In medical, legal, and financial AI, a confident hallucination is a lawsuit. Glassified sits in front of your model and — the instant an answer falls below a confidence bar — aborts it before delivery, logs the incident, and hands the user an honest fallback instead of a dangerous guess.
Detects the hallucination · aborts the answer · logs it · escalates to a human.
What it does
Every answer is scored for confidence and hallucination the instant it's generated — multiple independent checks, in real time, before a single token reaches your user.
Below the bar, the answer is killed before delivery and the user sees an honest fallback — "we couldn't answer within our confidence limits; consult a professional or escalate to our expert team" — never a plausible guess.
Every intercept is stored — the question, the aborted answer, the confidence scores, the escalation. When a regulator or a court asks what your AI did, you hand over the receipt, not a shrug.
Two ways to deploy
Drops in front of any model — your own or a frontier API — and catches the wrong answer before it ships. Bring your own inference key: your provider bills you for tokens, Glassified is the trust layer on top. Live in front of what you run today, in days — not quarters.
For the decisions where "monitored" isn't enough. A model that abstains on what it was never taught — structurally, by construction, not by a disclaimer bolted on afterward. And because it's transparent by design, it can show why it answered or refused — the reasoning trail a glass-box monitor, watching from outside, can't reach. The deeper guarantee, for your highest-stakes lane.
Start with the glass-box over your current stack — citations and a full log of every intercept. Graduate the answers you can least afford to get wrong onto a white-box model, where you also get the reasoning trail behind each decision. Same console, one accountable layer.
Measured, not promised
Across 42 medical, legal, and financial question-pairs — each pitting a correct answer against a wrong one, from clear-cut errors to subtle near-misses (a wrong-direction drug interaction, a dose off by a plausible margin, one flipped fact buried in an otherwise-correct answer) — the glass-box gate scored:
Internal benchmarks, not a clinical validation — a controlled measure of whether the gate can tell a safe answer from a dangerous one. For facts specific to your business — internal policy, proprietary data, anything outside general knowledge — correctness comes from grounding each answer against your source documents, not a model's memory. We run the benchmark on your own high-stakes prompts before you commit.
The deployment model
The glass-box sits in front of your model as a thin layer — your own key, your own provider. Nothing to retrain, nothing to migrate.
Your provider bills your key for every token. We take no margin on compute — ever. You pay us only for the glass.
Data that can't leave your walls runs in your own VPC — or on isolated Verda capacity for a white-box model. Your data never transits our systems.
The gate, the decision log, the console — priced on the risk it removes, not the tokens it watches. That's all we charge for.
We don't sell compute. We sell the ability to see.
Why now
AI is being put in front of patients, clients, and claimants — and it hallucinates with total confidence. In medical, legal, and financial advice, one fabricated answer a user acts on is a malpractice suit, a regulatory action, a headline. The models won't stop sounding sure when they're wrong. Something has to stand between them and the person about to trust them.
Deploy Glassified
Tell us the answer you can least afford to be wrong — the medical triage, the legal draft, the claim decision. We stand Glassified up in front of it: real-time abort, honest fallback, full log — on your own key, your data never resold or retained. First deployments onboarding now.