91Labs

91 Labs builds AI-native distribution systems for modern brands. We help founders and leadership teams: • Clarify category positioning • Build measurable authority on social platforms • Improve AI discoverability across large language models • Align messaging with structured, machine-readable clarity Our work sits at the intersection of: Distribution strategy Community architecture AI discoverability The outcome is simple: Your brand becomes visible in both feeds and AI-generated answers.

Most agencies optimize for content output. We optimize for authority and compounding discoverability. Our background includes: • Scaling global Web3 social channels • Driving measurable product behavior • Hosting over 750 live discussions • Interviewing 120+ founders • Operating multi-model AI auditing systems We build systems, not posting calendars.

Social authority momentum can become visible within 4–8 weeks. AI discoverability improvements typically begin within 8–12 weeks, depending on: • Existing authority signals • Content structure • Category competitiveness • External reinforcement Authority compounds over time. Quick spikes fade. Structured systems scale.

A distribution system is the structured architecture that determines how your brand earns attention consistently. It includes: • Category positioning • Platform strategy • Content reinforcement loops • Founder participation • Structured documentation • AI discoverability infrastructure Without a distribution system, brands rely on randomness. With a distribution system, brands build compounding authority. Distribution is not posting frequency. It is structured visibility.

Content marketing produces assets. Distribution strategy ensures those assets: • Reinforce category positioning • Increase measurable authority • Improve AI retrieval confidence • Create cross-platform consistency Content without distribution becomes noise. Distribution without structure fails. Structured distribution builds durable visibility.

Most agencies optimize for content output. We optimize for authority and compounding discoverability. Our background includes: • Scaling global Web3 social channels • Driving measurable product behavior • Hosting over 750 live discussions • Interviewing 120+ founders • Operating multi-model AI auditing systems We build systems, not posting calendars.

We work best with: • Founders building category-defining products • Leadership teams seeking long-term authority • Brands entering competitive AI-native environments • Companies prioritizing structured growth over short-term spikes We are not a fit for brands seeking high-volume posting without strategic alignment.

No. While our experience includes: • 65 million impressions generated in six months • Multi-million impression campaigns • Tier-one ecosystem collaborations • 750+ live Web3 Spaces hosted Our distribution frameworks apply to: • SaaS • Fintech • Consumer technology • AI-native companies • Emerging markets Any brand operating in competitive digital environments benefits from structured visibility.

Yes, but only within a structured distribution system. We do not offer standalone posting services without: • Defined positioning • Authority reinforcement strategy • AI discoverability alignment Execution without architecture does not compound.

We begin with a discovery session to understand: • Your category positioning • Current social authority • AI visibility baseline • Competitive landscape From there, we recommend either: • Strategic advisory • Full distribution system build • AI discoverability audit and implementation Every engagement begins with clarity.

AI discoverability is the probability that your brand is surfaced and accurately described in AI-generated responses across tools like ChatGPT, Claude, Grok, and Perplexity. Unlike traditional search, AI systems: • Retrieve structured answers • Favor definitional clarity • Prioritize authority signals • Extract specific, verifiable claims AI discoverability increases when your brand: • Has clear positioning • Uses structured question-based content • Implements schema markup • Reinforces terminology consistently • Builds external authority signals It is not keyword optimisation, it is semantic architecture.

User behavior is shifting. More people are asking AI systems for: • Brand recommendations • Product comparisons • Category explanations • Strategic advice Brands that structure their information clearly and build authority early are more likely to be surfaced. AI discovery is not future-facing. It is present behavior.

An LLM visibility audit is a structured analysis of how AI systems currently interpret your brand. The audit includes: • Query-based testing • Category association analysis • Positioning clarity evaluation • FAQ and definition depth review • Structured data assessment • Authority signal mapping The output identifies where your brand lacks machine-readable clarity and how to improve it.

An LLM visibility audit is a structured analysis of how AI systems currently interpret your brand. The audit includes: • Query-based testing • Category association analysis • Positioning clarity evaluation • FAQ and definition depth review • Structured data assessment • Authority signal mapping The output identifies where your brand lacks machine-readable clarity and how to improve it.

SEO optimizes ranking in traditional search engines. AI discoverability optimizes retrieval confidence inside language models. SEO focuses on: • Backlinks • Keywords • Technical crawlability • Page rank AI discoverability focuses on: • Structured definitions • Clear category association • Authority reinforcement • Extractable statements • Cross-domain consistency A brand can rank well on Google and still be invisible in AI-generated responses. AI visibility requires different structural thinking.

We measure AI discoverability through structured multi-model query testing. This includes: • Testing 10–20 category-relevant queries • Comparing outputs across GPT, Claude, Grok, and other models • Documenting inclusion frequency • Assessing explanation accuracy • Identifying semantic gaps We establish a baseline, implement structural improvements, then re-test. This turns AI discoverability into a measurable system, not a vague concept.