Consumer Reports wrote a standard for AI that touches your money. We graded ourselves against it.

Consumer Reports wrote a standard for AI that touches your money. We graded ourselves against it. By Brady Bastian, founder of Pendragon Consumer Reports recently published a Consumer Finance AI Standard — nine principles, each with specific, testable criteria, for any AI product that handles a person's financial life. It's the most concrete answer anyone has given to the question every one of our users eventually asks: what should I be able to expect from this thing? Our habit, when someone publishes a serious benchmark, is to run it against ourselves and publish what we find — including the parts that don't flatter us. We did it with our own study protocol and with poisoned data. So here is the same treatment for the Consumer Reports standard. One thing first, plainly: these grades are our own self-assessment. Consumer Reports has not evaluated, certified, or endorsed Pendragon. If they ever build a certification program, we'll be first in line — and this post is us showing our work in advance. The scoreboard, before the spin can start Nine principles. Our own grades: strong on six, partial on two, and one principle where we currently fail almost entirely. The rest of this post is the evidence, the failures get their own section, and the one we fail is the most interesting thing in the whole standard. Where the architecture does the work A pattern emerged as we went criterion by criterion: the principles we're strongest on, we're strong on by construction — not because a policy says so, but because the system is physically shaped that way. Duty of Loyalty: the standard asks whose interests the AI serves when they conflict with the company's. Our honest answer is that we removed the conflict instead of managing it. Pendragon's only revenue is your subscription. No affiliate commissions, no sponsored recommendations, no selling your data, no order flow. When Arthur suggests paying off your card instead of investing, there is no revenue line that prefers either answer. Most of the standard's conflict-of-interest criteria are, for us, questions about machinery we deliberately never built. Honesty and Non-Manipulation: the standard requires accuracy that holds up when a user pushes back — no caving to insistence, no telling optimistic users what they want to hear. This is the core of how Pendragon is built: the math runs in a deterministic layer the conversation cannot reach. You can ask Arthur the same question with hope, fear, or fury, and the numbers that come back are computed identically each time, signed, with the work shown. The standard also asks companies to disclose sycophancy testing — we've gone further and pre-registered our evaluation protocols publicly so you can check whether we actually ran what we promised. Privacy and Data Minimization: your checking data lives in a checking workspace; your retirement data in a retirement workspace; each isolated at the database layer with its own credentials. The specialist analyzing your spending physically cannot read your portfolio. Access is read-only — we can't move a dollar — and deleting your data is two typed words in Settings, hard-deleted, no support ticket. Our beta testers' favorite screen, unexpectedly, was the delete screen. Transparency: the standard wants explanations, sources, confidence, and alternatives delivered with the recommendation, not on request — and it explicitly says generic disclaimers don't count. That describes the provenance footer under every answer Arthur gives: which accounts were checked, what was calculated, how confident he is, and a badge that says Unverified when he couldn't check. When we found a bug last week where a fallback fabricated a confidence score, we treated it as the most serious bug in the product and said so in the commit history. Agency and Control and Security: the standard's hardest security criterion — that communications between AI agents be authenticated and resistant to privilege escalation — is rare because almost nobody runs multi-agent finance systems yet. We do, so every cross-specialist request in Pendragon is cryptographically signed and scoped by a governance contract that lists exactly which actions it permits. And the standard's entire section on controlling autonomous financial actions mostly dissolves against a product that takes none: Arthur computes, explains, and remembers. He does not move money. Our terms contain no mandatory arbitration clause. The part that doesn't flatter us A self-assessment that only finds passing grades is marketing. Here's what we currently fail, in plain terms: We fail most of the Duty of Vigor. The standard's ninth principle asks financial AI to be an advocate: to know the consumer-protection laws that bear on your situation, to tell you when a denial can be appealed and how, to say whether an offer is above or below market, to help you assemble a challenge and then leave the decision with you. Arthur does almost none of this today. He'll tell you what your finances say; he won't yet tell you that the adverse decision you're staring at comes with a legal right to demand reasons. We think this principle is the most forward-looking thing in the standard, and it reads to us like a product roadmap. The standard's own phrasing — the AI "equips the consumer and leaves the decision to act with the consumer" — is close to word-for-word how we describe Arthur's job internally. We intend to earn this grade. There is no human to escalate to. The standard expects a pathway from AI to a responsive human for urgent or distressing situations. Right now, we're a founder-run product and the pathway is email. That's honest, but it isn't a system, and it doesn't meet the criterion. You can delete your data but not yet download it. Deletion is first-class; export doesn't exist yet. It's now committed work. Our fairness testing is committed but not finished. Our pre-registered protocol includes demographic-neutrality testing with published results — including the rule that we publish the baseline before fixing anything it finds. The commitment is public; the full run isn't done. Until it is, we claim the method, not the result. The paperwork lags the architecture. No SOC 2 attestation, no published penetration test, no formal algorithmic impact assessment document, no accessibility audit against WCAG, English only. Startups earn these over time, and we will — but the standard is right that trust you can't hand to a skeptical reader in document form is trust that doesn't fully count. One more line the standard draws that we want to be precise about: registration. Pendragon is not a registered investment adviser and doesn't act like one — no trades, no asset management, no "you should." Arthur shows you outcomes and trade-offs computed from your own data; the decision is always yours. As our planning features deepen, we treat that boundary as a real constraint on design, not a technicality to lawyer around. What we're committing to In the spirit of the standard — and in writing, so you can hold us to it: data export is next on the build list; a defined escalation path to a human with a response-time commitment replaces "email the founder"; the fairness baseline run completes and publishes, favorable or not; and we'll publish a trust and governance document mapping our architecture to this standard's nine principles, criterion by criterion, so nobody has to take a blog post's word for it. Why this standard matters Here's the uncomfortable truth the standard exposes, and it isn't about us: almost nothing on the market today meets it. Loyalty conflicts are the business model of free finance apps. Sycophancy is the default behavior of every general-purpose chatbot. Provenance — showing which of your accounts an answer actually came from — mostly doesn't exist. Consumer Reports wrote a standard for products that largely haven't been built yet. We're closer than most, not because we're smarter, but because we made the architectural bets early: money math sealed off from the conversation, isolation by default, loyalty by business model, and receipts under every answer. The gaps we have are documents to write and features to build. The gaps an ad-funded or commission-funded AI has are its revenue model. One of those is fixable. Read the standard. Then ask any financial AI you use — ours included — the questions it implies: Who pays you? Can you show me where that number came from? What happens when I push back on you? And when someone tells me no, will you tell me my rights? Arthur will answer the first three today. The fourth is why we're still building.