How Quid connected engineering and the business with n8n to deliver faster and ship new offerings

Quid is a consumer and market intelligence platform that helps businesses understand their industry, whether it's what people think, feel, and say about brands, products, and categories or anticipating market shifts by identifying M&A, investment and patent filing trends. The company has a global presence, with teams based throughout North America, Europe, Asia and Australia. Quid operates a portfolio of specialised data systems, each optimised for a particular part of the consumer and market intelligence pipeline. Rather than consolidating these systems at the storage layer, Quid uses n8n as an orchestration layer that composes capabilities across them and delivers a unified result at the point of customer engagement.

With n8n, Quid achieved:

• 473,000+ workflow executions across build and production instances in the past year
• 2,000+ analyst hours saved over the same period
• Analyst report cycles reduced from around six hours to under an hour, freeing capacity for interpretive and client-facing work
• New daily reporting product tier launched, previously not viable under a fully manual delivery model
• POC turnaround compressed from months to a single day
• Engineering building blocks reused across agents and serving multiple customers

The challenge

Historically, all technical build work at Quid flowed through the core engineering team. With customer-facing teams operating across Asian, European and US time zones, that single channel introduced natural sequencing constraints on how quickly the business could respond to live customer signals. A meeting in the afternoon in the US might wait until the next working day to translate into engineering feedback, which extended POC cycles and meant that customer requirements could evolve before initial implementation work had completed.

There was a second, structural challenge. When engineering shipped a new capability, the institutional knowledge of what it did and where else it could be applied tended to stay close to the team that built it. Capabilities with potential application across multiple products were often consumed by a single one. Underlying both of these was the reality that Quid runs a portfolio of specialised data systems, with different parts of the platform optimised for different workloads. Consolidating these at the storage layer would have been a substantial multi-year programme. The team chose instead to invest in an orchestration approach that composes capabilities across systems at the point of customer delivery.

Quid's experimentation with no-code tooling started in mid-2024. Engineering's initial position was to validate that a visual workflow tool would deliver real leverage rather than duplicate work already being done well in code. That was the right bar. Adoption accelerated once engineering saw their own building blocks being reused by other teams without additional engineering investment.

The solution

Quid runs n8n self-hosted across four instances, with separate environments for build and production. The platform 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, and multiple LLM providers including Claude, OpenAI, and OpenRouter.

The decision to keep LLM access flexible was deliberate. "We don't want to be tied into any one LLM provider," Arianna said. "We want to be able to switch between our in-house models, Claude, ChatGPT, models available via OpenRouter, and even bring in emerging models like large Indian LLMs." Routing every model call through n8n means Quid can test new providers without going through full procurement and security review for each one.

n8n is used in three ways.

• The first is operational automation: refreshing database views, scheduling client emails, and similar recurring workflows.
• The second is interface work, building forms and chat surfaces that allow colleagues across the business to trigger complex workflows without needing to engage with the underlying implementation.
• The third, and the most strategically significant, is LLM-powered agent work for report building, data analysis, and data collection.

The methodology that unlocked this was a shift toward composable building blocks. Rather than shipping single-purpose features, engineering exposes modular capabilities that the applied team and other groups can recombine into customer-facing workflows. One example: 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 enough for this level of specificity. Engineering originally built the capability for the trends product, then exposed it as a composable block through n8n. It now powers five agents across three clients.

The same pattern shows up in query construction, which sits at the core of how Quid curates data sets for analysis. 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. That triage block is now used across multiple products, including an interface where users continue to construct Boolean queries manually.

Quid has also pulled MCP into the stack. David uses Claude connected to n8n's MCP to write integration tests and run evaluations in the background, roughly doubling his throughput on that work. More notably, Quid prototyped its own MCP server entirely through n8n. "All of that initial testing, we did entirely through n8n," Arianna said. "It helped us answer questions we wouldn't have realised we had to answer otherwise."

The outcome

The Commerce Factory reporting process previously occupied two analysts for most of a working week. It now runs in a few hours across three days, with the freed capacity redirected to interpretive and client-facing work. "That has been a significant gain, and we are continuing to find further efficiencies" David said.

Across the broader analyst team, individual reports that used to take around six hours now typically take 30 to 60 minutes. Quid has launched a daily reporting product tier in the past year, serving customers for whom this cadence would not have been commercially viable under the previous delivery model. "Daily reporting at this price point was not feasible under our previous delivery model. The orchestration layer lets us offer it as a productised tier." Arianna said.

The POC timeline compressed from weeks to a day, sometimes faster. Arianna described building a working agent prototype in 30 minutes after a leadership request. "I built it with Claude and n8n, put it on the screen, and asked: is this actually what you need?" That first version forced the people who'd asked for it to articulate what they actually wanted. Some of the original asks evaporated. New ones came up. After another week and a half of iteration she had a version with a well-defined enough scope to hand to engineering for proper productionisation.

Because n8n lowers the cost of prototyping, Quid has at times deliberately time-boxed multiple teams against the same problem in parallel. On one query-building challenge, four teams worked independently, after which the team composed the strongest elements of each prototype into a shipped solution. The combined effort was modest because each prototype was small, and the final design was stronger than any single team would have produced alone. "Because n8n lowers the cost of prototyping, we can deliberately put multiple teams on the same problem, time-box them, and then combine the strongest parts of each approach." Arianna said. Across the past year, Quid logged close to half a million n8n workflow executions across its environments, spanning experimentation and production-grade customer-facing workflows. The team estimates around 2,000 analyst hours saved.

The hardest result to measure is one Arianna highlighted first: technical literacy has spread across the company, so more colleagues outside of engineering can specify what they need with enough precision for it to be built quickly. "I have so many more people who understand how things work together," she said. "They might not be able to build, but now they can tell me what they need me to build."

That literacy is what shapes the roadmap. Quid plans to formalise the MCP layer, deepen the round-trip between Claude and n8n for engineering tasks, and push further into customer-facing products powered by n8n in the background. "We want a lot more reusability across teams," Arianna said. "That's where n8n keeps unlocking value"