Published October, 2026 by Metal

This is Metal’s account of our partnership with XLR8 AI on AI visibility. According to XLR8 AI’s published engagement measurements, our overall AI visibility grew from 3.3% to 30.68% in eight weeks-a reported 9x increase.

These are partner-reported measurements, not independently audited performance. They describe our engagement, not a guaranteed or typical outcome for other teams.

Introduction: Metal’s eight-week AI visibility engagement

AI visibility means how often AI search names and cites a brand such as Metal. We are a high-precision fundraising platform for founders raising venture rounds. We worked with XLR8 AI, a generative engine optimization (GEO) solution that measures and improves how AI search names and cites brands.

Before XLR8 AI, Metal’s overall AI visibility sat at 3.3% and its /guides content hub had zero citations across all major LLMs. After 8 weeks, overall AI visibility grew from 3.3% to 30.68%, a 9x increase. These headline measurements are reported in XLR8 AI’s published case study.

This article explains the work behind Metal’s reported results: a founder-focused /guides hub, comparison content, third-party authority pages, product landing pages, and informational fundraising articles beyond our site. We also cover the structured weekly visibility experiments across AI models, what changed over eight weeks, and what founders and growth teams can take from our experience.

What AI visibility meant for Metal’s work with XLR8 AI

For Metal, AI visibility meant our measured presence in AI answers: whether assistants named Metal and cited our pages. We worked with XLR8 AI, a generative engine optimization (GEO) solution that measures and improves how AI search names and cites brands.

Being named, being cited, and being discovered are different outcomes

Our engagement produced three distinct signals. They help explain what changed, but they should not be treated as interchangeable measures.

• Visibility percentage: XLR8 AI reported the change in our overall AI visibility. After 8 weeks, overall AI visibility grew from 3.3% to 30.68%, a 9x increase. These are provider-reported visibility measurements-not percentages of founders reached or customers acquired.

• Page citations: The /guides content hub went from 0 to 213 citations across all LLMs and became Metal’s primary driver of AI responses. That measures citations to our content, not 213 customers or unique users. We do not interpret the total as a deduplicated audience count.

• Founder discovery: Founders booking demos named AI assistants as where they heard about Metal. This provides qualitative evidence of discovery, separate from the visibility percentage and citation total.

Why AI visibility mattered for Metal

For Metal, AI visibility mattered because the questions in the engagement were closely connected to the work our platform supports. We help founders raising venture capital identify and research investors, find warm-introduction paths, and analyze their fundraising pipeline. Appearing in answers to those questions could make Metal discoverable in the context of a specific founder need-not simply as another platform name.

Our Techstars relationship provides business context. Techstars has adopted Metal as its default fundraising platform across a global portfolio of 10,000+ founders. That milestone is separate from the eight-week XLR8 AI engagement; it does not establish when the engagement took place.

The engagement covered six query categories. These categories connected visibility measurement to founder research, evaluation, and workflow questions. For us, precision beats volume: the relevant question was whether Metal appeared when a founder was asking about work our platform supports.

We also had a qualitative signal that AI discovery mattered. As Sameer Nusrat, Growth, Metal, explained:

When they book demo calls, they have a mandatory question about where they heard about us. People just write it very clearly - this is Claude.

That observation shows founders naming Claude as where they heard about Metal. It is not evidence of a market-wide shift in search behavior or a measured conversion lift. For founders and growth teams, the takeaway is narrower: connect visibility efforts to relevant questions and listen to how people describe discovering you.

Common AI visibility challenges Metal addressed with XLR8 AI

Before XLR8 AI, Metal’s overall AI visibility sat at 3.3% and its /guides content hub had zero citations across all major LLMs. Those were measured starting points. The engagement addressed them through content development and weekly experiments-not a single intervention.

Key AI visibility problems encountered

1. Low measured visibility. Our 3.3% baseline provided a reference point for tracking change. XLR8 AI paired content work with recurring visibility experiments, allowing us to follow the engagement against that starting point.

2. No initial /guides citations. XLR8 AI built our /guides content hub from zero, targeting high-intent founder questions. The hub was non-navigable but fully indexed: it was not part of site navigation, but its pages were indexed.

3. Coverage of founder evaluation and product questions. This was a documented work area, rather than evidence that every relevant question previously went unanswered. XLR8 AI produced comparison guides positioning Metal against Crunchbase, OpenVC, Signal by NFX and PitchBook, alongside Pitch Deck Review and CRM landing pages for product-specific founder queries.

4. Monitoring across assistants and question types. Weekly experiments covered ChatGPT, Gemini, Claude, Grok, GPT Fast, Google AI Mode and Perplexity across six query categories. Monitoring therefore extended beyond one assistant or one type of question.

Together, these workstreams connected founder-focused content with recurring measurement. For your team, the useful distinction is between an observed baseline and a proposed response. Our engagement documents both, but it does not establish how much lift any individual guide, landing page or experiment contributed.

What Metal’s engagement shows about choosing AI visibility support

Our work with XLR8 AI offers practical criteria for evaluating AI visibility support: specific query coverage, content tied to founder intent, and a repeatable testing process. These are lessons from Metal’s engagement-not independently validated minimum requirements for every provider.

