See the math.
Trust the data.
Glass Box shows you exactly why every risk score is what it is — the raw institutional data, the formula, and an independent audit before you see any number. No black box. No hallucinations displayed unchecked.
Eight legendary lenses. One audited score.
Each lens is pretrained on a legendary investor's documented strategy and reads your holdings for growth and risk — every view traces back to the same audited data. Lenses inform; they never advise.
Berkshire Hathaway · Value / Buy & Hold
Fidelity Magellan · GARP
Citadel · Multi-Strategy
Bridgewater · Global Macro
Renaissance Tech · Quantitative
Millennium · Multi-Strategy
D.E. Shaw · Quant
Soros Fund Mgmt · Global Macro
click a lens to hear its story · drag / arrows to slide · filter to spin the desk
Not AI magic.
AI math — audited.
Agent A1 pulls institutional data
Debt-to-equity, Altman Z-Score, free cash flow, 30 years of history — directly from Financial Modeling Prep's institutional API. Twelve Data provides real-time technicals. No aggregators, no intermediaries.
Agent A5 translates to plain English
A configured narrative agent generates a clear summary from the underlying risk metrics. Every number in the text traces back to its raw data source. Every claim is auditable, not asserted.
Agent A6 audits before you see it
The Auditor cross-checks the narrative against raw API data. If anything doesn't match, it's flagged and corrected before display. You only see verified output — and we publish the error rate publicly, every week.
What the models learned — and from where
Transparency isn't only about the audit. It's about what went in. Here's the provenance behind every score.
Institutional sources
FMP + Twelve Data — the same feeds funds pay for, never scraped aggregators.
Deterministic core
Risk math (Altman Z, D/E, beta) is formula-based and reproducible — no model guesswork on the numbers.
Narrative models
LLMs explain, they don't decide. Trained to summarize the metrics, constrained to cite every claim.
Audit layer
A6 checks every narrative against raw data. Weekly error rate published — nothing hidden.
Was this verification useful?
Your rating helps us track how useful the evidence trail actually is — logged locally in this demo, not yet wired to a live trust-score pipeline.
Built for the conversation with your client, not just the score.
An evidence trail your clients can see
When a client asks "why does it say that?", point to the exact API response and the audit that checked it — not a black-box score.
Compliance-ready by default
Every verification is logged with its source data, its narrative, and its audit result. Export the trail for E&O documentation or a compliance review.
Multi-client, white-label ready
Run verifications across client portfolios and hand back reports branded for your practice, not ours.
“Finally — a platform that shows me where the number came from. My clients ask 'why does it say that?' and I can now point to the exact API response. That's career-defining trust-building.”
“The Discrepancy Rate widget changed how my clients think about AI. Instead of 'is this reliable?' they now ask 'what's the error rate this week?' That's a fundamentally different — and better — conversation.”
Start free. Upgrade when you trust us.
No dark patterns. All prices visible. Cancel anytime.
Notes on transparency.
Why we publish our own error rate
Every AI research tool makes mistakes. Most hide them. Here's why showing ours, every week, is the point.
How Agent A6 audits Agent A5
A walkthrough of the verification step that runs before any narrative reaches your screen — and what happens when it disagrees.
Reading a Debt/Equity ratio without the jargon
The raw metrics behind a risk score, explained the way we'd explain them to a client.