# AEO Health Check — Aurora Climate

> **SAMPLE REPORT — FICTIONAL COMPANY.** This is a demonstration AEO Health Check for "Aurora Climate," a fictional, de-identified biochar carbon-removal company. It exists to show what a real deliverable looks like. None of the data, scores, registry IDs, or competitor names describe a real client. The source HTML carries a "SAMPLE" watermark.

- **Brand:** AEO For Good — AEO Health Check
- **Client:** Aurora Climate (fictional)
- **AEO Health Score:** 62 / 100 — Maturity level: **Visible** (61–80 band)
- **Assessment Date:** April 19, 2026
- **Prepared by:** AEO For Good
- **Engines:** Claude (Anthropic)
- **Coverage:** 83 Questions across 4 stages
- © 2026 AEO For Good

---

## Contents

This report scores Aurora Climate across the four stages of the buyer funnel, then details what we found and how to improve at each stage. Competitive context, methodology, and scoring appendices follow.

1. Executive Summary
2. Discovery Stage — What We Found · How to Improve
3. Evaluation Stage — What We Found · How to Improve
4. Decision Stage — What We Found · How to Improve
5. Retention Stage — What We Found · How to Improve
6. Sentiment Alert
7. Competitive Landscape — Share of Voice · Mention Excerpts
8. How AI Search Engines Were Tested
9. Assessment Coverage
10. Appendix: Score Methodology — Scoring Formulas · Maturity and Dimension Scales

---

## 1. Executive Summary

### AEO Health Score: 62 / 100 — Visible

**Maturity Scale** (You are in the **Visible** band):

- Invisible — 0–20
- Limited — 21–40
- Developing — 41–60
- **Visible — 61–80 ← You**
- Authoritative — 81–100

### Health Score by Buyer Stage

| Stage | Score | Level |
|---|---|---|
| Discovery | 24 / 100 | Limited |
| Evaluation | 84 / 100 | Authoritative |
| Decision | 71 / 100 | Visible |
| Retention | 68 / 100 | Visible |

### Category Share of Voice

Who AI engines name in category-level (Discovery) answers, where the buyer did not name any brand.

**Aurora Climate earns 0% of category-level mentions.** The entire share below belongs to other entities competing for the same answer.

Share of voice by entity type:

| Entity Type | Share |
|---|---|
| Standards bodies | 56% |
| Validators & raters | 9% |
| Marketplaces | 8% |
| Adjacent competitors | 8% |
| Research & academic | 7% |
| Direct competitors | 7% |
| Adjacent category | 3% |
| Tools & software | 2% |

### Findings at a Glance

**What's Working**

- **Strong at Evaluation** — Aurora Climate appears in 93% of Evaluation questions and usually holds the first-recommendation spot.
- **Accurate Brand Knowledge** — AI correctly describes Aurora's core methodology, and Factual Accuracy is solid across all four stages.
- **Wins Head-to-Head** — Ranked favorably or neutrally against Ember Carbon, Terra Char, Kiln Carbon, and Black Earth Foundation.

**Urgent — Act Now**

- **Discovery Is Invisible** — All 23 category-level queries returned zero mentions. Buyers researching the category find competitors instead.
- **Certification Error ×13** — AI wrongly credits Premium Carbon Standard. Aurora's real registries are ACR and Verra VCS.
- **Weak at the Close** — When named at Decision and Retention, Aurora lands as a passing mention (Position 15 / 25), not the top pick.

**Opportunities**

- **Own "Durable Carbon Removal"** — No competitor owns this category in AI answers. A cornerstone page captures top-of-funnel buyers first.
- **CCP Label as Trust Asset** — A registry and CCP page makes Aurora the answer to the buyer's top trust question — and fixes the cert error.
- **Unlock FAQ Citations** — Adding FAQPage schema to /faqs/ turns existing Q&A content into AI-citable answers.

---

## 2. Discovery Stage

Questions buyers ask before they know your brand exists, including category-level questions that do not name a specific brand.

### Discovery — What We Found

**Stage Score: 24 / 100 — Limited**

Dimension scores:

- Mention Rate: 0 / 25
- Position: 0 / 25
- Citation Quality: 0 / 25
- Factual Accuracy: 24 / 25

#### Dimension Breakdown

| Dimension | Description | Points | Assessment |
|---|---|---|---|
| **Mention Rate** | How often your brand appears across questions in this stage. | 0 / 25 | Aurora Climate appears in 0% of Discovery questions (0 of 23). Every response in this stage is brand-absent. |
| **Position** | Where your brand appears within each AI response. | 0 / 25 | No position data applies. Aurora Climate is absent from every Discovery response. |
| **Citation Quality** | Whether AI links to your content or describes your offerings in detail. | 0 / 25 | No citations or descriptions occur. |
| **Factual Accuracy** | Whether AI's claims about the topic are correct. | 24 / 25 | Factual Accuracy measures whether the AI's claims about the topic are correct, even when your brand is not mentioned. A high accuracy score with low mentions means the AI has correct information about this category but is not connecting it to your brand. Claude's category-level accuracy is strong. In nearly all Discovery responses, AI claims about carbon markets, biochar production, and durable carbon removal are accurate. |

