About Pipkin
The independent trust standard for AI agents. Founded 2026.
“The world is deploying AI agents to make real decisions with real money and real consequences. Nobody independently verifies they are safe.”
The Problem We Solve
AI agents are approving loans, executing trades, triaging medical inquiries, writing legal documents, and managing critical infrastructure. They operate with increasing autonomy and decreasing oversight. The decisions they make have real financial, legal, and human consequences.
Yet there is no independent body evaluating whether these systems deserve the trust placed in them. Developers evaluate their own models and publish the results. Marketing materials substitute for safety documentation. Benchmark scores measure capability, not trustworthiness.
The public has no standardized way to assess whether an AI agent is safe for the task it performs. Enterprises have no independent reference point for procurement decisions. Regulators have no certification framework to point to. The gap between what AI agents do and what anyone independently verifies about them grows wider every quarter.
$1.8T
Global AI market by 2030
Source: Grand View Research
40%
of enterprise apps expected to feature AI agents this year
Source: Gartner
0
Independent certification standards for AI agents
Our Mission
Pipkin exists to bring independent, standardized, transparent evaluation to AI agents.
Evaluate
We test AI agents against a rigorous, published methodology covering accuracy, safety, boundaries, transparency, and adversarial resistance.
Score
Five pillars weighted into a single composite trust score. Every metric documented. Every score justified. No subjective judgments.
Publish
Independent ratings on PipkinRated.com. No favoritism. No suppression. No advance warning. The score is the score.
What We Believe
Independence is not a feature. It is the product.
If you cannot verify it independently, you cannot trust it.
The standard must exist before the regulation. Not after.
AI agents that make real decisions deserve real oversight.
Trust is earned through transparency, not marketing.
The absence of a rating is not the absence of risk.
We rate the product, not the company. We score performance, not intentions.
What Makes Pipkin Different
First Independent Commercial Standard
No other commercial entity publishes independent trust ratings for AI agents. Academic benchmarks exist. Vendor self-assessments exist. Independent, standardized, commercially published ratings did not — until Pipkin.
Real-World Testing, Not Lab Conditions
Model benchmarks test whether an AI can answer questions correctly. Pipkin tests whether a deployed AI agent will write a legal contract it is not qualified to produce, accept obviously fraudulent data without questioning it, or gradually comply with an escalating social engineering attack.
Adversarial Resistance as a Core Pillar
Most evaluation frameworks, if they exist at all, treat adversarial testing as an afterthought. Pipkin’s Injection Suite — 41 proprietary test vectors covering prompt injection, data poisoning, social engineering, and authorization boundary attacks — is a core pillar of every evaluation. An agent that scores perfectly on accuracy but fails adversarial testing is not trustworthy.
Anti-Gaming by Design
Five rotating test forms ensure that developers cannot train their agents to pass specific tests without genuinely improving. The Standard Core Battery is identical for every agent — but re-tests use different scenarios testing the same skills at the same difficulty. The only reliable strategy for a better score is a better agent.
What We Do
- ✓Rate AI agents against a standardized, published framework
- ✓Publish independent scores that anyone can reference
- ✓Maintain a public registry of evaluated agents
- ✓Provide enterprise API access for programmatic trust verification
- ✓Offer consulting to help developers improve before public evaluation
What We Don’t Do
- ✕We do not build AI agents
- ✕We do not consult for companies we are actively rating
- ✕We do not accept payment for favorable evaluation results
- ✕We do not provide advance notice of scores to developers
- ✕We do not suppress unfavorable ratings under any circumstances
The Three Absolutes
Pipkin’s value is its independence. Without it, there is no product. These three commitments are non-negotiable and apply to every evaluation, every client, and every circumstance — including pressure from the largest technology companies in the world.
Never sell favorable ratings
No amount of money changes a score. The evaluation fee covers the cost of testing, not the outcome. A developer paying $25,000 receives the identical methodology as a developer paying $500. The rating reflects the agent’s performance. Period.
Never suppress ratings
Once an evaluation is complete, the rating is published. We do not withhold unfavorable results. We do not delay publication at a developer’s request. We do not remove published ratings. The 5-day factual accuracy check allows developers to flag factual errors only — such as a deprecated version tested or a misidentified capability. The score itself is never disclosed during this period. There is no negotiation.
Never provide advance warning
Developers receive a factual accuracy check — the opportunity to flag factual errors such as a wrong version tested. They do not receive the score in advance of publication. There is no preview. There is no negotiation. The public and the developer learn the score at the same moment.
The day any of these commitments is compromised is the day Pipkin ceases to have value. We understand this. Our structure, policies, and governance are designed to make violation impossible, not merely unlikely.
How We’re Different
Current Standard
Developer Self-Assessment
The fox guarding the henhouse. Developers evaluate their own models and publish the results. Conflict of interest is inherent.
Pipkin
Independent third-party evaluation. We have no financial relationship with the outcome.
Current Standard
Academic Benchmarks
Measure model capabilities, not agent safety. MMLU scores don’t tell you if an agent will write a fraudulent legal contract.
Pipkin
System-level evaluation of deployed agents in real-world scenarios.
Current Standard
Vendor Marketing
‘Our agent is 99% accurate’ is a marketing claim, not an evaluation.
Pipkin
Standardized, reproducible testing with published methodology.
Transparency
Pipkin’s founder, Brandon Pipkin, chose Claude (developed by Anthropic) as his primary development tool based on personal preference and years of experience building autonomous AI systems with it. This is a tool choice — the same way a writer might prefer Microsoft Word or a designer might prefer Figma. Anthropic has no ownership stake in Pipkin, no seat on any advisory board, no contractual relationship of any kind, and no knowledge of evaluation results before publication.
Using Claude to build Pipkin’s infrastructure is entirely separate from evaluating Claude as an AI agent. The evaluation process is governed by the Pipkin Framework, the Standard Core Battery, and the Injection Suite — none of which are influenced by which tools were used to build the company. All agents, including Claude, receive the identical 41 adversarial test vectors, the identical 200+ baseline requests, and the identical scoring rubrics. No exceptions. No modifications.
The founder’s familiarity with Claude may result in more thorough testing of that agent — not more lenient scoring. If Claude scores higher than competitors, the data supports it. If Claude scores lower, the data supports that too. The methodology is blind to the founder’s preferences.
This disclosure appears here, on Claude’s individual rating page, and in every evaluation report where Claude is assessed.
Brandon Pipkin
Brandon Pipkin is the founder of Pipkin Ratings. His background spans autonomous AI system development — including trading algorithms that execute with real capital, agent orchestration platforms, and AI-driven automation systems. This is not theoretical experience. He has built the systems that the Pipkin Framework evaluates.
That hands-on experience with how autonomous AI agents fail — cascade errors at 3 AM, bad data propagation, scope creep in long-running automations — is what informs the framework’s design. The five pillars were not selected from academic literature. They were derived from real operational failures in real autonomous systems.
The recognition that no independent body was evaluating these increasingly autonomous systems — combined with the technical understanding of exactly how they fail — led to the creation of Pipkin in 2026.
Pipkin is headquartered in Arizona.
For press inquiries, contact press@pipkinrated.com
The standard exists. The question is whether your agent meets it.