NEAR Foundation AI Visibility Market Strategy Report - Blockchain Platforms

Mark HuntleyBy Mark HuntleyFounder and CEO
12 minutes read

Key Takeaways

  • NEAR Foundation’s raw mention presence rose to 13.8%, but valid recommendation coverage fell to 1.1%.
  • The brand recorded no top-three or rank-one placements, so mentions are not converting into shortlist positions.
  • Google AI Overviews produced NEAR Foundation’s only valid recommendation; ChatGPT, Copilot, Gemini, and Perplexity did not.
  • Sentiment was positive overall, which suggests the main issue is placement quality rather than framing.

Answer Capsule

NEAR Foundation holds 1.1% valid recommendation coverage in the October 2026 LLM Authority Index Blockchain Platforms benchmark, the lowest of seven tracked brands, while its raw mention presence rate reached 13.8%. The benchmark shows a widening gap between NEAR Foundation's visibility and its recommendation conversion: presence roughly doubled from July 2026 to October 2026 while valid recommendation coverage declined for a third consecutive month. The clearest weakness is placement: NEAR Foundation recorded 0.0% top-three rate and 0.0% rank-one rate across the full series. The clearest opportunity is converting its single remaining valid recommendation into a durable, repeatable shortlist position inside the Best Layer 1 Blockchain Platforms cluster.

Who This Report Is For

This report is written for NEAR Foundation's marketing, developer relations, and communications leadership, and for category analysts tracking how AI systems recommend Layer 1 blockchain platforms at the consideration stage.

Report Card

Field

Value

Report type

AI Visibility Company Market Strategy Report

Target company

NEAR Foundation

Category / market studied

Blockchain Platforms

Reporting month

October 2026

AI platforms tracked

6 (ChatGPT, Copilot, Gemini, Perplexity, Google AI Overviews, Google AI Mode)

Public high-intent clusters

3

AI observations analyzed

87 qualified observations from 800 prompt-surface observations

Competitors tracked

6

Executive Summary

NEAR Foundation is visible but under-recommended. The October 2026 benchmark records a 13.8% raw mention presence rate against a 1.1% valid recommendation coverage rate, a gap of 12.7 points between appearing in an AI answer and being placed inside a recommendation shortlist. That gap is the widest relative separation between presence and recommendation of any tracked brand in the category.

The company's presence in the collection has grown. Raw mention presence rose from 6.5% in July 2026 to 13.8% in October 2026, a 7.3-point increase. Over the same four-month window, valid recommendation coverage fell from 2.8% to 2.0% to 1.8% to 1.1%, a three-month decline the benchmark flags as a watch item. The valid recommendation count is 1 in October 2026, down from 3 in July 2026.

Mention framing is not the problem. NEAR Foundation recorded 5 positive mentions, 7 neutral mentions, and 0 negative mentions in October 2026, producing a net sentiment score of 0.4167, the second-strongest in the benchmark behind Solana Foundation. The company is being described favorably. It is not being shortlisted.

The strongest cluster is C01, Best Layer 1 Blockchain Platforms, the only cluster with sufficient coverage in the October 2026 series. All 87 qualified observations fell into that cluster. NEAR Foundation's single valid recommendation sits at an average recommended rank of 10, the deepest placement in the category, at the edge of the top-ten window.

The strongest platform signal is Google AI Overviews, where NEAR Foundation recorded 1 valid recommendation from 50 observations, a 2.0% valid recommendation coverage rate, and a net sentiment score of 0.5. Gemini produced the next-strongest visibility signal at 25.0% raw mention presence with no valid recommendations. ChatGPT, Copilot, and Perplexity produced no valid recommendations for NEAR Foundation in October 2026.

The clearest gap is placement quality. NEAR Foundation recorded 0.0% top-three rate and 0.0% rank-one rate in both July 2026 and October 2026. Solana Foundation recorded a 14.9% top-three rate and Ethereum Foundation recorded a 4.6% rank-one rate over the same period. NEAR Foundation's presence is not converting into either a recommendation slot or a first-choice placement anywhere in the benchmark.

