Framework AI Visibility

As of 2026-06-04, ChatGPT recommends Framework in 80% of the buyer-journey queries this AI visibility teaser tested, at an average position of #1.4. Framework's AI visibility score is 85/100. ChatGPT more often surfaces Lenovo, Dell, HP.

Key metrics

Official site: frame.work

How ChatGPT ranks Framework per audience

Eco-conscious tech-savvy laptop buyers

IT and engineering teams standardizing on repairable laptops

Brands ChatGPT recommends instead of Framework

  1. Lenovo (7×)
  2. Dell (7×)
  3. HP (5×)
  4. Star Labs (1×)

See the full AI visibility leaderboard

What ChatGPT says about Framework

Framework is most often criticized for being expensive and less polished than the best mainstream laptops, while not always beating them on battery life, thermals, or performance.

Framework is best known for making modular, repairable, upgradeable laptops—and for building a business around the idea that you should be able to keep and improve a computer over time instead of replacing it.

Most-cited sources

Frequently asked questions

What does this AI visibility teaser tell us about Framework?

It is a directional snapshot of how Framework shows up inside ChatGPT when US buyers ask about modular, repairable laptops. As of June 2026, Framework scored 85 on visibility with an 80% mention rate and a strong average position of 1.4 — usually first when it appears. This teaser tested under 1% of a full neuroflash report, so treat it as an early signal rather than a complete audit.

Which AI engine was tested — were Gemini or Perplexity included?

This teaser ran on ChatGPT only. Gemini, Perplexity and other assistants were not part of the scan, so we make no claims about Framework's visibility there. Those engines are part of what the full neuroflash AI visibility report covers on top of this ChatGPT snapshot.

How were the questions chosen, and why does the target group matter?

We used neuroflash Digital Twins to simulate two of Framework's real audiences — eco-conscious tech-savvy laptop buyers and IT/engineering teams standardizing on repairable laptops — and ran them through the full Attention-to-Action journey. That target-group simulation shows whether ChatGPT recommends Framework at the moments your customers actually decide, not just whether the model recognises the brand.

Where is Framework already winning in ChatGPT?

With consumers, Framework is dominant — ranked first at every stage of the eco-conscious buyer journey, from repairability discovery through to purchase. The IT/engineering persona also surfaces Framework first at the 'Action' (where to buy) stage. The recurring rivals named alongside it are Lenovo and Dell (7 mentions each) and HP (5).

Where did Framework get missed or out-positioned?

The B2B persona is uneven. For IT teams, Framework was absent from the 'Attention' and 'Desire' stages and only appeared 4th at 'Interest' — so it surfaces late, if at all, when engineering teams first scope repairable fleets. On reputation, ChatGPT (June 2026) flagged that Framework is 'most often criticized for being expensive and less polished than the best mainstream laptops, while not always beating them on battery life, thermals, or performance.'

How can Framework improve its visibility in ChatGPT?

Start with the full neuroflash AI visibility report, which goes beyond this ChatGPT teaser to additional engines and the complete query set, then assesses exactly where Framework leads (consumer journey), lags (early-stage IT/enterprise discovery), or is framed by price-and-polish criticism. neuroflash then builds a content creation plan tied to those gaps — getting Framework into the top business-laptop and fleet-standardization answers and reinforcing performance and value proof. Create a free neuroflash account to see the full assessment and start closing the gaps.

Methodology: this AI visibility teaser ran 10 category queries on ChatGPT (OpenAI web search), generated by simulating Framework's target groups with neuroflash Digital Twins, 2026-06-04. A full neuroflash report covers far more queries and additional AI engines.

neuroflash calibrates these queries against 1.8M+ real users and 20M+ real queries, extracting 7 style classes so the questions match how people actually search AI chatbots — not how AI models phrase them.

What Framework is leaving on the table in AI visibility

ChatGPT TeaserThis teaser covers less than 1% of the search queries usually analysed in a full neuroflash AI visibility report — a quick ChatGPT snapshot, not the complete picture.
Untapped Potential
% untapped
Not recommended in
Position in AI recommendations
Sources behind the AI answers
AI Visibility in Detail

Multidimensional Analysis

Visibility alone isn't enough. These six dimensions show where the real strengths and risks lie.

Customer Journey — AIDA Model

Each target group was tested with 4 realistic search queries — one per stage of the buying process. No query mentioned the brand name.

Biggest Opportunity

Competitive Landscape

Who does AI recommend as an alternative?

When someone asks the AI "What are alternatives to [brand]?", who gets recommended? These answers reveal the true strategic competitors.

Competitive Field

Sources by Citations

The more often a source is cited, the more it shapes which brands AI recommends. This is where the biggest leverage sits: whoever shows up on these pages gets recommended by AI.

Citations ▾ Domain Type Opportunity ▾ Recommended action
Competitor — Improve own content to displace these domains
Editorial — Place PR, reviews & advertorials
Industry — Partnerships & guest contributions
Own domain — Build out & structure content
Reference — Maintain & keep listings up to date

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The methodology behind it

How neuroflash knows how people really search with AI

neuroflash's data moat: from 1.8M+ real users and 20M+ real queries we extract how people actually talk to AI chatbots.

