Sunrun AI Visibility Market Strategy Report - Solar Energy

Answer Capsule

Sunrun leads the Solar Energy AI recommendation index in October 2026 with 79.7% valid recommendation coverage, an 11.2-point lead over second-place Palmetto Solar. The benchmark shows Sunrun converting near-saturated mention presence (92.5%) into top-three placement (73.6%) and first-place recommendations (45.8%) at a rate no other tracked brand approaches. The clearest win is rank-one dominance across every placement tier; the clearest weakness is a 2.9-point baseline gain that stays within normal variation while Palmetto Solar posted the only significant baseline rise in the cohort. The clearest opportunity is converting Sunrun's remaining neutral mentions and non-recommendation appearances into shortlist positions, particularly on ChatGPT and Perplexity, where rank-one rates trail the brand's Google AI surface performance.

Who This Report Is For

This report is written for Sunrun's marketing, communications, and category strategy teams, and for solar energy executives evaluating how AI systems position residential solar brands at the recommendation stage.

Report Card

Executive Summary

Questions This Section Answers

• How large is Sunrun's lead over Palmetto Solar and the rest of the solar cohort?
• Which platform and cluster drive Sunrun's recommendation capture?
• Where does Sunrun's presence convert poorly into first-place recommendations?

Sunrun holds dominant recommendation power in the Solar Energy category. The October 2026 benchmark recorded 79.7% valid recommendation coverage for Sunrun across 428 qualified observations, an 11.2-point lead over Palmetto Solar at 68.5% and a 31.1-point lead over third-place Blue Raven Solar at 48.6%. Sunrun is the only tracked brand with a rank-one rate above 45%, and it leads every placement tier in the current month.

The brand's mention presence is near-saturated at 92.5%, meaning Sunrun appears in nearly every qualified AI response about solar companies. Of the 396 observations where Sunrun was mentioned, 355 carried positive framing, 39 were neutral, and only 2 were negative. The net sentiment score of 0.8914 reflects a framing profile that is overwhelmingly positive with almost no cautionary or negative characterizations.

Sunrun's strongest cluster is C01, Best Solar Companies & Installers, the only cluster with sufficient qualified observations in the public benchmark. Within this cluster, Sunrun holds a 73.6% top-three rate and a 45.8% rank-one rate, producing 315 top-three placements and 196 rank-one results. The brand's average recommended rank of 1.7574 is the strongest in the cohort.

The strongest platform signal for Sunrun is Google AI Overviews, where the brand holds 85.95% valid recommendation coverage, a 57.85% rank-one rate, and a 0.9402 net sentiment score across 121 observations. Copilot also shows strong recommendation conversion at 91.18% coverage, though on a smaller observation base of 68.

The clearest platform gap is Perplexity, where Sunrun's rank-one rate drops to 24.56% despite a 75.44% valid recommendation coverage. ChatGPT shows a similar pattern: 66.67% coverage but only a 36.67% rank-one rate. These two platforms represent the largest gap between Sunrun's presence and its first-place recommendation conversion.

The benchmark classifies Sunrun's 2.9-point gain from the July 2026 baseline of 76.8% as within normal month-to-month variation. However, the brand's move from September 2026 (71.1%) to October 2026 (79.7%) was an 8.6-point gain that stands beyond normal variation, indicating a meaningful recent acceleration in recommendation capture.

What Sunrun Is Winning

Questions This Section Answers

• Which placement metrics put Sunrun ahead of other solar brands?
• How does Sunrun's sentiment and negative visibility compare with the rest of the cohort?
• Which AI platform shows Sunrun's strongest recommendation behavior?

Sunrun holds the strongest placement profile in the category by a wide margin. Its 73.6% top-three rate is 19.9 points above Palmetto Solar's 53.7% and 41.1 points above Blue Raven Solar's 32.5%. Its 45.8% rank-one rate is 32.7 points above Palmetto Solar's 13.1% and 42.1 points above Blue Raven Solar's 3.7%. No other tracked brand converts shortlist appearances into first-place recommendations at anywhere near Sunrun's rate.

The brand's average recommended rank of 1.7574 is the best in the cohort. When Sunrun receives a rank-eligible recommendation, it appears at position 1.76 on average, meaning it is typically the first or second option AI systems surface. Palmetto Solar's average rank is 2.6267, and Blue Raven Solar's is 2.9126.

Sunrun's sentiment profile is exceptionally clean. With 355 positive mentions, 39 neutral mentions, and only 2 negative mentions across 428 observations, the brand carries a net sentiment score of 0.8914. The negative visibility rate is 0.47%, the lowest among all tracked brands with meaningful presence. This indicates that AI systems rarely frame Sunrun in cautionary or negative terms.

