Marketing ops · Production

Quid cuts analyst report cycles from six hours to under an hour using n8n for LLM-powered market intelligence orchestration

The problem

All technical build work flowed through Quid's core engineering team, creating time-zone bottlenecks that extended POC cycles and let customer requirements evolve before implementation caught up. Institutional knowledge of newly built capabilities stayed close to the teams that built them, limiting reuse across Quid's portfolio of specialised data systems.

Workflow diagram · grounded in source
1
Request via form or chat interface
trigger
“building forms and chat surfaces that allow colleagues across the business to trigger complex workflows without needing to engage with the underlying implementation”
2
Query triage classification
ai_action
“Query creation was decomposed into a triage step that classifies whether the user is building a query about a person, category, brand, or campaign, and routes to specialised logic”
3
Route to specialized query logic
routing
“routes to specialised logic. That triage block is now used across multiple products, including an interface where users continue to construct Boolean queries manually”
4
LLM entity identification
ai_action
“Quid needed to identify highly specific entities in social media conversation, not generic concepts like running shoes, but specific shoe models, named athletes, emotional descriptors, and environments. An LLM alone is not reliable enoug…”
5
Integrate data from internal and external systems
integration
“integrates with Quid's internal orchestration engine for heavy batch analytics, Quid's external metrics and soundbite APIs, Google APIs, AWS, Alicloud, various public APIs”
6
Analyst report delivered
output
“individual reports that used to take around six hours now typically take 30 to 60 minutes”
Reported outcome

n8n orchestration cut individual analyst report cycles from around six hours to under an hour, freed capacity previously consumed by a week-long Commerce Factory reporting process, and enabled Quid to launch a daily reporting product tier that was not viable under a manual delivery model.
Over 2,000 analyst hours were saved across 473,000+ workflow executions in the past year, and POC turnaround compressed from months to a single day.

Reported metrics
Workflow executions in past year473,000+
Analyst hours saved2,000+
Analyst report cycle timereduced from around six hours to under an hour
Individual report turnaround30 to 60 minutes
Show all 8 reported metrics
workflow executions in past year473,000+
analyst hours saved2,000+
analyst report cycle timereduced from around six hours to under an hour
individual report turnaround30 to 60 minutes
POC turnaround timecompressed from months to a single day
Commerce Factory reporting timeruns in a few hours across three days (previously most of a working week for two analysts)
entity identification block reusepowers five agents across three clients
integration test throughputroughly doubling his throughput
Reported stack
n8nClaudeOpenAIOpenRouterMCPGoogle APIsAWSAlicloud
Source
https://n8n.io/case-studies/quid/
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

n8n orchestration cut individual analyst report cycles from around six hours to under an hour, freed capacity previously consumed by a week-long Commerce Factory reporting process, and enabled Quid to launch a daily r…

What tools did this team use?

n8n, Claude, OpenAI, OpenRouter, MCP, Google APIs, AWS, Alicloud.

What results were reported?

Workflow executions in past year: 473,000+; Analyst hours saved: 2,000+; Analyst report cycle time: reduced from around six hours to under an hour; Individual report turnaround: 30 to 60 minutes (source-reported, not independently verified).

How is this marketing ops AI workflow structured?

Request via form or chat interface → Query triage classification → Route to specialized query logic → LLM entity identification → Integrate data from internal and external systems → Analyst report delivered.

Related marketing ops cases