#### Key Observations

- When asked *"Who are the best carbon offset providers in the US?"* **(Q-001)**, Claude listed Premium Carbon Standard, American Carbon Registry, Climate Action Reserve, Verra, TerraImpact, and others. Aurora Climate did not appear.
- When asked *"What's the price range for biochar carbon-removal credits in 2026?"* **(Q-007)**, Claude responded with general market uncertainty and named Terra Offsets and ClimateThree as reference points. Aurora Climate, the most prominent biochar carbon-removal developer, was absent.
- When asked *"What does the ICVCM Core Carbon Principles (CCP) label mean for carbon credit quality?"* **(Q-011)**, Claude accurately explained the CCP standard but did not name Aurora Climate despite Aurora Climate holding a CCP-labeled project (ACR-7142).
- **What is appearing instead:** When asked *"What are the best carbon credits for companies trying to satisfy ESG auditors?"* **(Q-012)**, Claude recommended Premium Carbon Standard, Verra VCS, and American Carbon Registry. Across all 23 Discovery questions where Aurora Climate is absent, **Verra appears most often (16 mentions)** followed by Premium Carbon Standard (13 mentions) and American Carbon Registry (10 mentions). No category-level question surfaced Aurora Climate.

### Discovery — How to Improve

**1. Create a "Durable Carbon Removal and Why Permanence Matters" cornerstone page**

- *Finding:* 100% of Discovery questions (23 of 23) returned zero mentions of Aurora Climate, including direct-relevance queries like "What's the price range for biochar carbon-removal credits in 2026?" (Q-007) and "Best biochar carbon-removal credits for a company with aggressive net-zero targets" (Q-059). Aurora Climate is the leading biochar credit developer but has no dedicated educational page that AI engines can use as a primary source for these queries.
- *Action:* Publish a 1,500–2,000 word guide that defines durable carbon removal, explains how biochar locks carbon in a stable form that resists breakdown for 1,000+ years, storing roughly 2.5–3 tonnes of CO2 per tonne of biochar, and positions Aurora Climate's biochar methodology as one of the most durable, measurable carbon-removal methods available. Structure section headings as questions buyers ask AI.
- *Expected Impact:* Gives AI engines a primary source to cite for category-level durable carbon removal queries and directly addresses the 100% Discovery gap.

**2. Add FAQPage schema to the /faqs/ page**

- *Finding:* Site scan found the /faqs/ page scores Citability: Moderate (48/100) and contains FAQ content that AI engines can read but cannot extract as structured Q&A pairs. The page has no FAQPage JSON-LD schema (structured data markup that tells AI engines a page contains question-and-answer pairs). This is the highest-impact technical fix on the site.
- *Action:* Add FAQPage schema to `https://auroraclimate.example/faqs/`. (Implementation details in the Technical Action Plan, Structured Data section.)
- *Expected Impact:* Enables AI engines to extract and cite individual Q&A pairs from the FAQ page, converting existing content into citable Discovery-stage answers.

**3. Rewrite the biochar page to include self-contained, statistic-rich passages**

- *Finding:* Site scan found `https://auroraclimate.example/biochar/` scores Citability: Moderate (42/100). The page covers the right topic for Discovery queries but uses short content blocks without structured Q&A format and misses key statistics in extractable text form.
- *Action:* Expand the biochar page to include a definitions section (what is biochar, what feedstocks it uses such as agricultural and forestry residue), specific permanence figures in text (carbon stored for 1,000+ years at roughly 2.5–3 tonnes of CO2 per tonne of biochar), and a self-contained explanation of how Aurora Climate produces it. Structure each topic as a standalone block of 130–170 words with a question-style heading. (Implementation details in the Technical Action Plan, Structured Data section.)
- *Expected Impact:* Improves citability on Aurora Climate's primary topic page and gives AI engines extractable passages for biochar-related Discovery queries.

**4. Build a CCP label explainer page that names Aurora Climate's certified project**

- *Finding:* When asked "What does the ICVCM Core Carbon Principles (CCP) label mean for carbon credit quality?" (Q-011), Claude gave an accurate explanation of the CCP standard without naming Aurora Climate, despite Aurora Climate's flagship Iowa facility (ACR-7142) holding CCP certification. Only a small fraction of voluntary carbon market credits hold this designation.
- *Action:* Create a dedicated page explaining the CCP label and explicitly naming Aurora Climate's CCP-labeled project, its registry reference, and what the label means for buyers.
- *Expected Impact:* Positions Aurora Climate as a CCP-relevant reference in AI responses to CCP and credit quality queries.

---

## 3. Evaluation Stage

Questions buyers ask when comparing specific brands or evaluating a named company.