Google AI Overviews is Sunrun's strongest platform by recommendation behavior. Across 121 observations on this surface, Sunrun holds 85.95% valid recommendation coverage, a 57.85% rank-one rate, and 104 valid recommendations. The brand's average recommended rank on AI Overviews is 1.5243, the strongest platform-level average in the dataset.

Sunrun also holds a dominant position on Copilot, where it achieves 91.18% valid recommendation coverage and a 42.65% rank-one rate across 68 observations. The brand's mention presence on Copilot is 100%, meaning it appears in every qualified Copilot response about solar companies.

Where Sunrun Has the Clearest AI Visibility Gaps

Questions This Section Answers

• On which platforms does Sunrun's rank-one conversion trail its recommendation coverage?
• What do Sunrun's neutral mentions represent for coverage and sentiment?
• How does Palmetto Solar's baseline trajectory compare with Sunrun's movement?

Sunrun's most significant gap is not in presence or coverage but in rank-one conversion on specific platforms. On Perplexity, Sunrun holds 75.44% valid recommendation coverage but only a 24.56% rank-one rate. This means that while Sunrun appears in three-quarters of Perplexity's recommendation shortlists, it is the first recommendation in fewer than one in four. Palmetto Solar holds a 17.54% rank-one rate on Perplexity, and SunPower holds a 33.33% rank-one rate on the same platform, indicating that Perplexity distributes first-place recommendations more evenly than Google AI Overviews or Copilot.

ChatGPT shows a similar pattern. Sunrun holds 66.67% valid recommendation coverage on ChatGPT but only a 36.67% rank-one rate. The brand's average recommended rank on ChatGPT is 2.15, compared to 1.52 on Google AI Overviews. This suggests that ChatGPT is more likely to place Sunrun in the second or third position rather than first.

The benchmark data also shows that Sunrun's neutral mention count of 39 represents 9.11% of qualified observations. These are instances where Sunrun appears in an AI response but is not framed positively or negatively. Converting these neutral appearances into positive recommendations represents a clear opportunity to increase both coverage and sentiment scores.

Sunrun's baseline movement from July 2026 to October 2026 was 2.9 points, which the benchmark classifies as within normal variation. Palmetto Solar, by contrast, posted a 10.5-point baseline gain that stands beyond normal variation. While Sunrun remains far ahead, Palmetto Solar's trajectory suggests the gap could narrow if Sunrun's recommendation capture plateaus while Palmetto Solar continues to gain.

The benchmark also recorded three high-severity factual inconsistencies involving Sunrun's installed capacity across ChatGPT, Copilot, and Google AI Overviews. These inconsistencies are detailed in the following section and represent a citation-layer issue that may affect how AI systems characterize Sunrun's scale and market position.

Biggest Opportunity

Sunrun's biggest opportunity is converting its neutral and non-recommendation appearances into first-place recommendations on ChatGPT and Perplexity. The brand already appears in 92.5% of qualified observations and holds 79.7% valid recommendation coverage. The gap is not reach but conversion: on ChatGPT, Sunrun's rank-one rate is 36.67% against a 66.67% coverage rate, and on Perplexity, its rank-one rate is 24.56% against a 75.44% coverage rate.

Closing this gap requires understanding which prompts on these platforms produce second-place or third-place placements rather than first-place recommendations, and which cited sources those responses draw from. The benchmark shows that Perplexity distributes rank-one recommendations more evenly across brands than Google AI Overviews does, which suggests that Perplexity's source layer may weight different evidence types. Identifying and strengthening Sunrun's presence in those source types is the clearest path from reference to recommendation on this platform.

Competitive Landscape

Questions This Section Answers

• How does Sunrun's top-three and rank-one rate compare with Palmetto Solar and Blue Raven Solar?
• Which solar brands trail Sunrun by the widest margin at the rank-one tier?

Sunrun holds the strongest recommendation-stage position in the Solar Energy category, with Palmetto Solar as the clearest challenger and a steep drop-off to the mid-tier brands. The table below shows the full tracked competitor set ranked by top-three rate.

Average recommended rank covers rank-eligible recommendations only.

Sunrun's 73.60% top-three rate is 19.86 points above Palmetto Solar and 41.12 points above Blue Raven Solar. Its 45.79% rank-one rate is more than three times Palmetto Solar's 13.08% and more than twelve times Blue Raven Solar's 3.74%. The gap between Sunrun and the rest of the cohort is largest at the rank-one tier, where Sunrun's conversion rate is unmatched.

AI Response Inconsistency Alerts

Questions This Section Answers

• What installed capacity figures are AI systems citing for Sunrun, and where do they conflict?
• Which platforms and source layers are producing the conflicting Sunrun capacity figures?

The benchmark detected three high-severity factual inconsistencies involving Sunrun across three AI platforms: ChatGPT, Copilot, and Google AI Overviews. All three conflicts concern Sunrun's installed residential solar capacity, and all three involve conflicting capacity figures that cannot simultaneously be accurate.