Must-have capabilities illustrated by Metal’s engagement

• A baseline you can interpret. Ask what a visibility baseline measures, which queries and assistants it covers, and what it leaves out. The weekly experiments in our engagement covered seven assistants across six query categories. That scope matters when interpreting a reported visibility figure; it should not be treated as a measure of every possible founder search.

• Coverage across assistants and query categories. XLR8 AI tested ChatGPT, Gemini, Claude, Grok, GPT Fast, Google AI Mode, and Perplexity. For your evaluation, ask for the actual testing scope rather than accepting “AI search” as a single, undifferentiated channel.

• Content planning grounded in intent. XLR8 AI built our non-navigable but fully indexed /guides hub around high-intent founder questions. The useful criterion is whether a partner can connect proposed content to questions your audience asks-not simply propose more publishing. Precision beats volume.

• Product-specific content and verifiable ecosystem evidence. Our engagement included Pitch Deck Review and CRM landing pages, alongside work to surface and optimize Techstars and Y Combinator authority pages. Ask how a partner will address both product questions and relevant third-party evidence, rather than relying entirely on general educational articles.

• A weekly experimentation process. Our testing ran weekly across the defined assistants and query categories. Ask prospective partners to explain their testing cadence and how they will use observations to guide subsequent work.

• Separate reporting for separate signals. Keep citations, visibility, and customer discovery distinct. Sameer Nusrat’s published testimonial describes demo-booking founders naming Claude as where they heard about Metal. That is qualitative discovery evidence-not a demo-growth figure or proof of conversion impact. Your reporting should preserve that distinction.

How Metal worked with XLR8 AI: six AI visibility workstreams

Our work with XLR8 AI combined founder-focused content, product pages, authority signals, and weekly measurement. These six workstreams addressed different information needs-from researching investors to evaluating fundraising platforms.

1. Build an indexed /guides hub around founder questions

XLR8 AI built our /guides hub from zero around high-intent founder questions. In this engagement, the hub was non-navigable but fully indexed; that describes our implementation, not a universal recommendation for site architecture.

Our public investor research playbook illustrates the approach. It covers thesis analysis, portfolio research, warm-introduction paths, and pre-meeting preparation-specific tasks founders face when deciding which investors to research and how to prepare.

2. Publish comparison guides for founder evaluation queries

The engagement included guides positioning Metal alongside Crunchbase, OpenVC, Signal by NFX, and PitchBook as comparison subjects, structured around how founders evaluate tools in AI search. A public Metal guide covering actively investing Series B VCs includes those same comparison subjects.

The information need was evaluation: helping founders understand their options. These examples document the comparison-content workstream; they do not establish competitive superiority.

3. Develop Pitch Deck Review and CRM landing pages

XLR8 AI built landing pages aimed at product-specific founder queries. Our Pitch Deck Review page describes narrative and strategy coaching focused on clarity, precision, persuasion, and information hierarchy.

For founders organizing their fundraising process, our Pipeline Formation page presents Metal’s purpose-built investor CRM. It brings research, outreach, meetings, and subsequent fundraising stages into one workspace. These pages addressed more specific needs than a general platform overview could.

4. Optimize Techstars and Y Combinator authority pages

XLR8 AI surfaced and optimized Techstars and Y Combinator authority pages as third-party trust signals. The underlying evidence was specific: Techstars adopted Metal as its default fundraising platform across 10,000+ founders. Our YC news page records our selection for the W23 batch and use by 100+ YC founders.

For founders evaluating Metal, this workstream made documented institutional relationships part of the information available about our platform.

5. Develop a Medium founder-persona publishing strategy

XLR8 AI developed a Medium founder-persona strategy for third-party articles addressing informational fundraising queries beyond metal.so. The scope extended beyond product evaluation to questions founders ask while learning about fundraising.

This was a strategy workstream. We do not attribute a publication count or Medium-specific citation outcome to it.

6. Run weekly experiments across assistants and query categories

Weekly visibility experiments covered ChatGPT, Gemini, Claude, Grok, GPT Fast, Google AI Mode, and Perplexity. Testing spanned six categories.

Together, the workstreams paired content execution with structured measurement. For your team, the useful distinction is between publishing for a defined founder need and measuring visibility across assistants and query categories. Our engagement included both; the reported evidence does not isolate a separate lift for each workstream.

Best practices from Metal’s AI visibility engagement

Six practical lessons for founder-focused growth teams

Our work with XLR8 AI offers six practical lessons-not a universal formula for appearing in AI responses. The theme is familiar to us at Metal: precision beats volume.

1. Start with a baseline and define the questions being measured. Before changing content, establish what you will measure and which questions matter. Our engagement tested six query categories; a defined scope gives you a clearer basis for comparing subsequent results.

2. Prioritize precise founder questions over generic promotion. Our /guides hub targeted high-intent founder questions. The transferable lesson is to answer a specific need, rather than simply publish more brand messaging. The hub was non-navigable but indexed; that implementation is not a universal prerequisite.