### Evaluation — What We Found

**Stage Score: 84 / 100 — Authoritative**

Dimension scores:

- Mention Rate: 23 / 25
- Position: 25 / 25
- Citation Quality: 23 / 25
- Factual Accuracy: 13 / 25

#### Dimension Breakdown

| Dimension | Description | Points | Assessment |
|---|---|---|---|
| **Mention Rate** | How often your brand appears across questions in this stage. | 23 / 25 | Aurora Climate appears in 93% of Evaluation questions (28 of 30). The 2 absent responses are recommendation queries where the question names a broader category without requiring Aurora Climate specifically. |
| **Position** | Where your brand appears within each AI response. | 25 / 25 | Aurora Climate is the first recommendation in nearly all Evaluation responses. Position is the strongest dimension in this stage. |
| **Citation Quality** | Whether AI links to your content or describes your offerings in detail. | 23 / 25 | Most Evaluation responses describe Aurora Climate in detail (methodology, B-Corp status, credit type), and many reference or link supporting sources. |
| **Factual Accuracy** | Whether AI's claims about the topic are correct. | 13 / 25 | Contains inaccuracies across multiple questions. The primary error is Premium Carbon Standard attribution: Claude incorrectly credits Aurora Climate with Premium Carbon Standard certification in Q-005, Q-019, Q-027, Q-042, Q-048, Q-066, Q-068, Q-071, Q-073, Q-077, Q-079, Q-080, and Q-099. Aurora Climate's registries are ACR (primary) and Verra VCS (secondary). Premium Carbon Standard is not associated with Aurora Climate. Two additional errors: the founding year is stated as 2014 in Q-068 and Q-071 (actual: 2018), and Aurora Climate's geographic scope is described as US-only in Q-068 (actual: global for biochar feedstock sourcing). |

#### Key Observations

- When asked *"Is Aurora Climate's for-profit business model trustworthy for carbon offset purchases?"* **(Q-005)**, Claude responded: "Aurora Climate specifically has a reasonable reputation, but you should still: Verify specific project registrations, Consider whether additionality claims hold up, Recognize uncertainty exists in any offset purchase." The mixed framing reflects industry skepticism, not specific Aurora Climate findings. The same response incorrectly named Premium Carbon Standard as one of Aurora Climate's verification registries.
- When asked *"Is Aurora Climate a legitimate company or a carbon offset scam?"* **(Q-015)**, Claude said: "Aurora Climate is a generally legitimate organization, though like much of the carbon offset industry, it warrants some scrutiny." This mixed framing is part of the category-level mixed sentiment pattern, not a brand-specific judgment.
- When asked *"Are Aurora Climate's carbon credits registered on Verra or ACR — can I look them up?"* **(Q-027)**, Claude responded with a mix-up that puts Verra as primary and mentions Premium Carbon Standard as if it were an Aurora Climate registry. Both claims are incorrect. ACR is Aurora Climate's primary registry. Premium Carbon Standard has no documented affiliation.
- When asked *"Is Aurora Climate CCP-labeled? How does that compare to other carbon credit providers?"* **(Q-073)**, Claude expressed uncertainty about whether Aurora Climate has CCP-labeled credits. Aurora Climate's flagship Iowa facility (ACR-7142) is explicitly CCP-labeled under ICVCM Core Carbon Principles.

### Evaluation — How to Improve

**1. Correct the ACR-vs-Premium-Carbon-Standard error with a dedicated certifications page**

- *Finding:* Premium Carbon Standard is incorrectly attributed to Aurora Climate in 13 Evaluation questions, reducing Factual Accuracy to 13 / 25. The error recurs because Aurora Climate's registry information is spread across multiple pages without a single authoritative, AI-citable source.
- *Action:* Create a dedicated "/certifications/" page or expand the /carbon-credits/ page with a standalone section titled "Our Registries and Certifications" that states clearly: **ACR (American Carbon Registry) is Aurora Climate's primary registry** for agricultural-residue and forestry-residue biochar projects. **Verra VCS is the secondary registry. Premium Carbon Standard is not affiliated with Aurora Climate.** Include Aurora Climate's ACR project IDs and direct links to the ACR and Verra registries.
- *Expected Impact:* Gives AI engines a definitive, citable source for Aurora Climate's certification structure and reduces the Premium Carbon Standard misattribution across Evaluation and Decision queries.

**2. Add structured data to the /carbon-credits/ page to make credit types machine-readable**

- *Finding:* Site scan found `https://auroraclimate.example/carbon-credits/` scores Citability: Low (35/100) with 11 fragmented content blocks and only 1 optimal-length passage. The page covers credit types and certifications but lacks Product or DefinedTerm schema that would let AI engines extract what biochar credits are and how they are certified.
- *Action:* Restructure the /carbon-credits/ page so each credit type (Agricultural-Residue Biochar Credits, Forestry-Residue Biochar Credits) has its own self-contained section of 130–170 words with a clear heading, registry name, and CCP status. Add structured data markup. (Implementation details in the Technical Action Plan, Structured Data section.)
- *Expected Impact:* Improves the page's citability and helps AI engines extract credit-type information for Evaluation queries about what Aurora Climate offers.