The first conflict involves ChatGPT and Copilot disagreeing on Sunrun's networked residential solar capacity. When asked "What are the top three solar companies in the US?", ChatGPT stated that Sunrun has 8.7 GW of networked residential solar capacity as of June 30, 2026, citing Wood Mackenzie, Sunrun's investor relations filings, and SolarMetric. Copilot, responding to the same question, stated that Sunrun has 2.8 GW of capacity, citing PanelCensus, Solar Savings Hub, and Solar Pro Guide. The two figures differ by 5.9 GW and cannot both be correct at the same point in time.

The second conflict involves Google AI Overviews and Copilot disagreeing on Sunrun's total installed capacity. When asked "Who is the largest residential solar company?", Google AI Overviews stated that Sunrun has over 7.5 GW installed, citing Global Gurus and Solar Power World Online. Copilot, responding to the same question, stated that Sunrun has 2.8 GW of capacity across 388,000 installations, citing PanelCensus, SolarMetric, and The Roofing Brief. The two figures differ by at least 4.7 GW.

The third conflict again involves ChatGPT and Copilot disagreeing on installed capacity. When asked "Who is the largest residential solar company?", ChatGPT stated that Sunrun has 8.4 GW of networked solar capacity at the end of 2025, citing Wood Mackenzie, Sunrun's investor relations filings, and MarketScreener. Copilot, responding to the same question, stated that Sunrun has 2.8 GW of capacity across 388,000 installations, citing PanelCensus, SolarMetric, and The Roofing Brief. The flagged source excerpt from Wood Mackenzie states that "The US distributed solar industry installed 8.4 GWdc of capacity in 2025," which appears to be an industry-wide figure rather than a Sunrun-specific figure, suggesting a possible source attribution error in ChatGPT's response.

These inconsistencies indicate that AI systems are drawing from conflicting source layers when characterizing Sunrun's scale. The 2.8 GW figure cited by Copilot appears across multiple third-party sources, while the 8.4 GW and 8.7 GW figures cited by ChatGPT and Google AI Overviews appear to draw from industry reports and Sunrun's own investor materials. The presence of conflicting capacity figures across platforms may affect how AI systems position Sunrun relative to competitors in recommendation contexts.

Prompt Evidence

ChatGPT / Best Solar Companies & Installers Prompt: "What are the top three solar companies in the US?" Result: Sunrun was recommended, but ChatGPT cited 8.7 GW of installed capacity while Copilot cited 2.8 GW for the same question, indicating a source-layer conflict on Sunrun's scale.

Perplexity / Best Solar Companies & Installers Prompt: "Who is the best solar company to install solar panels?" Result: Sunrun appeared in the recommendation shortlist with 75.44% valid recommendation coverage on Perplexity, but its rank-one rate on this platform is only 24.56%, meaning it is frequently listed but not placed first.

Google AI Overviews / Best Solar Companies & Installers Prompt: "Who is the largest residential solar company?" Result: Sunrun was recommended with over 7.5 GW installed, but Copilot cited 2.8 GW for the same question, creating a factual inconsistency across platforms.

Copilot / Best Solar Companies & Installers Prompt: "What are the top three solar companies in the US?" Result: Sunrun appeared in 100% of qualified Copilot responses and achieved 91.18% valid recommendation coverage, with a 42.65% rank-one rate.

What CiteWorks Studio Would Do Next

Phase 1: AI Visibility Market Discovery Audit Map Sunrun's prompt-level wins and losses across all six AI platforms, with particular focus on the ChatGPT and Perplexity rank-one conversion gap and the capacity inconsistency source layer.

Phase 2: Recommendation Readiness Plan Identify which prompt categories and buyer-intent clusters produce second-place or third-place placements rather than first-place recommendations, and prioritize the highest-impact correction targets.

Phase 3: Owned Answer Layer Buildout Strengthen Sunrun's owned content to provide clear, consistent, and citable answers about installed capacity, market position, and service footprint, reducing the likelihood that AI systems draw conflicting figures from third-party sources.

Phase 4: Citation / Authority Layer Development Engage with the third-party source layer, including review sites, comparison platforms, and industry publications, to ensure that Sunrun's capacity and market position are consistently represented across the sources AI systems cite most frequently.

Phase 5: Monthly AI Visibility and Recommendation Tracking Track Sunrun's recommendation coverage, rank-one rate, and sentiment score month over month across all six platforms, with alerts for any platform-level rank-one decline or new factual inconsistency.

Why This Matters

Questions This Section Answers

• Why does Sunrun's presence rate overstate its position in buyer shortlists?
• What distinguishes Sunrun's strongest platform for recommendation conversion from its weakest?