The benchmark treats this reading as directional rather than settled. On a base of one valid recommendation, a single placement change would move the coverage rate materially. The pattern is consistent across three consecutive months, which is why it warrants attention, but the sample size limits how firmly the trend can be stated.

What NEAR Foundation Is Winning

Questions This Section Answers

  • Why is NEAR Foundation's sentiment score the second-highest in the Blockchain Platforms benchmark?
  • Which platform produced NEAR Foundation's only valid recommendation, and how large is that signal?
  • How much has NEAR Foundation's raw mention presence grown since July 2026?

NEAR Foundation's clearest win is framing quality. Its net sentiment score of 0.4167 in October 2026 is the second-highest in the benchmark, behind only Solana Foundation at 0.5179 and ahead of Avalanche at 0.3788, Ethereum Foundation at 0.3265, BNB Chain at 0.3194, TRON DAO at 0.3, and Polygon Labs at 0.2. The company recorded zero negative mentions across the qualified observation set.

The second win is presence growth. Raw mention presence rose 7.3 points from July 2026 to October 2026, from 6.5% to 13.8%. That is a meaningful increase in how often AI systems surface the brand at all, and it runs counter to the coverage decline.

The third win is a narrow but real recommendation pocket on Google AI Overviews. NEAR Foundation's single valid recommendation in October 2026 came from that platform, where it recorded a 2.0% valid recommendation coverage rate and a 0.5 net sentiment score. That is a small signal, but it identifies a platform where the brand is being placed inside a shortlist rather than only mentioned.

Beyond those three signals, the evidence base is thin. NEAR Foundation holds no top-three placements, no rank-one placements, and no valid recommendations on ChatGPT, Copilot, Gemini, or Perplexity in October 2026. The wins are real but narrow.

Where NEAR Foundation Has the Clearest AI Visibility Gaps

Questions This Section Answers

  • How does NEAR Foundation's presence-to-recommendation conversion compare to other tracked Layer 1 platforms?
  • Why does NEAR Foundation's average recommended rank of 10 limit its shortlist impact?
  • Which competitors take the recommendation slot when NEAR Foundation is mentioned in a Best Layer 1 Blockchain Platforms answer?

The primary gap is recommendation conversion. NEAR Foundation appears in 13.8% of qualified observations but converts that presence into a valid recommendation in only 1.1% of them. By comparison, Solana Foundation appears in 64.4% of observations and converts at 16.1%, BNB Chain appears in 82.8% and converts at 16.1%, and Avalanche appears in 75.9% and converts at 16.1%. NEAR Foundation's conversion ratio is the weakest in the benchmark.

The second gap is placement. NEAR Foundation recorded 0.0% top-three rate and 0.0% rank-one rate in both July 2026 and October 2026. Solana Foundation's 14.9% top-three rate and Ethereum Foundation's 4.6% rank-one rate show what placement conversion looks like elsewhere in the category. NEAR Foundation's single valid recommendation sits at an average recommended rank of 10, at the outer edge of the top-ten window, which means even when the brand is recommended, it is recommended last.

The third gap is platform coverage. NEAR Foundation recorded no valid recommendations on ChatGPT, Copilot, Gemini, or Perplexity in October 2026. On Gemini it recorded a 25.0% raw mention presence rate with zero valid recommendations, meaning the platform surfaces the brand but does not place it in a shortlist. On Copilot it recorded a 37.5% raw mention presence rate with zero valid recommendations. Those are presence-without-recommendation patterns on two platforms where competitors are converting.

The fourth gap is competitive displacement. Solana Foundation is the cluster winner in C01 for six of the seven tracked brands' competitor indexes, including NEAR Foundation's. Solana Foundation holds a 14.9% top-three rate, a 2.5714 average recommended rank, and a 0.5179 net sentiment score. Where NEAR Foundation is mentioned alongside Solana Foundation, the recommendation slot is going to Solana Foundation.

The fifth gap is the widening distance to the category leaders. The gap between Avalanche and NEAR Foundation widened from 8.4 points in July 2026 to 15.0 points in October 2026. The three category leaders each gained between 3.0 and 4.9 points of coverage over the same window while NEAR Foundation lost 1.7 points.