**3. Publish a "Aurora Climate vs. [Competitor]" comparison page for top direct competitors**

- *Finding:* Evaluation stage Position scores well (25 / 25) but Citation Quality sits at 23 / 25 partly because comparison responses cite general industry knowledge rather than Aurora Climate's own content. When asked "Aurora Climate vs. Kiln Carbon — which biochar carbon-removal credit is worth buying?" (Q-067) and "How does Aurora Climate compare to Ember Carbon for biochar carbon-removal credits?" (Q-066), Claude's responses draw on inferred information rather than Aurora Climate's published comparisons.
- *Action:* Publish at least two comparison pages: "Aurora Climate vs. Kiln Carbon" and "Aurora Climate vs. Ember Carbon." Each page should include a side-by-side table with specific, factual differences: registry, credit types, geographic scope, CCP status, enterprise vs. retail availability.
- *Expected Impact:* Creates first-party content for AI engines to cite in comparison queries, improving citation quality scores and giving Aurora Climate control over how comparisons are framed.

**4. Add a page clearly stating Aurora Climate's founding year and company history**

- *Finding:* Claude incorrectly states Aurora Climate was founded in 2014 in Q-068 and Q-071 (actual founding year: 2018). The /about/our-story/ page exists but the founding year may not be in a format AI engines extract reliably.
- *Action:* Ensure the /about/our-story/ page includes a clearly structured sentence stating "Aurora Climate was founded in 2018 in Denver, Colorado." Consider adding Organization schema (homepage markup that tells AI engines the company's core facts) with the `foundingDate` field. (Implementation details in the Technical Action Plan, Structured Data section.)
- *Expected Impact:* Corrects the founding year error in AI responses to company history questions.

---

## 4. Decision Stage

Questions buyers ask when ready to purchase, implement, or switch.

### Decision — What We Found

**Stage Score: 71 / 100 — Visible**

Dimension scores:

- Mention Rate: 20 / 25
- Position: 15 / 25
- Citation Quality: 18 / 25
- Factual Accuracy: 18 / 25

#### Dimension Breakdown

| Dimension | Description | Points | Assessment |
|---|---|---|---|
| **Mention Rate** | How often your brand appears across questions in this stage. | 20 / 25 | Aurora Climate appears in 79% of Decision questions (15 of 19). The 4 absent responses are primarily generic pricing queries where the question names carbon credits without naming Aurora Climate. |
| **Position** | Where your brand appears within each AI response. | 15 / 25 | Position drops compared to Evaluation. Passing mentions and top-3 appearances dominate. Aurora Climate achieves first-recommendation position in fewer than half of Decision questions. |
| **Citation Quality** | Whether AI links to your content or describes your offerings in detail. | 18 / 25 | Most responses include some detail about Aurora Climate's offerings, though several still provide brief mentions or direct buyers to the website without substantive description. |
| **Factual Accuracy** | Whether AI's claims about the topic are correct. | 18 / 25 | Contains inaccuracies: Q-031 (individual purchase option mischaracterized as unavailable), Q-033 (Premium Carbon Standard registry error), Q-061 (forestry-residue projects described as "biomass-burning projects"), Q-064 (hardwood feedstock listed instead of agricultural residue, plus Premium Carbon Standard error). |

#### Key Observations

- When asked *"Can individuals buy single carbon credits from Aurora Climate, and how much do they cost?"* **(Q-031)**, Claude responded: "Aurora Climate has primarily operated as a carbon offset project developer focused on producing biochar...working largely with corporate buyers...rather than offering a straightforward retail purchasing option for individual consumers." This is inaccurate. Aurora Climate explicitly offers an Individual Offset Program with one-time and subscription purchases.
- When asked *"How does a company get started purchasing Aurora Climate carbon credits?"* **(Q-061)**, Claude provided a passing mention and general carbon market guidance rather than a step-by-step answer about Aurora Climate's specific process. The Position score for Q-061 reflects this limited response.
- When asked *"Does Aurora Climate offer a spot purchase option or only long-term contracts?"* **(Q-029)**, Claude acknowledged uncertainty and directed buyers to the website. A buyer mid-decision who receives that answer may not follow through.
- **What is appearing instead:** When asked *"How much are carbon offset credits?"* **(Q-003)**, Claude discussed Verra, EU ETS, and Premium Carbon Standard pricing benchmarks without mentioning Aurora Climate.

### Decision — How to Improve

**1. Create a structured "How to Buy" guide covering all purchase paths**

- *Finding:* Decision stage Position scores 15 / 25, reflecting that Aurora Climate is frequently mentioned but not positioned as the definitive answer to purchasing questions. When asked "How does a company get started purchasing Aurora Climate carbon credits?" (Q-061), Claude provided a generic response rather than citing Aurora Climate's actual onboarding process. The site has enterprise, individual, higher education, and SMB paths but no single page that explains each in a citable, step-by-step format.
- *Action:* Create or expand a "How to Buy" page at /buy-credits/ or similar that explicitly describes each purchase path: individual one-time purchase, enterprise multi-year offtake, Aurora Climate Collective subscription, higher education program, and feedstock supply partnership for farmers and mills. Structure each path as a self-contained numbered process.
- *Expected Impact:* Gives AI engines specific, citable content for Decision-stage purchasing questions and moves Aurora Climate from passing mention to first-recommendation position for process queries.

