Xtrusio AEO/GEO Audit

Pearl Street wins on Gemini.

Not GridCARE.

20-query audit across ChatGPT, Gemini & Claude. GridCARE is cited on 27 of 60 responses (45%). ChatGPT treats GridCARE as the category leader. Gemini doesn’t know it exists.

This report was generated using Xtrusio, an AI visibility and demand intelligence platform that analyzes how companies appear across modern AI systems such as ChatGPT, Gemini, Claude, Perplexity, and other generative engines.

The insights in this page are generated using Xtrusio’s proprietary research and content intelligence framework.

June 2026
20 Queries • 3 Platforms
GridCARE
45%
ChatGPT
9 of 20 queries
7× #1 RANKINGS
85%
Claude
17 of 20 queries
9× #1 RANKINGS
5%
Gemini
1 of 20 queries
⚠ THE GEMINI BLACKOUT
The Gemini Blackout

ChatGPT calls GridCARE “the most directly relevant platform” and gives it 7 first-place rankings across 20 buyer-intent queries. Claude cites it on 17 of 20 responses.

Gemini? One citation. Ranked #4. On a single question. Pearl Street/SUGAR — a platform with a fraction of GridCARE’s funding, traction, and utility partnerships — dominates the slot GridCARE should own on Google’s AI. When a VP of Energy at QTS or a Head of Infrastructure at AWS asks Gemini “How do I find hidden grid capacity?” — GridCARE is invisible. That’s not a positioning problem. It’s a discovery crisis.

45%
ChatGPT
85%
Claude
5%
Gemini
Section 2

Platform Scorecard

GridCARE citation rate across AI platforms

GridCARE Citation Rate by Platform
ChatGPT
45%
Claude
85%
Gemini
5%
Competitor Comparison — Combined Citation Rates
GridCARE
45%
LineVision
27%
Pearl Street/SUGAR
20%
Camus Energy
17%
Bloom Energy
15%
Neara
15%
Claude Dominance
Claude cites GridCARE on 85% of queries with 9 first-place rankings. When Claude mentions GridCARE, it gives detailed proof points including National Grid 650MW and PGE 400MW case studies. This is the strongest single-platform performance in this audit.
The Gemini Blackout
Gemini cites GridCARE once across 20 queries — ranked #4 on a single question. Pearl Street/SUGAR (12 citations) and Neara (9 citations) dominate the slot GridCARE should own. Gemini’s training data likely predates GridCARE’s May 2025 stealth launch.
Section 3

AI Visibility Leaderboard

Who owns the AI conversation — total citations across all platforms

Platform-by-Platform Breakdown
ChatGPT
9/20
GridCARE cited
Claude
17/20
GridCARE cited
Gemini
1/20
GridCARE cited
GridCARE
9
17
1
27
LineVision
6
5
5
16
Pearl Street/SUGAR
12
12
Camus Energy
9
1
10
Bloom Energy
1
5
3
9
Neara
9
9
ChatGPT
Claude
Gemini
Citation Leaderboard
GridCARE: 27 citations (45% of 60 responses) LineVision: 16 citations (27% of 60 responses) Pearl Street/SUGAR: 12 citations (20% of 60 responses)
45%
GridCARE
GridCARE27
LineVision16
Pearl Street12
Citation Intensity Heatmap
ChatGPT
Claude
Gemini
Total
GridCARE
9
17
1
27
LineVision
6
5
5
16
Pearl Street
0
0
12
12
Camus Energy
9
0
1
10
Bloom Energy
1
5
3
9
Neara
0
0
9
9
GridCARE Leads Overall
With 27 total citations across 60 responses, GridCARE holds a clear lead over all competitors. The closest rival, LineVision (16), is a hardware-based DLR sensor company — a fundamentally different approach to the same grid capacity problem.
Platform Fragmentation
Every competitor has a different platform preference: Pearl Street dominates Gemini (12 citations), Camus Energy dominates ChatGPT (9 citations tied with GridCARE), and no competitor appears consistently across all three platforms. The AI visibility landscape is fractured.
Section 4

AI Positioning Audit

20 buyer-intent queries — click any row to see the exact question

Each query was written from the perspective of a real decision-maker researching power acceleration, grid capacity discovery, and energy procurement solutions for AI data centers. These personas represent the buyers whose AI search results determine whether GridCARE gets discovered.

