Four patterns, four industries — illustrative scenarios

Flows your business can feel.

Each example takes a familiar workload — support tickets, fraud alerts, lifecycle emails, knowledge search — and maps it to a concrete FlowDSL flow: live diagrams, the exact modules wired together, and illustrative target metrics. These are reference architectures, not customer deployments.

30×
faster time-to-alert in fintech case
-90%
vendor cost on the e-commerce stack
71%
internal Qs resolved without a human
$0.08
avg cost per LLM-backed answer
SaaS / B2B Support

Cut first-response time to seconds without sacrificing CSAT

AI-deflected tier-1 tickets with a clean escalation path to humans.

Illustrative scenario · target numbers

POST /messagesin scopestreamout of scopeeventWeb widgetVue / React embed~180/hrIntent classifierllm-chat · qwen2.5Answer LLMgpt-4o-miniReply to userSSE streamHandoff routerpriority + routingOps inboxemail + Slack
42 min → 3 min68%
Read case
Fintech / Risk

From end-of-day fraud reports to real-time alerts

LLM-assisted anomaly triage on every transaction, with audit-grade cost attribution.

Illustrative scenario · target numbers

streamsuspectwindowpayloadverdictTransactionscard.authorized~42/sRules enginefast-path filterNightly scancron · 02:00 UTCEnrichMongo + historyLLM analyststructured verdictVerdict routeroutput.name == alert
22 h → 0.7 h-54%
Read case
E-commerce

Lifecycle emails without the marketing-ops headache

Event-driven onboarding, abandoned-cart, and win-back flows a non-engineer can edit.

Illustrative scenario · target numbers

eventeventeventStorefrontevent producerseventsuser.createdwelcome seriescart.abandoned1h / 24h / 72horder.placedthank-you + surveySegment resolvertier / regionTemplate renderMJML + locale
6 d → 0.5 d+38%
Read case
Internal Tools

A company assistant that actually knows the company

Retrieval-augmented flow over the internal wiki, tickets, and postgres — no dedicated RAG service.

Illustrative scenario · target numbers

questionveckeywordentitySlack / Webuser asksEmbed queryllm-embedWiki searchvector storeTickets searchmongoops/findSchema lookuppostgres/queryCompose contexttop-k merge
18 min → 0.8 min71%
Read case

Got a flow worth showing off?

These are the patterns Redelay is built for. Building something with a different shape — realtime bidding, multi-agent research, offline batch? We'd like to hear about it and document it.