AI Ops Stack · LLM Applications

Production AI,
without the
blind spots.

FluiqAI is the unified ops layer for LLM applications: security scanning, intelligent caching, deep observability, and automated evaluation on every request.

Observability tools tell you what broke.
Fluiq helps you prevent it.

Most platforms stop at tracing. Fluiq adds a security layer, a caching layer, and a quality gate, so you catch problems before your users do.

Full trace visibility across every LLM call

Every token, latency, and cost attributed to the exact agent node that spent it. Streaming traces, cost anomaly alerts, and per-model breakdowns, without changing how you write code.

• Per-node token attribution
• p50 / p95 / p99 latency tracking
• Real-time trace streaming

fluiq.instrument(api_key="fl_...")

Block attacks before they reach your model

Pre-call scanning catches jailbreaks, prompt injections, and skeleton-key attacks before the LLM call is made. Post-call scanning redacts PII and secrets from stored traces.

• Pre-call jailbreak + injection blocking
• PII & secret redaction on traces
• No false positives, fails open on errors

fluiq.secure(mode="block")

Gate responses that fail quality thresholds

LLM-as-judge runs server-side after each call. Set per-metric thresholds. Warn mode logs quality scores to the dashboard; block mode raises FluiqEvalError before the response reaches your app.

• hallucination, faithfulness, relevance, toxicity
• Scores stored and visible in the dashboard
• Block mode prevents bad responses reaching users

fluiq.eval(thresholds={'hallucination': 0.8})

Write, version, and deploy prompts like software

A dedicated IDE-style editor for your prompt templates, with {{variable}} injection, full version history, and per-environment deployment. Iterate directly on real production traces, compare model outputs side-by-side, and ship with confidence.

• {{variable}} template syntax: define slots, fill at runtime via SDK
• Version history: save, browse, and restore any past version instantly
• One-click deployment to dev, staging, and production environments
• Side-by-side model comparison with the same prompt across models
• Pull directly from live traces and iterate on real-world prompts

fluiq.get_prompt("customer-support", env="production")

How it works

Four functions. Production-ready in minutes.

Framework-agnostic

Works with the stack you already use.

Fluiq patches at the function-call level, not the framework level. Any Python function that hits an LLM or vector database becomes a traced span with one decorator.

OpenAIAnthropicGoogle GeminiLangChainLangGraphCrewAIPineconeChromaWeaviateFAISSGoogle ADKQdrant

Free up to 50K traces a month.

Start with observability on the free tier. Add security, optimization, and evaluation as your pipeline grows. No code changes required.

No credit card required. pip install fluiq, instrument in 60 seconds.