AI presence alone is not enough. Sunrun appears in 92.5% of qualified AI responses about solar companies, but its rank-one rate is 45.8%, meaning that in more than half of its appearances, another brand is recommended first. The gap between presence and first-place recommendation is where buyer shortlists are formed, and it is the metric that most directly affects which brands buyers consider.

The next move is targeted correction of the prompt, page, and citation layers. Sunrun's strongest platform, Google AI Overviews, shows what strong recommendation conversion looks like: 85.95% coverage and a 57.85% rank-one rate. Its weakest platform for rank-one conversion, Perplexity, shows where the gap remains: 75.44% coverage but only a 24.56% rank-one rate. Closing that gap requires understanding which sources Perplexity weights most heavily and ensuring Sunrun's evidence layer is strong in those sources.

Core Metrics

Sentiment Score

Sentiment Score = (positive mentions × 1 + neutral mentions × 0 + negative mentions × -1) / total mentions

Sunrun's sentiment score is 0.8914, calculated from 355 positive mentions, 39 neutral mentions, and 2 negative mentions across 396 total mentions. This score indicates that AI systems frame Sunrun positively in the overwhelming majority of responses where the brand appears.

This matters because unclassified mention counts are misleading. A brand that appears in 396 responses but is framed negatively in half of them is not in the same position as a brand that appears in 396 responses with overwhelmingly positive framing. Share of voice is a diagnostic metric, not a business KPI. A positive recommendation, a neutral reference, a cautionary mention, and a competitor-displaced mention are not equal. Counting all mentions as wins is bad measurement. Classified sentiment is required before interpreting AI visibility.

Sunrun's 39 neutral mentions represent 9.11% of qualified observations. These are instances where Sunrun appears in an AI response but is not framed positively or negatively. Converting these neutral appearances into positive recommendations represents a clear opportunity to increase both coverage and sentiment scores.

Sentiment by Platform

Methodology

1. This report is a benchmark-based analysis of Sunrun's AI recommendation visibility in the Solar Energy category for October 2026. It is not a client implementation case study and does not imply that CiteWorks Studio caused any benchmark outcome.
2. The reporting window is October 2026, with baseline comparisons to July 2026, August 2026, and September 2026 where the benchmark provides those data points.
3. Six AI platform families were tracked: ChatGPT, Copilot, Gemini, Perplexity, Google AI Overviews, and Google AI Mode. All six contributed qualified observations in October 2026.
4. The October 2026 benchmark produced 428 qualified observations from 800 prompt-surface observations collected. The July 2026 baseline produced 488 qualified observations from the same collection volume.
5. The competitor universe consists of 10 tracked brands: Sunrun, Palmetto Solar, Blue Raven Solar, Momentum Solar, Tesla Wall Connector (Tesla, Inc.), SunPower, Trinity Solar, Freedom Forever, Elevation, and Sunnova.
6. The public benchmark includes one qualified high-intent cluster: C01, Best Solar Companies & Installers. Clusters C02 (Solar Company & Product Comparisons) and C03 (Solar Pricing, Costs & Financing) had no qualified observations in the current measurement period.
7. Stage 0 extraction retains the full detail of each AI response, including the query, AI surface, answer, brand outcome, recommendation placement, sentiment, and, where exposed, citations or attributable evidence sources.
8. A mention is counted when a tracked brand appears in an AI response to a qualified prompt. A valid recommendation is counted when a brand appears in a valid recommendation shortlist, as marked by the dataset. Mentions and recommendations are separate metrics and are not interchangeable.
9. Top-three rate measures the share of qualified observations where a brand appears among the top three recommended options. Rank-one rate measures the share of qualified observations where a brand is the first recommended option. Average recommended rank covers rank-eligible recommendations only.
10. The Tesla Wall Connector (Tesla, Inc.) and Tesla Solar tracking variants require separate handling due to a brand-label change that took effect in August 2026. The July and October figures for Tesla Wall Connector (Tesla, Inc.) and the August figure for Tesla Solar should be read with this labeling change in mind.
11. All coverage percentages in this report use the qualified benchmark set as the denominator: 488 observations in July 2026 and 428 in October 2026. The raw collection is larger and includes prompts that did not survive qualification.
12. The movements described in this report identify changes worth investigating. They do not by themselves establish what caused those changes, and no causal relationship should be inferred from the direction or size of a move alone.

See How AI Is Recommending Your Brand

Sunrun holds the strongest recommendation position in the Solar Energy category, but the gap between presence and first-place conversion on platforms like ChatGPT and Perplexity shows where the next gains are available. A company-level AI visibility audit maps the prompt, surface, competitor, ranking, sentiment, and evidence-source patterns behind these benchmark outcomes into a prioritized strategy. The benchmark shows the outcome; the audit explains the mechanics.