Overview

What is Quantlix?

Quantlix is the AI Runtime Control Plane for production AI systems. It sits in the request path between your application and AI models, tools, and knowledge sources to enforce policies, capture traces, run evals, and control runtime behavior.

The short version

Your app sends an AI request. Quantlix sits in the request path: it checks whether that request is allowed, whether it matches the expected shape, whether sensitive data should be removed, whether the budget permits it, and which model or provider should receive it. The result is traceable, evaluable, and exportable.

Four pillars

Every Quantlix capability falls under one of four runtime pillars: Deploy, Control, Observe, and Evaluate.

Deploy

Ship AI workflows, models, retrieval, and tools as production-grade deployments — without rebuilding orchestration for each new use case.

Control

Enforce policies, budgets, and guardrails in the request path before the model is called. Outcomes are explicit: allow, block, redact, or budget-gate.

Observe

Capture every request, model call, tool call, policy decision, and eval result in one trace. Audit-ready exports come standard.

Evaluate

Run evals as quality gates before changes reach production. Compare versions, catch regressions, and ship with evidence.

Core jobs

  • Enforce contracts so model inputs and outputs stay predictable.
  • Apply policies such as block, allow with warning, redact, or budget gate.
  • Route model calls through provider-backed deployments and record the runtime request path.
  • Run workflows that fetch context, redact data, call tools, and invoke models.
  • Record traces, enforcement events, redaction summaries, and audit evidence.

How Quantlix is different

Observability tools

Observability tools tell you what happened after a request ran. Quantlix sits in the request path and can stop unsafe, off-contract, or over-budget requests before they run — observability is one capability inside the control plane, not the whole product.

API gateways

API gateways route and protect network traffic. Quantlix understands AI-specific contracts, model payloads, policy decisions, traces, and provider inference targets — and routes both the data plane and the control surface.

Eval frameworks

Eval frameworks score output quality offline. Quantlix runs evals as quality gates inside the runtime control plane: tied to deployments, policies, and traces, not a separate scoring tool.

Agent frameworks

Agent frameworks help build multi-step AI behavior. Quantlix controls those behaviors at runtime — workflow execution, policy enforcement, redaction, traces, and audits — so what shipped is also what's enforced.

Common questions

Does Quantlix replace LangChain or agent frameworks?

No. Quantlix is the AI Runtime Control Plane around AI systems. You can still build with LangChain, custom agents, MCP tools, or direct provider APIs, then use Quantlix to enforce policies, capture traces, run evals, and control runtime behavior.

What problem does Quantlix solve first?

Runtime control. It sits in the request path so teams can decide what AI is allowed to do before it executes — validate the payload, apply policy, redact sensitive data, check budgets, call the provider, and record what happened.

Who is it for?

Application teams shipping AI features, platform teams standardizing provider access, and compliance owners who need evidence for what reached a model provider.