Enterprise AI infrastructure

AI infrastructure for teams that ship.

We build the runtime, evaluation and gateway layer that lets teams put large language models into production without rebuilding their stack — routing, guardrails, evals and cost control in one place.

Talk to us See the platform
12B+
Tokens routed / mo
38ms
Median gateway latency
99.98%
Control plane uptime
2
Founding team, LA
01

The layer between your app and the model

Three pieces. Each one solves a problem that shows up the moment you move past a prototype.

Nexus Runtime

Multi-provider model orchestration with deterministic fallbacks. Failover, retries and prompt versioning handled below your application code.

runtime.route(model, policy)

Nexus Eval

Continuous evaluation on your own traffic. Regression gates in CI, drift alerts, and per-release scorecards instead of vibes.

eval.gate(release, threshold)

Nexus Gateway

One endpoint, one bill, per-team budgets. Unified auth, rate limits, PII redaction and audit logs across every provider you use.

gateway.enforce(tenant)
02

From prototype to production

Teams usually land here after their second model migration. We shorten that path.

  1. Point your SDK at the gateway Swap a base URL. Existing OpenAI-compatible calls keep working; new providers become one config line.
  2. Define routing policy Declare intent — cost ceiling, latency target, capability — and let the runtime choose the model per request.
  3. Wire evals into CI Every prompt or model change runs against recorded traffic before it reaches users.
  4. Watch cost and quality together Spend per tenant, per feature, per release — next to eval scores, in the same dashboard.
nexus.config.ts
// routing + guardrails, one file
import { nexus } from "@nexus/runtime";

export default nexus({
  routes: [{
    when: { task: "support-reply" },
    prefer: "fast",
    maxCostPer1k: 0.9,
    fallback: ["gpt-class", "claude-class"]
  }],
  guard: {
    redact: ["pii", "secrets"],
    blockOnEvalDrop: 0.04
  },
  telemetry: { tenant: true, audit: true }
});
03

Small team, narrow focus

Nexus AI Labs is a two-person founding team based in Los Angeles. We spent years shipping ML systems inside companies where a failed model migration meant a failed quarter. The tooling we wanted didn't exist, so we built the boring, reliable version of it — routing, evals, and governance that hold up under real traffic.

CL

Chang Lin

Founder & CEO · MS Computer Science, Stanford
Previously ML infrastructure. Building Nexus since 2021.

04

Working on something that needs this?

hello@nexus.002836.xyz

We take on a small number of design partners each quarter. Tell us what you're building and where it's breaking.