Workflows
Multi-step pipelines with tools, branching, retries, and human handoffs—built for how real products behave.
Production AI systems platform
Deploy, observe, evaluate, and govern AI systems.
Quantlix helps teams build production AI systems with workflows, retrieval, evals, guardrails, and observability in one platform.
Free tierStarter €9/moGrowth €19/moPlans
Platform preview
Pick a surface to explore—same product, your pace. No auto-rotation.
Workflow
Visual pipeline with policy gates
Vertical flow
Input
ReadyRetrieval
OptionalGuards
MatchedApproval
WaitingOutput
QueuedGuards & approval enforced at runtime
Canvas5 steps
Prefer exploring first? Live demoDocsHow it works
Everything you need to run AI in production
Quantlix is a platform for building and operating production AI systems—not only a runtime enforcement layer. Design workflows, ship with confidence, and govern as you scale.
Retrieval
Connect knowledge bases and ship answers grounded in your sources, with visibility into what was retrieved.
Evals
Regression-test prompts and workflows, compare versions, and ship changes with evidence.
Observability
Traces, citations, and run history so you can debug production behavior without guesswork.
Policies
Guardrails and approvals that apply consistently—not one-off checks in application code.
Budget control
Cap cost and usage before it surprises finance; enforce limits at the platform boundary.
Platform fit
Where Quantlix fits in your AI stack
Instead of stitching together custom guardrails, ad-hoc validation scripts, separate observability products, one-off eval harnesses, and bespoke RAG trace logic, Quantlix provides a single runtime layer that enforces policy, records traces, and scores quality on every run.
- One boundary for contracts, policies, and budgets—before tokens spend.
- Observability and evals tied to the same run IDs you use in production.
- Workflows and retrieval stay first-class—not bolted on after the fact.
Request path
Your app or API
Traffic you control
Workflow / agent
Optional orchestration layer
Quantlix runtime
Policy · retrieval · trace · eval
Model provider
OpenAI, Anthropic, open weights, …
Quantlix is the control and verification layer: what was allowed, what was retrieved, what the model returned, and how it scored—without wiring four different tools by hand.
Explore first
See the product before you sign up
Walk through live enforcement examples, read how runs are traced, and compare that to your current stack—no account required.
~2 minutes
Quick path through the product
No video required—expand each step. This is the same story as the live examples: control plus verification, not just inference.
Ship a deployment with a contract and policy pack—enforcement is on from the first request.
See a governed request in action: step-by-step demo · Create an account
START FASTER
Start from proven templates
Pick a pattern that matches your product outcome, then tailor workflows, retrieval, evals, and governance in one place.
Core paths (chatbot, RAG, regression evals) are available without Enterprise. Human approval queues are marked below when they require an Enterprise-tier capability.
Safe chatbot
Customer-facing chat with guardrails, policy checks, and full traces—so support and compliance stay aligned.
Explore template →RAG assistant
Ground answers in your docs and tools. Citations and retrieval visibility built in—not bolted on afterward.
Explore template →Human approval workflow
EnterpriseRoute high-risk outputs to reviewers, capture decisions, and keep an audit trail for regulated workflows.
Explore template →Regression-tested prompt app
Treat prompts and workflow versions like code: run eval suites, compare releases, and catch regressions before launch.
Explore template →WORKFLOW BUILDER
Multi-step pipelines, not one-off prompts
Production AI rarely stops at a single model call. Quantlix workflows chain retrieval, generation, tools, and approvals into explicit pipelines—so behavior is repeatable, testable, and observable end to end.
- Branching, retries, and fallbacks when steps fail
- Retrieval and reranking as first-class steps
- Human-in-the-loop gates where risk requires a person
OBSERVABILITY
Observability that answers “what happened?”
Trace every run with step-level visibility, latency and cost metadata, citations, and policy outcomes—so production debugging happens from one console, not scattered logs.
run_8fa21b · deployment/prod
Trace console
Succeeded
TraceCitationsPolicy
Duration
1.24s
Cost
$0.0041
Tokens
1,482
Citations
3
Trace timeline
Retrieve
3 chunks · 42ms
Generateactive
gpt-4o-mini · 1.2s
Policy check
allowed · pii-safe
Respond
complete · cited
Citations & evidence
[1] Internal runbook §4.2
Rollback sequence for failed deploy steps...
[2] Security policy v3
Data residency and PII handling requirements...
Grounded answer: 3 supporting citations matched
EVALS
Evals that mirror how you ship
Run golden datasets against prompts and workflows, compare versions side by side, and treat regressions like failed tests—before users see them in production.
- Side-by-side runs when you change a prompt or workflow
- Pass/fail signals on quality and grounding—not just averages
- Drill into failing examples with traces and citations
suite/regression-v3
compare modeWorkflow v1.3
92%pass rate
baseline+1.1%
Baseline stable across safety checks
groundednesscitationsafety
Workflow v1.4
94%pass rate
improved+2.0%
Improved retrieval recall and groundedness
groundednesscitationsafety
Workflow v1.5
88%pass rate
regressed-6.0%
Citation grounding regressed in 6 examples
groundednesscitationsafety
Regression detected in citation grounding
6 failing examples compared with v1.4 (citation precision -8.2%).
test → compare → improve → ship safely
One control plane between your product and models
Ship features on top of Quantlix—workflows, retrieval, evals, and governance stay consistent no matter which models you use underneath.
Your application
Product & APIs
Chat, copilots, internal tools—what customers actually use.
QUANTLIX
Production AI platform
- Workflows & retrieval
- Traces & evals
- Policies & budgets
- Unified observability
control-plane: routes requests, enforces policy, emits traces
Model providers
LLMs & embeddings
Swap or A/B test models without rewriting your product surface.
HOW IT WORKS
From idea to governed production runs
Go from prototype to reliable operations with a workflow-first path that keeps quality, cost, and governance visible at each step.
Design your system
Model multi-step flows—retrieval, generation, tools, and approvals—in one builder instead of gluing scripts together.
Run with guardrails
Ship to production with policies, budgets, and tracing on by default. Know what shipped and what was blocked.
Prove quality over time
Regression-test prompts and workflows, compare versions, and catch drift before customers do.
WHY QUANTLIX
Built for outcomes, not integration glue
Most teams stitch deployment, observability, and governance by hand. Quantlix unifies those layers so teams can ship faster with confidence.
Without a unified platform
- Separate tools for deployment, evals, and logging
- Policies implemented ad hoc in app code
- Hard to answer what happened for a single user-facing run
With Quantlix
- One place to design workflows and ship with traces and evals
- Policies and budgets enforced consistently at the platform layer
- Product teams see outcomes—quality, safety, cost—not only raw requests
GOVERNANCE
Policies and budgets that actually stick
Define guardrails once so unsafe or expensive requests are blocked before they reach production models.
- Block bad requests early. Invalid payloads and policy violations fail fast with a clear reason teams can act on.
- Stay inside budget. Cap usage and cost at the boundary so surprises show up in product telemetry, not invoices.
- Audit without archaeology. Decisions are logged with runs and workflows, not scattered across ad hoc logs.
blocked
policy/pii-check · before_model_call
Request blocked
The customer message contained disallowed personal data. Nothing was sent to the model; the run is recorded for review.
Logged to traceNo model spendApproval queue untouched
GET STARTED
Build production AI systems with confidence
Workflows, retrieval, evals, observability, policies, and budgets—together.
New here? Step through deploy → policy → trace → eval · Interactive example