Overview & Result
Prolific AI builder Hassan demonstrated a two-tier fraud detection architecture. In processing 100 high-risk customer emails, running a heavyweight frontier reasoning model on everything would be prohibitively slow and expensive. Hassan used Jev to screen all 100 emails in 1.42 seconds (14ms per email), resolving 88 clear benign/fraud cases immediately, and routing the remaining 12 borderline cases to Kimi K3 for deep forensic reasoning, achieving 96% accuracy at a total cost of $0.07.
How Jev fits in the loop
- Incoming emails and transaction metadata are immediately fed to Jev Choice classifier with probability calibration.
- If Jev confidence exceeds 0.92 (clear fraud or clear legitimate), transaction is auto-flagged or auto-approved in under 20ms.
- If confidence is within the uncertain window (0.40 - 0.91), the payload is dispatched via background queue to Kimi K3 for chain-of-thought analysis.
- Final decisions and audit traces are logged to database with complete probability and reasoning provenance.
How to reproduce
- Review Hassan's architecture diagrams and benchmark script in the shared repository.
- Set up FastAPI endpoint receiving incoming email payloads.
- First pass: invoke Jev Choice API with confidence thresholds (tau=0.92).
- Route transactions falling into [0.40, 0.91] confidence band to Kimi K3 asynchronous task worker.
Why this build matters
The quintessential pattern for production agent architectures: Jev handles 90% of traffic at microsecond speeds and zero cost, preserving expensive frontier reasoning models exclusively for ambiguous edge cases.
Reported performance
Reported by authorCost: $0.07 / 100 emails
Latency: 1.42s / 100 items (14ms / item)
Limitations
- Threshold calibration must be validated against real company chargeback and fraud base rates.
- Requires fallback logic if external secondary LLM provider experiences latency spikes.