Realm Labs provides runtime AI observability and control for enterprise applications, monitoring every AI interaction in real time to detect hallucinations, off‑goal answers, data leaks, and unsafe behavior. When a response crosses predefined thresholds, the platform can block, rewrite, route, or log the interaction, preventing failures from reaching users and enabling continuous improvement through pattern detection and drift correction.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises deploying generative AI often encounter unexpected hallucinations, off‑goal answers, data leaks, and unsafe behavior that only become apparent after the model is in production, leading to reliability, compliance, and brand‑risk issues.
Solution
Realm Labs provides a runtime AI observability and control platform that monitors every model interaction in real time. By instrumenting the model’s internal decision signals, the system detects hallucinations, off‑goal responses, data leakage, and unsafe outputs as they occur. When a response exceeds user‑defined thresholds, Realm can automatically block, rewrite, route, or log the interaction, preventing faulty outputs from reaching end users. Detected incidents are fed back into a learning loop that surfaces patterns, corrects model drift, and ensures the same failure does not repeat. The platform operates across the AI stack, offering unified policy definition, enforcement, and actionable insights for production AI systems.
Target Audience
Primary customers are enterprises and product teams that embed generative AI models into customer‑facing applications and require reliable, compliant, and secure AI performance at scale.
Features
- Real‑time monitoring of model‑level signals to surface hallucinations, off‑goal answers, and data‑leak risks before response delivery
- Configurable policies that trigger automatic actions: block, rewrite, route, or log offending interactions
- Continuous learning loop that aggregates incidents, identifies recurring patterns, and updates safeguards to mitigate model drift
- Cross‑layer observability providing deep visibility into AI behavior, emerging risks, and internal decision pathways
- Integrated compliance and security controls to protect sensitive data and enforce organizational, regulatory, and ethical boundaries