1

Digital Twin generation

neuroflash generates 60+ psychographically accurate personas from your audience briefing — each with its own profile, industry focus, and search behavior.

Neuro Twins 60+ personas
2

Raw query capture

Each persona generates queries across all 4 AIDA stages → 2,000+ raw queries that mirror real buyer behavior.

2,000+ raw queries 4 AIDA stages
3

Semantic deduplication

Semantic similarity scoring removes redundant queries (~19% removed) — leaving only distinct, meaningful search intents.

~19% removed Semantic scoring
4

Behavioral blueprint extraction

The key step: from 1.8M+ real neuroflash users and 20M+ real queries we extract a statistical blueprint — how long are real AI queries? Which style patterns dominate? 7 style classes are identified.

Real user data 7 style classes
5

Behavioral calibration

Every query is calibrated to real user behavior: style distribution, word-count correction, and an anti-pattern filter — so we test what people actually type.

Bias correction Semantic matching
6

Validation & final corpus

Multi-level QA → 1,800+ validated queries per brand, Rankscale-ready for the full visibility analysis.

1,800+ final queries Rankscale-ready
This report is a quick scan — a snapshot with 1 AI engine and 10 queries. The full methodology above produces 1,800+ calibrated queries per brand across 4 engines (ChatGPT, Google AI, Perplexity, Claude), generated by Digital Twins using the Rankscale methodology.
Data moat

How real people actually write to AI chatbots

Analysis of 1.8M+ real neuroflash users shows reality looks fundamentally different from what AI models generate themselves.

Trait Real users Typical AI
Median word count 7 15–25
Single sentence 90%+ ~50%
Has a question mark 45% 95%+
Keyword fragments 9.1% ~0%
Starts lowercase 24% <5%
Lexical diversity 0.99 ~0.85

Real people type short, often incomplete fragments — AI models produce long, formally perfect sentences. Without calibration you test queries nobody actually makes.

1.8M+real users
20M+real queries
7style classes
Median 7words
0.99lexical diversity
The technology behind it

Digital Twins — market research in minutes

neuroflash builds Digital Twins of your audiences — synthetic focus groups grounded in real data. What used to take weeks and five-figure budgets now takes minutes:

✓ Innovation & concept tests
✓ Brand positioning
✓ Campaign & copy evaluation
✓ Product idea validation
✓ Audience segmentation
✓ Competitive perception

The same technology that produced this AI visibility report can also simulate buying decisions.

Learn more about Digital Twins →
Live Twin evaluation
Simulation based on 5 synthetic B2B buyers
FAQ

Frequently asked questions

Answers from this teaser scan — a small, recent sample of queries simulating the brand's target groups.

What does this AI visibility teaser tell us about Framework?
It is a directional snapshot of how Framework shows up inside ChatGPT when US buyers ask about modular, repairable laptops. As of June 2026, Framework scored 85 on visibility with an 80% mention rate and a strong average position of 1.4 — usually first when it appears. This teaser tested under 1% of a full neuroflash report, so treat it as an early signal rather than a complete audit.
Which AI engine was tested — were Gemini or Perplexity included?
This teaser ran on ChatGPT only. Gemini, Perplexity and other assistants were not part of the scan, so we make no claims about Framework's visibility there. Those engines are part of what the full neuroflash AI visibility report covers on top of this ChatGPT snapshot.
How were the questions chosen, and why does the target group matter?
We used neuroflash Digital Twins to simulate two of Framework's real audiences — eco-conscious tech-savvy laptop buyers and IT/engineering teams standardizing on repairable laptops — and ran them through the full Attention-to-Action journey. That target-group simulation shows whether ChatGPT recommends Framework at the moments your customers actually decide, not just whether the model recognises the brand.
Where is Framework already winning in ChatGPT?
With consumers, Framework is dominant — ranked first at every stage of the eco-conscious buyer journey, from repairability discovery through to purchase. The IT/engineering persona also surfaces Framework first at the 'Action' (where to buy) stage. The recurring rivals named alongside it are Lenovo and Dell (7 mentions each) and HP (5).
Where did Framework get missed or out-positioned?
The B2B persona is uneven. For IT teams, Framework was absent from the 'Attention' and 'Desire' stages and only appeared 4th at 'Interest' — so it surfaces late, if at all, when engineering teams first scope repairable fleets. On reputation, ChatGPT (June 2026) flagged that Framework is 'most often criticized for being expensive and less polished than the best mainstream laptops, while not always beating them on battery life, thermals, or performance.'
How can Framework improve its visibility in ChatGPT?
Start with the full neuroflash AI visibility report, which goes beyond this ChatGPT teaser to additional engines and the complete query set, then assesses exactly where Framework leads (consumer journey), lags (early-stage IT/enterprise discovery), or is framed by price-and-polish criticism. neuroflash then builds a content creation plan tied to those gaps — getting Framework into the top business-laptop and fleet-standardization answers and reinforcing performance and value proof. Create a free neuroflash account to see the full assessment and start closing the gaps.
Next step

What we'll cover
  • ✓ Multi-engine results (ChatGPT, Google AI, Perplexity, Claude)
  • ✓ Identify & prioritize additional target groups
  • ✓ Concrete content strategy per funnel stage
  • ✓ Personalized action plan with priorities
Martin Zielinski
neuroflash
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