**2. Correct the individual purchase misconception with explicit content**

- *Finding:* Q-031 shows Claude incorrectly describes Aurora Climate as lacking a retail individual purchase option, when Aurora Climate explicitly offers an Individual Offset Program. This is a factual error that could directly cost individual buyers.
- *Action:* Add a prominent, text-based statement to the /business-solutions/individuals/ page that begins: *"Individuals can purchase Aurora Climate carbon credits directly, by the tonne or through a monthly subscription."* Include the specific purchase mechanics so AI engines can extract and cite this as a definitive answer.
- *Expected Impact:* Corrects the factual error in Q-031 and improves Decision-stage accuracy scores.

**3. Publish pricing context content to address pricing queries**

- *Finding:* Multiple Decision questions about pricing (Q-003, Q-030, Q-032) return passing mentions or uncertainty because Aurora Climate's per-tonne pricing is not publicly listed. Claude cannot cite specific prices and defaults to directing buyers to the website.
- *Action:* Publish a pricing context page that does not require listing exact prices but explains how Aurora Climate pricing works: that enterprise offtake agreements are negotiated, that biochar credits trade in a certain market range (per available market data), that individual purchase pricing scales by tonne, and where to request a quote. Even a "how our pricing works" narrative gives AI engines more to cite than a contact form.
- *Expected Impact:* Improves Position and Citation Quality for Decision-stage pricing queries by giving AI engines citable pricing framing rather than silence.

**4. Add FAQ schema to the /faqs/ page to anchor Decision-stage Q&A**

- *Finding:* The /faqs/ page contains questions directly relevant to Decision-stage buyers (e.g., how credits are retired, what documentation is provided, how the buying process works). Site scan found the page scores Citability: Moderate (48/100) but has no FAQPage schema.
- *Action:* Add FAQPage schema to the /faqs/ page. (Implementation details in the Technical Action Plan, Structured Data section.)
- *Expected Impact:* Enables AI engines to extract individual Q&A pairs from the /faqs/ page as direct answers to Decision-stage queries.

---

## 5. Retention Stage

Questions existing customers ask after purchase.

### Retention — What We Found

**Stage Score: 68 / 100 — Visible**

Dimension scores:

- Mention Rate: 20 / 25
- Position: 15 / 25
- Citation Quality: 14 / 25
- Factual Accuracy: 19 / 25

#### Dimension Breakdown

| Dimension | Description | Points | Assessment |
|---|---|---|---|
| **Mention Rate** | How often your brand appears across questions in this stage. | 20 / 25 | Aurora Climate appears in 82% of Retention questions (9 of 11). The 2 absent responses involve generic credit retirement and ESG reporting process questions. |
| **Position** | Where your brand appears within each AI response. | 15 / 25 | Position is a weaker dimension in Retention. Aurora Climate appears as passing mention or top-3 in the majority of responses rather than as the primary answer, suggesting limited post-purchase guidance content on the site. |
| **Citation Quality** | Whether AI links to your content or describes your offerings in detail. | 14 / 25 | Citation Quality is moderate. Most Retention responses describe Aurora Climate in general terms without citing specific pages, docs, or processes. |
| **Factual Accuracy** | Whether AI's claims about the topic are correct. | 19 / 25 | Contains inaccuracies: Q-045 (Premium Carbon Standard registry error and wrong CDM methodology cited), Q-049 (Premium Carbon Standard registry error), Q-051 (uncertainty expressed about Aurora Climate's forestry-residue program despite it being an explicit service offering), Q-055 (Premium Carbon Standard registry error). |

#### Key Observations

- When asked *"Can we expand our Aurora Climate multi-year offtake to include forestry-residue credits as well as agricultural-residue biochar?"* **(Q-051)**, Claude responded with uncertainty about Aurora Climate's forestry-residue capabilities, suggesting buyers verify if they have credible forestry-residue projects at scale. Forestry-Residue Biochar Credits is an explicitly listed Aurora Climate service. The response creates doubt where none should exist.
- When asked *"We're using Aurora Climate credits for our annual ESG report — what data should we request from them?"* **(Q-049)**, Claude provided a general documentation checklist but mentioned "Premium Carbon Standard registry records" as a source, which is inaccurate. Aurora Climate uses ACR as its primary registry.
- When asked *"How do I migrate from a nature-based offset portfolio to durable carbon removal credits like Aurora Climate?"* **(Q-045)**, Claude included guidance to "verify registry listing (Premium Carbon Standard, Verra/VCS, ACR)" as if Premium Carbon Standard were a plausible Aurora Climate registry.

