About RankLabs
AI systems are already shaping how brands are discovered, compared, and recommended. But for most companies, how AI interprets their brand is invisible.
RankLabs exists for teams that cannot afford that blind spot.
The Problem
Modern AI assistants do not simply surface pages or rank results. They interpret brands, products, and entities across fragmented signals, then generate answers and recommendations that can change quietly over time.
When that interpretation shifts, brands often don't know:
Invisible Drops
Why their products stopped appearing
Competitor Drift
Why competitors are referenced instead
Silent Changes
Why AI answers changed while analytics stayed flat
Unknown Root Cause
Whether the issue is technical, structural, or external
Most tools can show changed.
Very few can explain .
The Shift to Agentic Commerce
Ecommerce is entering a new phase.
AI systems are no longer just answering questions or surfacing results. They are beginning to act on behalf of users — comparing products, filtering options, and making purchase decisions automatically.
In this agentic commerce model:
Eligibility before discovery
Products must be eligible before they are discoverable
Interpretability before selection
Brands must be interpretable before they are selectable
Data gaps disqualify
Missing or ambiguous data becomes a disqualifier, not a ranking issue
This changes the nature of risk.
Brands don't lose visibility gradually. They are excluded entirely, often without any signal in traditional analytics.
RankLabs was built for this shift.
In an agentic environment, visibility is no longer about ranking — it's about whether AI systems can confidently act on your behalf.
Who Built RankLabs
Sangmin Lee
Founder & CEO
RankLabs was founded by Sangmin Lee, an engineer who previously worked at Peraton Labs, a U.S.-based research organization supporting advanced technology programs for the Department of Defense.
Peraton Labs traces its roots to Bell Labs, an institution that played a foundational role in the development of the internet, email, and many of the systems underlying modern computing, and continues to operate at the forefront of applied research alongside agencies such as DARPA.
Within that environment, AI and machine learning systems are evaluated under conditions where correctness, traceability, and failure modes matter more than scale or polish. Models are treated as components inside larger systems, not as black boxes producing convenient outputs.
That discipline directly shaped RankLabs' philosophy: instrument inputs, explain outcomes, and avoid inference where determinism is possible.
RankLabs applies this rigor to a domain that has largely operated on assumption and heuristics — how AI systems interpret and represent brands.
Our Approach
RankLabs is built on a simple premise:
AI behavior should be observable, explainable, and accountable.
Instead of relying on prompt-only inspection or surface-level monitoring, RankLabs instruments both sides of the system:
Brand Signals
Structured data, entities, canonical sources
RankLabs
Instruments both sides
AI Behavior
Answers, recommendations, citations
By analyzing inputs and outcomes together, RankLabs can map changes in AI answers back to concrete, verifiable causes — including data gaps, entity fragmentation, and structural inconsistencies.
Every decision is grounded in deterministic analysis.
That is what separates infrastructure from heuristics.
What RankLabs Is
RankLabs is a software platform (SaaS) used by enterprise commerce teams to understand how AI systems represent their brand and products over time.
Teams use RankLabs to:
Observe
How AI answers reference your brand
Detect
Representation changes and regressions
Identify
Technical and data-level causes
Validate
That official sources are used correctly
Maintain
Consistency as AI systems and websites evolve
RankLabs is designed for continuous use, not one-time audits or consulting engagements.
How RankLabs Is Different
Most AI visibility tools observe outputs only.
RankLabs is built to explain causality.
By controlling and analyzing the input layer AI systems rely on, alongside real-world AI responses, RankLabs can explain changes occur — not just that they occurred.
That distinction matters when revenue, trust, and brand integrity are at stake.
Who RankLabs Is For
RankLabs is built for organizations where AI misrepresentation creates material business risk, including:
Enterprise & high-growth commerce brands
Teams accountable for revenue outcomes
Companies with complex catalogs and structured data pipelines
RankLabs is not positioned as a lightweight monitoring tool or a services-led offering.
Our Position
RankLabs gives brands visibility and control.
We instrument signals, identify structural gaps, and generate content that reinforces entity clarity and strengthens the structural graph.
Every intervention is grounded in deterministic analysis — so AI systems represent your brand with confidence.
Why This Matters Now
AI systems evolve continuously, often without notice. Brands that cannot observe those changes are forced to react after damage occurs.
RankLabs provides the infrastructure to prevent that.
Ready for AI visibility?
See how AI systems represent your brand today. Book a walkthrough with our team.