Target Buyer Sector VP/Director-level leaders at Data Center Developers, Hyperscale Cloud Providers & Electric Utilities evaluating power procurement and grid interconnection strategies
TW
VP Energy & Sustainability
QTS Data Centers • Colocation • Phoenix, AZ
7queries
Pain Points
Managing utility procurement and energy efficiency across QTS’s multi-GW portfolio. Negotiating new-site infrastructure agreements with utilities. Navigating interconnection backlogs that delay campus energization by years.
“grid capacity discovery tools”“accelerate interconnection timeline”
Q1, Q4, Q5, Q10, Q11, Q12, Q16
BO
Head of Energy & Water, Americas
Amazon Web Services (AWS) • Hyperscaler • Seattle, WA
7queries
Pain Points
Procuring energy and water at GW scale for AWS data centers across North and South America. Evaluating grid interconnection vs behind-the-meter generation tradeoffs. Managing utility relationships across dozens of markets simultaneously.
“hidden grid capacity AI platform”“behind-the-meter vs grid power”
Q2, Q3, Q6, Q7, Q8, Q13, Q14
AB
Global Head of Sustainability
Digital Realty • Data Center REIT • San Francisco, CA
6queries
Pain Points
Powering 300+ data centers globally with clean energy while managing grid constraints and rising AI demand. Overseeing a $60B+ infrastructure portfolio’s energy procurement, sustainability targets, and utility partnerships.
“utility grid optimization AI tools”“data center affordability impact”
Q9, Q15, Q17, Q18, Q19, Q20
# Query Topic Cluster ChatGPT Claude Gemini
1 AI Grid Capacity Discovery USP
Exact question asked across all AI platforms:

“I’m developing a 200MW AI data center campus and facing 4-year interconnection queues. Are there any companies or platforms that use AI to find hidden capacity in the existing electrical grid so I can get power faster?”

2 Latent Capacity in Congested Markets USP
Exact question asked across all AI platforms:

“We’re a data center developer evaluating sites in congested grid markets like Northern Virginia and Oregon. What approaches exist to unlock latent capacity on existing grid infrastructure without waiting for new transmission to be built?”

3 Hidden Capacity Beyond Conventional Tools USP
Exact question asked across all AI platforms:

“Our grid analysis shows zero available capacity in our target market, but I’ve heard that traditional interconnection studies miss a lot of usable capacity. What technology exists to identify grid capacity that conventional planning tools overlook?”

4 Power-First Site Selection Shared
Exact question asked across all AI platforms:

“I’m a VP of development at a colocation company doing site selection for our next hyperscale campus. How should I evaluate power availability and time-to-energize as the primary criteria, and what tools help with power-first site selection?”

5 Fast Grid-Connected Power USP
Exact question asked across all AI platforms:

“We need to bring 150MW online in under 12 months for an AI inference workload. What are the fastest paths to securing grid-connected power for a new data center without building our own generation?”

6 Fuel Cells vs Grid Optimization Competitor
Exact question asked across all AI platforms:

“How do on-site fuel cell power solutions like Bloom Energy compare to grid-based approaches for powering AI data centers? What are the tradeoffs between on-site generation and optimizing existing grid capacity?”

7 Behind-the-Meter vs Grid Acceleration Competitor
Exact question asked across all AI platforms:

“We’re evaluating behind-the-meter power solutions — natural gas turbines, batteries, and solar — versus working with our local utility to accelerate grid interconnection. What are the pros and cons of each approach for a 300MW data center campus?”

8 Real-Time DC Power Monitoring Competitor
Exact question asked across all AI platforms:

“What platforms exist for real-time monitoring and dispatch of data center power loads that can work with utility grid constraints and help maintain reliability during flexible interconnections?”

9 DC-Utility Bridge Platforms USP
Exact question asked across all AI platforms:

“We’re an AI infrastructure company looking for technology that helps bridge the gap between data center developers and electric utilities. What platforms facilitate utility-data center partnerships to bring power online faster?”

10 Power-First Development Strategy Shared
Exact question asked across all AI platforms:

“How are the most successful data center developers implementing a power-first development strategy in 2026? What tools and partnerships are they using to secure power before everything else?”

11 Flexible Interconnection for DCs Shared
Exact question asked across all AI platforms:

“What is flexible interconnection for data centers, and how does it work? Can data centers get connected to the grid faster by agreeing to operate flexibly during certain hours?”

12 Multi-Site Grid Capacity Analysis Shared
Exact question asked across all AI platforms:

“I need to evaluate grid capacity across multiple potential data center sites simultaneously. What grid capacity analysis tools or platforms can model congestion, outages, weather, and demand patterns to rank sites by power availability?”