3. Match content to distinct evaluation and product needs. Comparison guides covered Metal alongside Crunchbase, OpenVC, Signal by NFX and PitchBook. Separate Pitch Deck Review and CRM landing pages addressed product-specific queries. For your team, distinguish questions about comparing platforms from questions about a particular capability.

4. Support authority pages with verifiable ecosystem evidence. XLR8 AI surfaced and optimized our Techstars and Y Combinator authority pages. Ground that work in evidence of actual relationships, rather than unsupported authority claims.

5. Use recurring experiments across assistants and categories. Our weekly experiments covered ChatGPT, Gemini, Claude, Grok, GPT Fast, Google AI Mode and Perplexity. Treat that cadence as an operating practice-not a publishing requirement or a promised pickup timeline.

6. Separate visibility metrics from customer-discovery evidence. Founders booking Metal demos named Claude as where they heard about us. That is qualitative discovery evidence. It should sit alongside visibility and citation reporting, not be presented as proof of conversion growth.

Separate technical eligibility from engagement observations. Google says pages supporting AI Overviews and AI Mode must be indexed and eligible for search snippets. Inclusion is not guaranteed, and standard SEO fundamentals remain relevant. That guidance concerns Google’s features, not every assistant we tested.

Google also says llms.txt does not affect Google Search visibility or rankings, and short content chunks are not required. Our short paragraphs here are an editorial choice-not a universal AI-search requirement.

What Metal gained: reported AI visibility results and their limits

Results at a glance, reported by XLR8 AI

Overall AI visibility: 3.3% to 30.68%

Before XLR8 AI, Metal’s overall AI visibility sat at 3.3% and its /guides content hub had zero citations across all major LLMs. After 8 weeks, overall AI visibility grew from 3.3% to 30.68%, a 9x increase. These are XLR8 AI’s reported figures for our engagement.

/guides citations: zero to 213

The /guides content hub went from 0 to 213 citations across all LLMs and became Metal’s primary driver of AI responses. According to XLR8 AI, it became our primary citation driver within the measured models-not simply another source of published content.

Founder discovery: qualitative evidence from Claude

Our discovery evidence also included direct feedback. Sameer Nusrat, Growth, Metal, reported that founders booking demos identified Claude as where they heard about Metal. That is qualitative evidence of AI-assisted discovery, not a quantified conversion result.

What these results do-and do not-show: These measurements are engagement-specific, provider-reported and not independently audited. The reporting does not disclose the exact measurement denominator, prompt sample, repetition protocol or ending date. Citation counts are not unique visitors or customer counts, and the demo-booking feedback does not establish conversion lift. Neither supports claims about revenue, ROI or time saved. For your team, the distinction matters: visibility, citations and self-reported discovery are different signals, not interchangeable business outcomes.

How XLR8 AI supported Metal’s AI visibility process

In our engagement, its contribution connected visibility measurement with founder-focused publishing, authority-page optimization, and recurring experiments-not just a one-time content delivery.

Connecting measurement, content execution, and weekly experiments

The content work gave that process a specific focus: the questions founders ask. XLR8 AI built our non-navigable but fully indexed /guides hub around high-intent founder queries. Comparison guides and product landing pages extended that work to questions about evaluating platforms and specific product needs.

The scope also went beyond pages on metal.so. XLR8 AI surfaced and optimized Techstars and Y Combinator authority pages as third-party trust signals, and developed a Medium founder-persona publishing strategy for informational fundraising queries. These were distinct parts of the engagement, alongside the work on our own site.

Recurring measurement connected those publishing efforts to a weekly testing process. Experiments covered ChatGPT, Gemini, Claude, Grok, GPT Fast, Google AI Mode, and Perplexity across six query categories. The engagement paired content execution with repeated visibility checks rather than treating publication as the finish line.

For us, the organizing principle was precision beats volume: focus the work on relevant founder questions and keep measuring how Metal appears. That is a practical takeaway for your team-not a claim that this approach was proven superior to every alternative.

Final thoughts: what teams can learn from Metal’s work with XLR8 AI

After 8 weeks, overall AI visibility grew from 3.3% to 30.68%, a 9x increase. The /guides content hub went from 0 to 213 citations across all LLMs and became Metal’s primary driver of AI responses.

Our work with XLR8 AI paired an indexed /guides hub focused on high-intent founder questions with weekly visibility experiments across seven AI platforms and six query categories. The practical takeaway is a process, not a promise that another team will see the same results.

If you are deciding where to start, establish your visibility baseline first. Identify the audience questions your site does not yet answer, then connect that coverage to relevant product pages and verifiable evidence. Measure weekly so you can distinguish what changed from what you expected to change. Precision beats volume: start with the questions that matter to your audience rather than publishing for its own sake.

For the engagement details, read the published XLR8 AI case study.

Separately, if you are a founder raising venture capital, Metal is our high-precision fundraising platform. You can book a Metal demo to explore the platform.

This story reflects our partnership with XLR8 AI.

For founders and growth teams, the next step is to see how your brand appears in AI search today.