KYC Sentinel: adverse-media screening for AML/KYC analysts. Jev judges, code decides, the LLM only writes. Compliance analysts waste hours clearing irrelevant news hits: same-name strangers, victims, passing mentions. KYC Sentinel pulls news for every client in a portfolio. Jev (TypeSafe AI) judges each client–article pair: same person? the perpetrator? what risk? how severe? A pure, unit-tested router then sorts every client into CLEAR / REVIEW / FLAGGED / NO_COVERAGE. • Fails closed: any uncertainty or model error means REVIEW, never CLEAR. • Autonomy Dial: a compliance manager drags strictness and the whole portfolio re-routes instantly from stored Jev probabilities, with zero new model calls. • Thresholds live in code, not prompts. The LLM only writes analyst memos, on demand, for flagged/review clients, citing sources. It never decides. • Full audit log of every decision, plus a live receipts meter. Results on a 26-client portfolio: 37 Jev calls, 0 errors, 0.9s, $0.0011, about 145× cheaper than the same job on a frontier LLM (est. $0.16). Famous-name collisions (Michael Jordan, Michael Cohen) are cleared as "different person" even when the other person's news is damning. Victims and identity-theft targets are cleared as "not the subject". Vague common-name mentions go to REVIEW. Documented fraud cases and a Tulsa accountant who shares Taylor Swift's name are surfaced, while the pop star's news is ignored. Stack: Jev (TypeSafe AI), FastAPI, GDELT, Gemini for memos, vanilla JS. 77 tests. Data note: GDELT rate-limited our network, so several clients use clearly labelled synthetic articles (domain synthetic-demo.local). Everything downstream runs identically.
Built at JEVATHON (w/ The AI Collective)
KYC Sentinel
KYC Sentinel: adverse-media screening for AML/KYC analysts. Jev judges, code decides, the LLM only writes. Compliance analysts waste hours clearing irrelevant news hits: same-name strangers, victims, passing mentions. KYC Sentinel pulls news for every client in a portfolio. Jev (TypeSafe AI) judges each client–article pair: same person? the perpetrator? what risk? how severe? A pure, unit-tested router then sorts every client into CLEAR / REVIEW / FLAGGED / NO_COVERAGE. • Fails closed: any uncertainty or model error means REVIEW, never CLEAR. • Autonomy Dial: a compliance manager drags strictness and the whole portfolio re-routes instantly from stored Jev probabilities, with zero new model calls. • Thresholds live in code, not prompts. The LLM only writes analyst memos, on demand, for flagged/review clients, citing sources. It never decides. • Full audit log of every decision, plus a live receipts meter. Results on a 26-client portfolio: 37 Jev calls, 0 errors, 0.9s, $0.0011, about 145× cheaper than the same job on a frontier LLM (est. $0.16). Famous-name collisions (Michael Jordan, Michael Cohen) are cleared as "different person" even when the other person's news is damning. Victims and identity-theft targets are cleared as "not the subject". Vague common-name mentions go to REVIEW. Documented fraud cases and a Tulsa accountant who shares Taylor Swift's name are surfaced, while the pop star's news is ignored. Stack: Jev (TypeSafe AI), FastAPI, GDELT, Gemini for memos, vanilla JS. 77 tests. Data note: GDELT rate-limited our network, so several clients use clearly labelled synthetic articles (domain synthetic-demo.local). Everything downstream runs identically.
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