The governance runtime for agentic AI.
Intercept policy violations. Inject missing context.
Recover — without human intervention.
Unlike observability tools that log failures after the fact, Neri corrects them — autonomously, in real time.
On every interaction. Preventing agents from making expensive, embarrassing, or non-compliant decisions.
Prevent agents from processing unauthorized refunds, promising discounts, or agreeing to credits beyond your threshold.
Enforce tone guidelines. Block competitor mentions, hostile language, or off-brand commitments before they reach customers.
Ensure agents never provide unverified financial advice, medical diagnoses, or promises outside legal boundaries.
| Capability | LangSmith / Arize | Guardrails AI | ● NeriLabs |
|---|---|---|---|
Observability & logging Track what agents do | ✓ | — | ✓ |
Input / output filtering Block bad responses | — | ✓ | ✓ |
Business logic enforcement PM-defined, deterministic rules | — | Partial | ✓ |
Autonomous recovery Re-ground agent, retry correctly | — | — | ✓ |
Private on-prem deployment Your cloud, your perimeter | — | Partial | ✓ |
Zero codebase changes One line, drop-in proxy | — | — | ✓ |
Join the teams using Neri to move from pilot to production. One proxy. Autonomous recovery. Zero rewrites.
Neri is an independent proxy layer — not a wrapper, not an SDK patch. It intercepts every LLM call, enforces your policies deterministically, and autonomously recovers when something goes wrong.
No probabilistic guardrails. No prompt hacks. Rules are evaluated as code — pass or fail — against every request and response. Your PM writes the policy, Neri makes it law.
When a violation is detected, Neri doesn't just stop and return an error. It surfaces the missing business context — the policy, the constraint, the correct procedure — and feeds it back to the LLM so the agent can complete the task correctly.
Neri is OpenAI API-compatible. Point your existing client at the Neri proxy URL and you're done. Works with any LLM provider that exposes an OpenAI-compatible interface — GPT-4, Claude, Llama, Mistral.
Neri adds ~5ms of latency at p99. That's the cost of a DNS lookup. Most guardrail solutions add 80–200ms because they make additional LLM calls to evaluate safety. Neri evaluates policies in-process, deterministically.
Deploy the proxy in minutes. We'll walk you through writing your first policies and connecting your existing agent.
Every industry deploying agents has the same problem — the agent works in demos and fails in production when it hits a real edge case. Here's where Neri makes the difference.
Health tech companies deploying AI agents for appointment scheduling, coverage questions, and symptom triage walk a compliance tightrope. One misread question answered as medical advice is a liability event. Neri enforces the boundary between helpful and clinical — deterministically, on every message.
Startups replacing traditional Chief of Staff functions with AI agents give those agents significant authority — calendar control, email drafting, stakeholder communication, prioritization. The risk isn't the agent being unhelpful. It's the agent being confidently wrong in a high-stakes context. Neri becomes the judgment layer the exec actually trusts.
Customer support agents in financial products handle refunds, disputes, and account changes at scale. Without governance, a well-crafted prompt from a bad actor — or simply a misread context — can trigger unauthorized financial actions. Neri enforces transaction limits, escalation thresholds, and regulatory constraints on every call.
Law firms and compliance teams using AI to handle document review, employee policy questions, or contract summarization face the same hard limit: the agent cannot practice law. It can inform, it can summarize, it cannot advise. Neri draws that line in code — on every response, every time.
SaaS companies using AI agents for churn prevention, renewal conversations, and upsell motions give those agents authority to offer discounts and extensions. Without governance, agents routinely over-discount, commit to features not on the roadmap, or bad-mouth competitors — all invisible to the rep.
Neri is policy-driven. If you can describe your business rule in logic, Neri can enforce it — across any agent, any LLM, any use case.
Neri runs as a Docker container in your cloud. We have zero access to your data, your prompts, or your responses. Your security team doesn't need to review our data handling — because we don't handle your data.
Designed from the ground up for organizations where data control isn't negotiable.
Neri ships as a Docker container. It runs inside your cloud, your data center, or your VPC. No SaaS. No shared infrastructure. We install, you own.
All policy evaluation happens in-process, inside your network. No request or response payloads are sent to NeriLabs. Your prompts never leave your perimeter.
Define policies once. Apply them across every agent, every team, every environment from a single policy registry your platform team controls.
Every intercept, every recovery, every policy match is logged to your own storage. You own the audit trail. Query it, ship it to your SIEM, or export it.
Run governance across dozens of agents from different teams with isolated policy sets. Sales agents, support agents, internal ops agents — each with their own rulebook.
We help you write your first policies, tune the semantic engine for your domain, and integrate with your existing observability stack. Hands-on, not self-serve.
We cannot see your prompts, your responses, or your users. The proxy runs entirely in your environment. This isn't a policy — it's an architecture.
Change the base URL on your OpenAI client. That's it. No new SDK, no agent refactor, no model fine-tuning. Works with your stack as it stands today.
Governance shouldn't slow down your product. Neri evaluates policies in-process with no secondary LLM calls. Deterministic checks, not probabilistic ones.
OpenAI, Anthropic, Azure OpenAI, Llama, Mistral — any provider with an OpenAI-compatible API works with Neri. Swap models without touching governance.
Enterprise deployments are handled directly. We'll scope your infrastructure, write your first policy set, and have you running in production within a week.
NeriLabs was started by a team that spent years watching enterprises pilot AI agents and pull back — not because the models weren't good enough, but because there was no infrastructure to make them trustworthy at scale. We're building that infrastructure.
Every team building agents ran into the same wall. The demo works. The model is smart. Production is terrifying.
We're working closely with our first customers to shape the product. If you're deploying AI agents and reliability is keeping you up at night, we want to talk.