Biggest Opportunity

Questions This Section Answers

  • Which platforms offer the best chance to convert NEAR Foundation's existing presence into a shortlist position?
  • Why is visibility not the binding constraint for NEAR Foundation in the Best Layer 1 Blockchain Platforms cluster?

The single biggest opportunity is converting NEAR Foundation's existing presence on Google AI Overviews and Gemini into shortlist placement inside the Best Layer 1 Blockchain Platforms cluster. NEAR Foundation already appears in AI answers on those platforms at meaningful rates, 8.0% on Google AI Overviews and 25.0% on Gemini, and it already holds one valid recommendation on Google AI Overviews. The gap is not visibility. The gap is that the brand is being described as context rather than placed as a recommendation.

The path runs through the consideration-stage prompt set that produced the qualified observations. Those prompts ask which Layer 1 blockchain platforms are best, which are most profitable for staking, and which belong in a top-ten chain list. NEAR Foundation is being mentioned in answers to those questions. The remediation work is to make the brand's public evidence layer specific enough, and its owned answer layer structured enough, that AI systems move NEAR Foundation from a mention inside the answer to a named entry inside the shortlist.

Competitive Landscape

Questions This Section Answers

  • Which Layer 1 blockchain platforms hold the strongest top-three and rank-one placement rates?
  • Why does NEAR Foundation rank last despite having the second-highest sentiment score?

Solana Foundation holds the strongest recommendation-stage position in the Blockchain Platforms category, with a 14.9% top-three rate and a 2.5714 average recommended rank. Avalanche, BNB Chain, and Solana Foundation are tied at the top of valid recommendation coverage at 16.1% each, and NEAR Foundation sits last in the tracked set at 1.1%.

Brand

Top-3 rate

Rank-1 rate

Avg recommended rank

Sentiment

Solana Foundation

14.94%

0.00%

2.5714

0.5179

BNB Chain

6.90%

0.00%

3.9286

0.3194

Ethereum Foundation

6.90%

4.60%

1.3333

0.3265

Avalanche

0.00%

0.00%

5.7857

0.3788

Polygon Labs

0.00%

0.00%

5.6667

0.2

TRON DAO

0.00%

0.00%

7.25

0.3

NEAR Foundation

0.00%

0.00%

10

0.4167

Average recommended rank covers rank-eligible recommendations only.

NEAR Foundation's row shows the weakest placement profile in the benchmark. It shares a 0.00% top-three rate and a 0.00% rank-one rate with Avalanche, Polygon Labs, and TRON DAO, but its average recommended rank of 10 is the deepest of that group, meaning its single recommendation lands at the outer edge of the top-ten window rather than in the middle of it. Its sentiment score of 0.4167 is the second-highest in the table, which shows that framing quality and placement quality are moving independently for this brand.

Prompt Evidence

Google AI Overviews / Best Layer 1 Blockchain Platforms Prompt: "What is the top 10 chain?" Result: NEAR Foundation appeared in the answer and received its single valid recommendation of the month, at an average recommended rank of 10.

Gemini / Best Layer 1 Blockchain Platforms Prompt: "What is the most profitable crypto staking?" Result: NEAR Foundation was mentioned in the response with positive framing but received no valid recommendation, a presence-without-placement pattern.

Copilot / Best Layer 1 Blockchain Platforms Prompt: "What does BNB mean?" Result: NEAR Foundation appeared as context in the answer with neutral framing and no recommendation placement.

ChatGPT / Best Layer 1 Blockchain Platforms Prompt: "crypto news" Result: NEAR Foundation did not appear in the response, and the platform produced no valid recommendations for the brand in October 2026.

What CiteWorks Studio Would Do Next

Phase 1: AI Visibility Market Discovery Audit Map every prompt where NEAR Foundation is mentioned but not recommended, and identify which competitor takes the shortlist slot in each answer.

Phase 2: Recommendation Readiness Plan Prioritize the consideration-stage prompts inside the Best Layer 1 Blockchain Platforms cluster where NEAR Foundation already has presence and can realistically convert to a shortlist position.

