen before, the system identifies the gap, incorporates the new evidence, and sharpens detection for the next event. Patent pending.
These dynamics are not unique to Texas. In PJM, rising 5CP capacity charges, increasing battery penetration, and growing data center load create the same need for settlement-point-specific operational intelligence. GridSignals is expanding to PJM in Summer 2026, built on the same physics-first architecture.
We deploy selectively by market and load profile to preserve signal quality and actionability. When many participants respond to the same signal, collective behavior can shift which intervals count. Our architecture is built to manage that uncertainty.
Contact UsReal-time operational posture with mechanism context. Not just "price is high" but "this is wind-driven congestion on the Panhandle corridor, likely resolves in 15 minutes."
Historical analysis of your specific settlement point — what events hit your node, what the system saw, what it would have recommended, and how much lead time was available.
After every event: what happened, what was recommended, what action was taken, what cost was avoided or incurred. Timestamped and auditable.
Synthesis of the week's grid conditions, events, model performance, and system health. What the grid did, what we saw, and what we learned.
For pre-energization sites: settlement-point risk profile, historical exposure analysis, a response-strategy framework (curtailment and on-site generation), and operational credibility documentation for lenders and tenants.
Ranked candidate intervals with uncertainty bands, intraday re-ranking, and settlement-aware timing. Receipts after each season showing what happened and why.
Designed and built the entire GridSignals platform — data infrastructure, ML models, real-time detection architecture, and autonomous operational systems.
Energy markets expertise across ERCOT, PJM, and other deregulated markets. Leads commercial strategy and customer relationships.