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Using Quantlix without an ML background

Quantlix is a control layer around AI products: safety rules, document lookup, execution timelines, and automated quality checks. You can be a strong application developer and still use it effectively—the product labels advanced terms like evals and retrieval beside plain English in the UI.

Choose your quickstart path

Try in 2 minutes

Use the fast first run to ask a test question with demo defaults and see how enforcement appears in the portal.

Deploy in 15 minutes

Connect a provider, create a deployment, add a policy, run one request, then inspect the trace and enforcement events.

Self-host in your VPC

Run the API, portal, orchestrator, database, and supporting services in your own environment when data boundaries require it.

Create your first workflow

Start with input → MCP → redact_text → model → output, or use retrieval → answer_with_citations for evidence-backed responses. Keep privacy steps before model inference.

What “quality checks” (evals) are

Saved test questions and scoring that tell you if answers got worse after you changed a prompt, model, or document set—similar to unit tests for software, applied to AI outputs.

What “knowledge lookup” (retrieval) is

Searching your own uploaded or synced documents to ground answers, often with source references (citations). It is how “chat over my PDFs” works without training a custom model.

What templates are for

Pre-built outcomes—safer chat, document Q&A, approval workflows—so you start from something that already runs instead of wiring every step from scratch.

When Quantlix is worth it

Helpful when you have real users or production traffic, need visibility into what the model did, or must enforce safety and budgets consistently. For a one-off throwaway experiment with no risk, it may be early—that is normal; the same product is there when your app grows.

Common first decisions

  • Use provider integrations when you want Quantlix to govern Anthropic, OpenAI, Azure OpenAI, Bedrock, Voyage AI, or similar model calls.
  • Use workflows when a request needs data fetching, redaction, retrieval, approvals, or tool calls before the model.
  • Use workflow examples when you want copyable node chains for common use cases.
  • Use policies when you need consistent block, warn, budget, or audit behavior across requests.
  • Use security and compliance when you need audit exports, MFA, SSO/OIDC, GDPR rights, or signed evidence.