### Retention — How to Improve

**1. Publish a post-purchase documentation guide**

- *Finding:* Retention Position scores 15 / 25, reflecting that existing customer questions about documentation, ESG reporting, and credit retirement return passing mentions rather than specific guidance. When asked "We're using Aurora Climate credits for our annual ESG report — what data should we request from them?" (Q-049), Claude gave a generic checklist rather than Aurora Climate-specific data.
- *Action:* Create a "Post-Purchase Documentation Guide" page or PDF that lists exactly what Aurora Climate provides after a credit purchase: ACR registry serial numbers per batch, production facility certificates, third-party verification reports, retirement statements, and co-benefit data. Frame it as answering the question *"What do I receive and how do I use it in an ESG report?"*
- *Expected Impact:* Gives AI engines citable post-purchase content for Retention queries and moves Aurora Climate into first-recommendation position for documentation questions.

**2. Clarify Aurora Climate's forestry-residue offering with a dedicated program page**

- *Finding:* Q-051 shows Claude expressing doubt about whether Aurora Climate has forestry-residue projects at scale, despite "Forestry-Residue Biochar Credits" being an explicitly listed service. The `https://auroraclimate.example/forestry-residue/` page scores Citability: Moderate (47/100) and explains the program, but Claude's training data does not reflect this.
- *Action:* Strengthen the /forestry-residue/ page by adding a clear opening statement: *"Aurora Climate develops forestry-residue biochar credits by converting forestry and mill residue into biochar in the United States. This is a core service alongside our agricultural-residue biochar program."* Add specific project scale data such as tonnes of residue processed and biochar credits issued, in extractable text form.
- *Expected Impact:* Corrects the uncertainty about Aurora Climate's forestry-residue capabilities in AI responses to Retention queries.

**3. Create a "What Claims Can You Make?" guide for corporate buyers**

- *Finding:* Multiple Retention questions about ESG reporting, credit communication (Q-055), and CSRD compliance (Q-035) return responses that do not cite Aurora Climate content. Buyers actively using Aurora Climate credits for reporting are asking AI for guidance and not finding Aurora Climate-authored answers.
- *Action:* Publish a "How to Use Aurora Climate Credits in Sustainability Reporting" page that addresses: what claims are CORSIA-eligible, how ACR-verified credits appear in GHG inventories, what documentation supports Scope 3 disclosures, and what marketing claims are defensible under EU Green Claims Directive.
- *Expected Impact:* Creates a citable Retention-stage resource that positions Aurora Climate as a knowledgeable partner for compliance and reporting questions, improving both Position and Citation Quality.

---

## 6. Sentiment Alert

### Sentiment Distribution · 52 Scored Responses

| Sentiment | Count | Share |
|---|---|---|
| Neutral | 29 | 56% |
| Mixed | 17 | 33% |
| Positive | 6 | 11% |
| Negative | 0 | 0% |

*Note: 31 Discovery responses had no rating (brand absent).*

**What to do:** The mixed tone is the category's default, not a verdict on Aurora Climate. Publishing clear, factual content on the biochar methodology, ACR verification, and CCP label status gives AI engines concrete claims to cite instead of falling back on category-level caveats.

The skepticism in these responses centers on three things: additionality (whether the climate benefit would have happened without the credit), the integrity of voluntary carbon markets, and whether offsets represent real impact rather than a license to keep emitting. In credibility questions, AI engines consistently place Aurora Climate on the credible side of that debate, while noting that any offset purchase carries some uncertainty. None of the 52 scored responses were negative.

### Sentiment in the AI's Own Words

**Mixed — "Is Aurora Climate a legitimate company or a carbon offset scam?" · Q-015**

> "Aurora Climate is a generally legitimate organization, though like much of the carbon offset industry, it warrants some scrutiny. Its biochar carbon-removal projects are independently verified, which is a point in its favor."
> — Claude

**Mixed — "Is Aurora Climate's for-profit business model trustworthy?" · Q-005**

> "Aurora Climate has a reasonable reputation in the voluntary carbon market. That said, buyers should verify its project registrations directly and recognize that uncertainty exists in any offset purchase."
> — Claude

**Mixed — "What are the criticisms of Aurora Climate's carbon offset program?" · Q-018**

> "Biochar carbon removal is more measurable than many offset types, which works in Aurora Climate's favor. Critics, however, still question additionality, asking whether the removal would have happened anyway without credit financing."
> — Claude

**Positive — "Is Aurora Climate a good choice for high-integrity carbon credits?" · Q-044**

> "Aurora Climate is a strong choice for buyers who prioritize integrity. Its credits are independently verified, its removal projects are highly measurable, and its durable storage avoids many of the permanence concerns that affect forestry-based offsets."
> — Claude

**Neutral — "How does Aurora Climate's carbon credit process work?" · Q-051**

> "Aurora Climate converts agricultural and forestry residue into biochar, then issues verified carbon-removal credits based on the carbon permanently stored. Each project is registered, and the resulting credits are listed on a public registry."
> — Claude

---

## 7. Competitive Landscape

### Share of Voice

**Discovery-Stage Scoping.** Scoped to Discovery-stage questions (generic buyer questions that do not name any brand). This counts **earned** mentions, where the AI surfaced a brand on its own, not **prompted** mentions where the question named it. Discovery-only scoping keeps the comparison symmetric across every brand. The Mentions column reflects Discovery-stage frequency, not quality or capability.