Phase 3: Owned Answer Layer Buildout Restructure NEAR Foundation's owned pages so that AI systems can extract a clear, specific, comparison-ready answer about what the platform is best for, rather than a general description.

Phase 4: Citation / Authority Layer Development Strengthen the third-party sources AI systems retrieve when forming Layer 1 recommendations, with emphasis on the source types that appear in the benchmark's citation layer.

Phase 5: Monthly AI Visibility and Recommendation Tracking Track NEAR Foundation's presence, valid recommendation coverage, top-three rate, rank-one rate, and sentiment month over month to confirm whether the conversion gap is closing.

Why This Matters

AI presence alone is not enough. NEAR Foundation's presence in the collection roughly doubled from July 2026 to October 2026 while its valid recommendation coverage fell for a third consecutive month. A buyer asking an AI system which Layer 1 blockchain platform to consider will see NEAR Foundation mentioned, but the recommendation slot is going to Solana Foundation, BNB Chain, Avalanche, or Ethereum Foundation. Being described favorably in an answer is not the same as being placed inside the shortlist that shapes the buyer's next step.

The next move is targeted correction of the prompt, page, and citation layers. The prompts where NEAR Foundation is mentioned but not recommended are identifiable. The pages that AI systems retrieve when forming those answers are identifiable. The sources that support competitor recommendations are identifiable. Closing a 12.7-point presence-to-recommendation gap requires working on all three layers at once, not on visibility alone.

Core Metrics

Metric

Value

Mentions

12

Valid recommendations

1

Top 3 recommendation count

0

Rank #1 recommendation count

0

Average recommended rank

10

Positive mentions

5

Neutral mentions

7

Negative mentions

0

Raw mention presence rate

13.79%

Valid recommendation coverage

1.15%

Top 3 recommendation rate

0.00%

Rank #1 recommendation rate

0.00%

Net sentiment score

0.4167

Strongest cluster by recommendation behavior

Best Layer 1 Blockchain Platforms (C01)

Strongest platform by recommendation behavior

Google AI Overviews

Sentiment Score

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

For NEAR Foundation in October 2026: (5 × 1 + 7 × 0 + 0 × -1) / 12 = 0.4167.

This matters because unclassified mention counts are misleading. A brand that appears in twelve AI answers sounds healthier than a brand that appears in twelve answers and is recommended in one of them. Share of voice is a diagnostic metric, not a business KPI. It tells you how often a brand is discussed. It does not tell you whether the discussion places the brand in front of a buyer as a candidate.

A positive recommendation, a neutral reference, a cautionary mention, and a competitor-displaced mention are not equal. NEAR Foundation's 12 mentions break down into 5 positive and 7 neutral, with no negative framing. That is a favorable distribution. But only 1 of those 12 mentions converted into a valid recommendation, and that recommendation landed at rank 10. Counting all 12 mentions as wins would overstate the brand's position in the buyer shortlist by a wide margin.

Classified sentiment is required before interpreting AI visibility. Without it, a brand cannot tell the difference between being recommended and being described. NEAR Foundation's sentiment score of 0.4167 is strong, and it should be read as a statement about framing quality, not about recommendation strength.

