A founder-led intelligence practice for machine-mediated trust.
Memetic Labs is a founder-led AI Buyer Intelligence practice led by Marc Osborn. It helps B2B SaaS leaders understand how AI systems translate public evidence into buyer-facing judgments about trust, risk, category fit, and enterprise readiness.
The company exists because buyers no longer only search. They ask systems to interpret.
When a buyer asks an AI assistant who to trust, who to shortlist, or what risks to consider, the answer is not just a search result. It is a synthesis of public evidence, stale descriptions, third-party claims, visible proof, and missing context.
Memetic Labs focuses on that synthesis layer. The goal is to help serious SaaS teams see how they are being interpreted before that interpretation shapes shortlists, objections, and competitive preference.
Direct senior involvement, with a deliberately focused scope.
One accountable strategic lead
Memetic Labs is led directly by Marc Osborn. His work sits at the intersection of AI systems analysis, buyer perception, GTM positioning, competitive intelligence, and applied LLM testing. Every engagement is led directly by the person responsible for the diagnosis, interpretation, and executive briefing.
Judgment matters more than headcount
This work rewards pattern recognition and executive translation more than dashboard volume. The value is seeing how AI interpretation connects to positioning, trust, proof, and revenue risk, then making the next action legible to leadership.
Method development: the practice has been shaped through hands-on audits and live buyer-prompt testing across high-trust categories. The methodology is designed to surface defensible findings, not manufacture certainty.
Credibility comes from constraint.
No black-box scoring
The work starts with actual buyer questions and actual AI answers. If the output is messy, inconsistent, or surprising, that is part of the finding.
Claims need sources
Material claims are evaluated against public evidence. We look for what is present, stale, missing, buried, ambiguous, or contradicted.
No model-control claims
AI systems change. No one can guarantee exact outputs across systems and time. The responsible work is baseline, diagnose, improve evidence, and retest.
Evidence agenda, not theory
The output is designed to help leadership decide what public proof, messaging, trust content, and competitive framing should change first.
Operating standards: We do not claim to control AI systems. We do not guarantee outputs, rankings, or citations. We distinguish what a model says from what the evidence can support. We prioritize public evidence buyers can access. We treat AI interpretation as a commercial signal, not a magic trick.
The advantage is not claiming certainty. It is knowing where uncertainty becomes commercial risk.
Memetic Labs treats AI outputs as buyer-facing perception signals, not just technical artifacts. The question is what a buyer would believe after reading them.
The work separates what the company wants buyers to believe from what the public record currently makes easy for AI to support.
Findings are interpreted through sales consequences: shortlisting, incumbent defaulting, objection formation, enterprise readiness, and trust loss before pipeline.
Recommendations are ranked for practical action: what to publish, clarify, ungate, update, support, or make machine-readable first.
Defensible promise: Memetic Labs does not promise to control AI systems. It promises to show what they currently say, identify the public evidence most likely shaping those answers, and turn that into a prioritized agenda your team can act on.
Best for high-trust SaaS teams with real proof that is not yet machine-legible.
Strong fit
- B2B SaaS companies selling into mid-market or enterprise.
- Teams with real customer, security, or deployment proof that is not obvious publicly.
- Companies in categories where buyers need education before confidence.
- Founders, CEOs, CROs, and CMOs who own trust, positioning, and revenue outcomes.
Weak fit
- Companies looking for guaranteed AI rankings.
- Teams unwilling to change public evidence.
- Businesses where buyers make decisions without research or risk evaluation.
- Anyone looking for fake authority, fake reviews, or hidden manipulation.
Start with a live diagnostic.
If the outputs are clean, the call is still useful. If they are not, you will know what the public record is doing before buyers do.