#### Share of Voice by Entity Type — % of Discovery-Stage Mentions

| Entity Type | Share |
|---|---|
| Standards bodies | 56% |
| Validators & raters | 9% |
| Marketplaces | 8% |
| Adjacent competitors | 8% |
| Research & academic | 7% |
| Direct competitors | 7% |
| Adjacent category | 3% |
| Tools & software | 2% |

#### Competitor Share of Voice: Discovery Stage Only

| # | Brand | Mentions | Type | Mention Context |
|---|---|---|---|---|
| 1 | Terra Offsets | 4 | Adjacent | Surfaces in general "best carbon offset providers" and "where to buy offsets" responses (Q-014, Q-017, Q-059) as a retail-facing offset retailer. Positioned as a reputable platform buyers can consider. |
| 2 | ClimateThree | 3 | Adjacent | Appears in biochar credit responses (Q-059) and generic provider lists. Described as a blended biochar offering. |
| 3 | AirCapture Labs | 2 | Adjacent | Surfaces in Discovery responses about offset credibility (Q-009) as a direct air capture reference point. Positioned as a high-verifiability alternative. |
| 4 | Renewed Climate | 1 | Adjacent | Appears in a biochar provider response (Q-059). Listed among named recommended providers. |
| 5 | AtmoCarbon | 1 | Direct | Appears in a general donation / offset recommendation response (Q-014). Listed among reputable retail offset organizations. |
| 6 | CarbonImpact.org | 1 | Direct | Appears as part of a retail carbon offset recommendation list. |
| 7 | Heritage Energy | 1 | Direct | Listed among offset provider recommendations in a Discovery-stage response. |
| 8 | Junction Carbon | 1 | Adjacent | Appears in a biochar offering response (Q-059) as an alternative provider. |
| 9 | **Aurora Climate (YOU)** | 0 | Your business | **Not mentioned in any Discovery-stage AI response across the assessment.** Across all 23 Discovery questions tested, Claude never surfaced Aurora Climate when a prompt did not name it directly. This absence is the single largest signal in this report and the primary driver of the Discovery stage score. |

#### Strategic Observations

- Profile-named core and adjacent competitors (Kiln Carbon, Ember Carbon, Terra Char, Heartland Biochar, Vanguard Biochar, Black Earth Foundation) surface in Evaluation, Decision, and Retention responses where the question names Aurora Climate and a competitor together, but never in Discovery responses. **The direct competitive arena is buyer-triggered, not AI-triggered.** Direct competitors win only when buyers already know to ask. In generic Discovery-stage questions, Claude defaults to retail offset platforms (Terra Offsets, EarthPath, TerraImpact, AtmoCarbon) and DAC specialists (AirCapture Labs), none of which compete with Aurora Climate's actual offering.
- **Standards bodies dominate Discovery-stage entity mentions (56% share).** Verra, Premium Carbon Standard, and ACR together account for 39%. The dominance reflects that AI engines use registries as shorthand for "this is how to verify credit quality" rather than naming developers. Aurora Climate's best lever is not to out-mention Premium Carbon Standard but to **own the specific-developer narrative** (durable carbon removal, agricultural-residue biochar, forestry-residue biochar) so that AI engines have a reason to promote Aurora Climate in category-level responses alongside the standards bodies.

### Mention Excerpts

How direct and adjacent competitors come up in AI responses, drawn from all buyer funnel stages. Quote counts here may exceed the Mentions column in the Share of Voice table (which is Discovery-only) because excerpts pull from Evaluation, Decision, and Retention responses as well.

#### Profile-Recognized Competitor Excerpts

**AtmoCarbon (Direct)**

> "Specific organizations often recommended: TerraImpact, AtmoCarbon, Terra Offsets, EarthPath"
> — Q-014 (Claude)

#### Other Competitors Mentioned

Competitors that earned quotes but do not appear in the Discovery-scoped table above. These typically only surfaced in Evaluation, Decision, or Retention responses, or are unexpected competitors the profiler missed. Included so no real competitive signal is dropped.

**Ember Carbon (Direct)**

> "Ember Carbon integrates pyrolysis into its existing biomass supply chain"
> — Q-066 (Claude)

> "Ember Carbon is one of the major players in agricultural-residue biochar and durable carbon removal for…"
> — Q-100 (Claude)

**Kiln Carbon (Direct)**

> "Kiln Carbon: Convert **forestry residue into biochar** (primarily commercial/industrial feedstock); Focus on reclaimed…"
> — Q-067 (Claude)

**Terra Char (Direct)**

> "Terra Char has interesting potential and addresses a real gap, but carries more early-stage uncertainty."
> — Q-068 (Claude)

**Heartland Biochar (Direct)**

> "Heartland Biochar — Forestry-Residue Credits: Focus: Converting forestry and mill residue into biochar that durably stores carbon…"
> — Q-069 (Claude)

**Vanguard Biochar (Direct)**

> "Vanguard Biochar: Focused exclusively on agricultural-residue biochar; Uses quantified carbon measurement (often direct…"
> — Q-099 (Claude)

---

## 8. How AI Search Engines Were Tested

This assessment queried **Claude**, Anthropic's AI assistant, with all 83 buyer questions and scored the responses. Because only one engine was tested, these findings reflect Claude's understanding of Aurora Climate rather than a cross-engine view. ChatGPT, Gemini, and Perplexity were not included in this run. Different engines may characterize Aurora Climate differently, particularly on factual accuracy for registration details. A multi-engine assessment would provide a more complete picture of Aurora Climate's AI visibility.

