Sentience
OS
Human-in-the-loop governance interface for LLM output verification and trust scoring. Scaling safety through behavioral design.
Role
Design Lead
Timeline
6 Months (2024)
Context
AI Infrastructure
Output
OS Interface
The Problem Statement
As LLMs scaled into production, monitoring their "hallucination" rate became an manual nightmare. Legacy dashboards were too slow to detect toxicity shifts, leading to safety protocols being triggered only after reputational damage was done.
Critical Insight
"Safety audits were taking 48 hours per release, creating a massive friction point for fast-moving engineering teams."
The Design Process
Model Shadowing
Working with ML engineers to understand how "attention weights" translate into human-readable trust scores.
Safety Gating
Designing the friction architecture for manual overrides in automated safety loops.
Validation
Measuring "Time to Detect" (TTD) with red-teamers using the new Sentience OS interface.
The Solution Details
Trust Scoring
Implemented a three-tier Trust Scoring system (Safe, Guarded, Blocked) that automatically routes high-risk LLM outputs to human reviewers before they reach the user.
Automated Gating
Created a visual "Rule Builder" that allows safety officers to deploy new policy gates across 1,000+ models simultaneously without writing code.
Measured Impact
+98%
Safety Accuracy
Correct detection of hallucination and bias in LLM production responses.
-95%
Audit Overhead
Reduction in manual safety verification time via automated intent routing.
50ms
Gating Latency
Average time to process safety gates across high-traffic global endpoints.