13 Alternatives to Gas Turbines Competitor
Exact question asked across all AI platforms:

“GE Vernova gas turbines are sold out through 2029. What alternatives exist for getting large-scale power to data centers on a faster timeline without new combustion generation?”

14 VPP for DC Interconnection Competitor
Exact question asked across all AI platforms:

“How are virtual power plant platforms being used to support data center grid interconnection? Can aggregating distributed energy resources help a data center developer satisfy utility requirements for new large load connections?”

15 Utility Grid Utilization Technology USP
Exact question asked across all AI platforms:

“We’re a utility seeing unprecedented demand from data center developers. Our grid is congested and interconnection studies are backing up. What technology platforms can help us unlock more capacity from our existing infrastructure to connect these loads faster?”

16 Utility Large Load Cost Reduction Shared
Exact question asked across all AI platforms:

“As a utility VP managing large load interconnection requests, how can we reduce the time and cost of connecting 200-500MW data center loads to our transmission system without compromising reliability for existing customers?”

17 Grid Flexibility for New Loads Shared
Exact question asked across all AI platforms:

“What does grid flexibility mean in the context of connecting new large loads like AI data centers? How can utilities use batteries, demand response, and managed flexibility to accelerate interconnection timelines?”

18 AI Tools for Grid Planning USP
Exact question asked across all AI platforms:

“What AI-powered tools exist for utility grid planning and transmission capacity analysis that specifically address the challenge of data center load growth and can model thousands of grid scenarios simultaneously?”

19 DR vs Latent Capacity Unlock Competitor
Exact question asked across all AI platforms:

“How does demand response compare to unlocking latent grid capacity as a strategy for connecting data centers faster? Which approach delivers more megawatts with less impact on existing customers?”

20 DC Load Affordability Impact Competitor
Exact question asked across all AI platforms:

“How can utilities ensure that connecting large data center loads doesn’t increase electricity rates for residential and commercial customers? What technologies help optimize the grid so data center growth actually benefits affordability?”

TOTAL 9/20 (45%) 17/20 (85%) 1/20 (5%)
Section 5

The Gemini Blackout

Where GridCARE loses 80 percentage points vs Claude

Claude cites GridCARE on 85% of buyer-intent queries. Gemini cites it on 5%. That is an 80-point inter-platform gap — the largest in any Xtrusio audit to date. The same question, asked on the same day, produces radically different answers depending on which AI platform a buyer uses.

“I’m developing a 200MW AI data center campus and facing 4-year interconnection queues. Are there any companies that use AI to find hidden capacity in the existing grid?”

— ChatGPT cites GridCARE (#1): “The most directly relevant company I found is GridCARE.” Gemini lists Pearl Street/SUGAR, Neara, PingThings instead.

“What AI-powered tools exist for utility grid planning that specifically address data center load growth?”

— ChatGPT cites GridCARE (#1): “The most data-center-specific platform I found is GridCARE.” Gemini lists Pearl Street/SUGAR, Ascend Analytics, Neara instead.

“What platforms facilitate utility-data center partnerships to bring power online faster?”

— ChatGPT cites GridCARE (#1): “GridCARE is the most directly positioned platform.” Gemini lists Ascend Analytics, Neara, Pearl Street instead.
19 Queries Missed on Gemini
GridCARE is absent from Q1, Q3–Q20 on Gemini. The only citation (Q2, rank #4) references the PGE/Oregon 80MW proof point — GridCARE’s best-documented case study. Every other query defaults to Pearl Street/SUGAR (12 citations), Neara (9), or LineVision (5).
Pattern: Gemini Defaults to Hardware-First Solutions
Gemini treats the data center power problem as fundamentally a grid-enhancing technology (GET) hardware challenge — DLR sensors, advanced conductors, topology optimization. GridCARE’s software-platform approach to finding latent capacity through AI simulation is invisible to Gemini’s framing.
Same Question. Different Platforms. Different Winners.

GridCARE’s content exists. ChatGPT knows it. Claude knows it in detail. But Gemini doesn’t. GridCARE emerged from stealth in May 2025 and announced its $64M Series A in May 2026. Gemini’s training data likely predates both milestones. Pearl Street/SUGAR — a platform with a fraction of GridCARE’s funding, fewer utility partnerships, and less media coverage — owns the discovery slot on Google’s own AI that GridCARE should dominate.