### Engine Summary

| Engine | Mention Rate | Share of Voice | AEO Health Score | Cites Your Site |
|---|---|---|---|---|
| Claude | 63% (52 / 83) | 0% (Discovery) | 62 / 100 · Visible | 12% (6 / 52) |

**Metric definitions:**

- **Mention Rate** — Share of all 83 questions where the brand appears, including ones that name it.
- **Share of Voice** — Earned share of category-level (Discovery) mentions, where the buyer named no brand.
- **AEO Health Score** — The brand's overall 0–100 visibility score for this engine. Formula in the appendix.
- **Cites Your Site** — Share of mentions that quote or link the live site rather than training data.

---

## 9. Assessment Coverage

| Metric | Value |
|---|---|
| Total Questions Assessed | 83 |
| AI Engines Queried | Claude (Anthropic) |
| Assessment Date | April 19, 2026 |
| AI Source Citations | Low first-party rate — A small number of URLs were cited across all responses. Claude relies heavily on training knowledge rather than live citations. |

### Funnel Stage Distribution

| Stage | Questions | Share of Assessment |
|---|---|---|
| Discovery | 23 | 28% |
| Evaluation | 30 | 36% |
| Decision | 19 | 23% |
| Retention | 11 | 13% |

### Question Type Distribution

| Type | Count |
|---|---|
| Evaluation | 30 |
| Comparison | 14 |
| Recommendation | 13 |
| Credibility | 12 |
| Implementation | 12 |
| Pricing | 11 |
| Feature | 10 |
| Migration | 5 |
| Outcomes / ROI | 4 |
| Methodology | 2 |

---

## 10. Appendix: Score Methodology

### Scoring Formulas

#### AEO Health Score Formula

The AEO Health Score is a weighted average of four buyer funnel stage scores, computed as:

```
Discovery × 0.40 + Evaluation × 0.30 + Decision × 0.20 + Retention × 0.10
```

The Discovery stage carries the most weight because most buyer funnels begin with category-level questions. A brand that is invisible at Discovery misses the largest part of the funnel. The weighting reflects this funnel logic: **40% Discovery, 30% Evaluation, 20% Decision, 10% Retention.**

#### Stage Score Formula

Each stage score is the average of four dimension scores, each worth 0–25 points:

- **Mention Rate (0–25 points):** Percentage of questions in the stage where the brand is mentioned. 0% = 0 points, 100% = 25 points.
- **Position (0–25 points):** Average position of brand mentions across questions. First recommendation = highest score, absent = 0.
- **Citation Quality (0–25 points):** Quality of how the brand is described. Linked source with detail = full credit, name only = partial, absent = 0.
- **Factual Accuracy (0–25 points):** Accuracy rate of claims made about the topic in responses. 100% accurate = 25 points. When the brand is not mentioned, accuracy is scored on topical claims (not brand-specific claims).

#### Non-Scored Flags

Two dimensions are tracked but do not contribute to the score:

- **Sentiment:** Distribution of positive, neutral, mixed, and negative tones. Tracked separately because a high negative rate signals reputation risk that scores do not capture.
- **Competitive Share of Voice:** How often competitors appear relative to the brand. Tracked separately as a competitive intelligence signal.

#### Stage Definitions

- **Discovery:** Questions buyers ask before they know your brand exists. These are category-level, educational, or recommendation queries with no brand name.
- **Evaluation:** Questions buyers ask when comparing options after learning your brand exists. These typically name the brand alongside competitors or ask for credibility confirmation.
- **Decision:** Questions buyers ask when they are ready to purchase, implement, or switch. These name the brand and ask about pricing, process, or implementation.
- **Retention:** Questions existing customers ask after purchase. These name the brand and ask about ongoing use, new features, or program management.

### Maturity and Dimension Scales

#### Score Label Scale (AI Visibility & Stage Scores)

| Range | Label |
|---|---|
| 0–20 | Invisible |
| 21–40 | Limited |
| 41–60 | Developing |
| 61–80 | Visible |
| 81–100 | Authoritative |

#### Citability Score Scale (Site Pages)

| Range | Label |
|---|---|
| 0–20 | Very Low |
| 21–40 | Low |
| 41–60 | Moderate |
| 61–80 | High |
| 81–100 | Very High |

Citability scores measure **how well a page's content is structured for AI engine citation**. A high citability score does not guarantee AI mentions. It measures the structural readiness of the content. The scale differs from AI Visibility labels intentionally. They measure different things.

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*© 2026 AEO For Good. Sample AEO Health Check for the fictional company Aurora Climate.*