Sentiment by Platform

Platform

Mentions

Positive

Neutral

Negative

Sentiment Score

Readout

Google AI Overviews

4

2

2

0

0.5

Strongest public recommendation signal

Gemini

2

1

1

0

0.5

Present, but not recommendation-led

Copilot

3

1

2

0

0.3333

Present as context, not recommendation

Google AI Mode

3

1

2

0

0.3333

Present, but not recommendation-led

ChatGPT

0

0

0

0

N/A

No public presence in this packet

Perplexity

0

0

0

0

N/A

No public presence in this packet

Methodology

  1. This report is a benchmark-based analysis of NEAR Foundation's position in the LLM Authority Index Blockchain Platforms category for October 2026. It is not a client implementation result.
  2. The reporting window is October 2026, with comparisons to July 2026, August 2026, and September 2026 where the benchmark provides them.
  3. Six AI and search surface families were tracked: ChatGPT, Copilot, Gemini, Perplexity, Google AI Overviews, and Google AI Mode. All six were present in the qualified observation set.
  4. The October 2026 run began with 800 prompt-surface observations and 617 unique questions. Of those, 800 mentioned a tracked brand or competitor, 160 were relevant, 640 were irrelevant, and 87 qualified for the public benchmark denominator.
  5. Seven brands were tracked: NEAR Foundation, Avalanche, BNB Chain, Ethereum Foundation, Polygon Labs, Solana Foundation, and TRON DAO.
  6. Three public clusters were defined: Best Layer 1 Blockchain Platforms (C01, consideration stage), Layer 1 Blockchain Platform Comparisons (C02, evaluation stage), and Layer 1 Blockchain Platform Pricing and Costs (C03, decision stage). Only C01 had sufficient coverage in October 2026. C02 and C03 returned no qualified observations.
  7. Stage 0 extraction produced the prompt-level observations that feed the benchmark, retaining the query, AI or search 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 anywhere in an AI response to a qualified prompt, regardless of whether the brand is recommended.
  9. A valid recommendation is counted when a tracked brand appears inside a recommendation shortlist in a qualified response. Neutral, cautionary, comparison-anchor, and listed-only mentions are not counted as valid recommendations.
  10. Top-three rate and rank-one rate are calculated against the 87 qualified observations. Average recommended rank covers rank-eligible recommendations only.
  11. The qualified observation set contracted from 107 in July 2026 to 87 in October 2026. Brand-level percentages are computed within the smaller October denominator, which affects month-over-month comparisons.
  12. NEAR Foundation's single valid recommendation means its coverage rate is highly sensitive to individual placement changes. The three-month decline is treated as directional rather than settled. The benchmark does not measure market share, sales attribution, organic search rankings, social mention volume, or causality from metric movement alone.

See How AI Is Recommending Your Brand

The public benchmark shows where NEAR Foundation stands in AI recommendations across the Blockchain Platforms category. A company-level AI visibility audit maps the specific prompts where the brand is mentioned but not recommended, the competitors taking those shortlist slots, and the source pages shaping those answers into a prioritized plan of record.

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What Is AI Citation Intelligence?
AI citation intelligence is the process of measuring where AI platforms source their information and how frequently a brand is mentioned or referenced in AI-generated responses. Because LLMs synthesize across multiple sources, the sites and brands that appear repeatedly tend to influence how a topic or company is framed. This practice focuses on identifying which sources shape AI outputs and tracking brand visibility across different AI systems.
What Is Citation Architecture?
Citation architecture describes the set of sources that consistently inform how AI systems talk about a brand, product, or topic. LLMs draw from websites, articles, forums, and public discussion, and the sources they rely on most often become the backbone of their answers. Building strong citation architecture means ensuring that accurate, credible, high authority sources are the ones most likely to shape the way AI tools summarize and recommend a brand.
What Is Generative Engine Optimization?
Generative engine optimization (GEO) is the practice of improving the chances that AI systems use and cite your brand or content when generating answers. While traditional SEO is centered on ranking pages in search results, GEO focuses on how LLMs retrieve, interpret, and combine information when responding to a question. The objective is to strengthen the content and sources AI systems rely on, so your brand is treated as a trusted reference in AI responses.
What Is AI Share of Voice?
AI share of voice tracks how often a brand appears in AI-generated answers compared with competitors in the same category. It reflects visibility across AI platforms such as ChatGPT, Gemini, Claude, and Perplexity. Monitoring AI share of voice helps organizations see whether AI systems consistently include and recommend their brand for key queries or whether competitor brands are showing up more often.

About The Author

Mark Huntley

Mark Huntley

Founder and CEO

Mark Huntley, J.D. is founder of CiteWorks Studio, a strategic advisory focused on visibility, authority, and recommendation presence in AI-shaped search environments. His work centers on embedding-level GEO, vector optimization, and cosine gap engineering — helping brands align their digital presence with the retrieval systems that increasingly shape discovery, interpretation, and choice.

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