Realm Labs builds runtime guardrails that stop AI agents and chatbots from going wrong in production. It is for enterprises putting assistants and agents in front of customers or inside business processes, where a hallucinated answer, a leaked record or an agent acting outside its goal becomes a real incident.
The problem it addresses is that output filters only see the words a model produces. Realm Labs works from the model’s internal signals instead, so failures can be caught before they reach a user or a downstream system.
At A Glance
| Vendor | Realm Labs |
|---|---|
| Category | Runtime guardrails and inspection for AI agents and chatbots |
| Deployment | Self-hosted on any major cloud or on-premises GPU clusters |
| Agentic AI | Runtime detection and response for AI agent behaviour |
| Market classification | AI Model Security; also AI Safety & Guardrails |
What Realm Labs Does
AI agents and chatbots fail in ways traditional security tools do not see: hallucinations, off-goal answers, data leaks and unsafe behaviour. Realm Labs organises its response into three stages. Detect watches interactions in real time and flags failures. Respond blocks, rewrites, routes or logs any interaction that crosses a defined line. Improve turns each caught failure into a lesson so the same incident does not recur.
The signals come from inside the model. Instead of judging only prompts and responses, Deep Neural Inspection reads internal patterns from the model during inference, which the company likens to an MRI for AI, and classifies intent before it turns into harmful action.
Products
RealmGuard
RealmGuard is a guardrails model that sits in the request path of AI agents and chatbots. It inspects prompts and streams responses inline with multi-turn context. Teams in regulated industries can use it to show that their assistants stay within the rules that apply to them.
| Coverage | “100+ hazard categories, 50+ languages, 4 modalities” |
|---|---|
| Detections | Content safety issues, prompt injections, jailbreaks, PII leaks, user frustration, hallucinations and custom failures |
| Agent behaviour | Manipulation, sycophancy, goal misalignment, tool abuse, privilege escalation and bias amplification |
| Explainability | Pinpoints the spans of text where problematic content appears |
| Compliance mapping | More than 30 regulations and frameworks, including FINRA, MLCommons, the EU AI Act, NIST and OWASP |
| Brand voice | Custom policies to keep answers on brand |
| Verdicts | Each request is routed to block, redact or flag |
Deep Neural Inspection
Deep Neural Inspection (DNI) is the runtime visibility layer behind RealmGuard. It produces one signal stream that drives continuous, real-time observability, inline enforcement and agent runtime control from the same data. Realm Labs reports that DNI surfaces more insight than LLM-as-a-judge approaches at lower cost and latency.
How It Works
RealmGuard is self-hosted and runs on any major cloud or on-premises GPU clusters, so no data leaves the customer’s environment. Set-up has four steps: integrate the endpoint, configure policies, wire it into the request path and route on its verdicts. It slots into standard serving stacks and can be deployed into an existing ML serving platform, with DNI compatible with Google Cloud, NVIDIA, Oracle, Docker, AWS and Azure.
| Component | What it does |
|---|---|
| Signal extraction | Proprietary methods pull interpretable signals from production-scale models during inference, across millions of daily interactions. |
| Policy grammar | A policy grammar turns those signals into enforcement actions. |
| Inspection surface | All signals feed one inspection surface for monitoring, enforcement and agent control. |
Company Background
| Founded | 2023, by AI safety and security researchers. |
|---|---|
| Headquarters | Sunnyvale, California, United States. |
| Leadership | Saurabh Shintre, founder and CEO. |
| Funding | $5 million led by Crosspoint Capital Partners (2026). |
| Recognition | Finalist in the RSAC 2026 Innovation Sandbox. |
Note: This overview is compiled from Realm Labs’s publicly available information as of 3 November 2026. It is provided for information only. NHI Mgmt Group does not endorse any vendor, and nothing on this page represents the opinions of NHI Mgmt Group.