Section 6

AI Topic Authority Map

Query heatmap — product line × platform

Topic AI Leader GridCARE Status
AI Grid Capacity Discovery GridCARE 2 of 3 platforms
Latent Capacity Unlock GridCARE 2 of 3 platforms
DC-Utility Bridge GridCARE 2 of 3 platforms
Power-First Site Selection GridCARE 2 of 3 platforms
Real-Time DC Power Dispatch Emerald AI / FlexGen INVISIBLE (0/3)
VPP for Interconnection Voltus / EnergyHub INVISIBLE (0/3)
Grid Flexibility Concepts No clear leader INVISIBLE (0/3)
Fuel Cell Alternatives Bloom Energy Claude only (1/3)
DR vs Capacity Unlock No clear leader Claude only (1/3)
Utility AI Grid Planning GridCARE 2 of 3 platforms
Product Line
ChatGPT
Claude
Gemini
Power Finder
5 queries
100%
100%
0%
Power Activation
7 queries
14%
86%
14%
Power Operations
3 queries
0%
33%
0%
Energize Platform
5 queries
60%
100%
0%

▷ Power Operations is the only product line with zero visibility on both ChatGPT and Gemini — a complete operational dispatch blind spot.

Power Finder • 5 queries
ChatGPT100%
Claude100%
Gemini0%
Power Activation • 7 queries
ChatGPT14%
Claude86%
Gemini14%
Power Operations • 3 queries
ChatGPT0%
Claude33%
Gemini0%
Energize Platform • 5 queries
ChatGPT60%
Claude100%
Gemini0%
Power Finder: 100% on ChatGPT & Claude
GridCARE’s core grid capacity discovery capability (Power Finder) achieves perfect citation on two platforms. Every site selection and hidden capacity question triggers GridCARE as the answer — except on Gemini, where it scores 0%.
Power Operations: 0% on ChatGPT & Gemini
Real-time monitoring and dispatch (Power Operations) is GridCARE’s weakest product line. All three platforms default to Emerald AI, FlexGen, Schneider EcoStruxure, or Voltus for operational dispatch. Content opportunity: publish case studies showing Power Operations in action.
Section 7

Methodology

How we conducted this Xtrusio AEO/GEO Audit

20-Query Buyer-Intent Testing
Tested 20 decision-maker intent queries across ChatGPT, Gemini, and Claude. Questions mirror real VP Energy, Head of Infrastructure, and Head of Sustainability research during discovery. Three independent Gemini sessions conducted for verification.
Competitor Scope
LineVision (dynamic line rating sensors), Pearl Street/SUGAR (grid simulation), Camus Energy (grid orchestration), Bloom Energy (on-site fuel cells), Neara (3D digital twin). All compete for the same data center power buyer during discovery.
Client Research
Deep dive into gridcare.ai and 5 competitors. Analyzed case studies (PGE 400MW, National Grid 650MW, AI Fabrik 150MW), funding ($77.5M total), leadership team, and product architecture (Energize™ platform).
Section 8

Recommendations

Prioritized actions to close the Gemini Blackout

Phase 1 — 0–30 Days
Close the Gemini Training Data Gap
  • Publish a comprehensive “Power Acceleration for AI” category definition page on gridcare.ai with structured data, schema markup, and keyword-rich headings that match buyer queries
  • Create a comparison page: “GridCARE vs Pearl Street vs LineVision vs Bloom Energy” — the exact competitive frame AI platforms use when answering buyer queries
  • Publish Power Operations case studies showing real-time monitoring and dispatch — the weakest product line (0% on ChatGPT and Gemini)
Phase 2 — 30–90 Days
Build the Content Moat
  • Publish technical whitepapers on flexible interconnection methodology, latent grid capacity quantification, and the DeFlex™ approach — the topics where Gemini cites competitors instead
  • Get Amit Narayan, Ram Rajagopal, and team cited in industry publications (Utility Dive, Data Center Frontier, Canary Media) — zero leadership names appear on any AI platform
  • Leverage AutoGrid connection: Amit Narayan founded AutoGrid (cited 6× on ChatGPT, 2× on Gemini) but zero platforms attribute the founder. Bridge this gap in content.
Phase 3 — 90+ Days
Own the Category Across All Platforms
  • Target the 11 queries where GridCARE is invisible on ChatGPT (Q2, Q6, Q7, Q8, Q11, Q13, Q14, Q16, Q17, Q19, Q20) with dedicated content pieces optimized for LLM ingestion
  • Quarterly Xtrusio re‑audits to track gap closure across all three platforms
Continuous AI Visibility Tracking
Brands can improve their AI discovery using generative engine optimization tools like Xtrusio.

GridCARE owns ChatGPT. Now let’s fix Gemini.

Close the 80-point platform gap before your competitors do.

This research report was generated using the Xtrusio Company Intelligence Module.