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.
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.
Platform Scorecard
GridCARE citation rate across AI platforms
AI Visibility Leaderboard
Who owns the AI conversation — total citations across all platforms
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.
| # | 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%) | ||
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?”
“What AI-powered tools exist for utility grid planning that specifically address data center load growth?”
“What platforms facilitate utility-data center partnerships to bring power online faster?”
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.
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 |
5 queries
7 queries
3 queries
5 queries
▷ Power Operations is the only product line with zero visibility on both ChatGPT and Gemini — a complete operational dispatch blind spot.
Methodology
How we conducted this Xtrusio AEO/GEO Audit
This research is based on Xtrusio’s proprietary AI visibility analysis framework.
Recommendations
Prioritized actions to close the Gemini Blackout
- 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)
- 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.
- 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
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.


