Customer Story
One of Canada's largest integrated distributors of plumbing, HVAC, and waterworks products for the construction industry
600k+
SKUs
2,000+
Suppliers
300+
Locations
As customer expectations have shifted towards self-service, speed, and accuracy, our customer has invested in digital infrastructure and experiences. But they found themselves constrained by a problem that nearly every large technical distributor eventually hits: product data had become a bottleneck for growth.
They invested in improving product data through a variety of strategies: supplier engagement, data syndication, data brokers, software, and more. They paired these strategies with a content team that is experienced, thoughtful, and deeply familiar with their categories, but progress remained slow.
"A large portion of content comes from manual effort"
— Senior Product Analyst
The costs were not abstract, as catalog enrichment relied on spreadsheets, manual resources, and outsourced web scraping.
Poor product content doesn't just affect e-commerce; it limited their ability to enable their counter sales and showroom teams, inside sales, quoting, and more.
"The ecommerce system is downstream from our PIM, but the PIM also feeds other systems. All of that depends on enriched data. Product data has really become the lifeblood of the company."
— IT Consultant
As they evaluated their options, one realization became unavoidable: product content was not a data acquisition problem. It was a human workflow problem.
Each product requires interpretation. Teammates had to research the product, determine which attributes mattered, normalize values, and resolve ambiguity. Repeating that work across hundreds of thousands of SKUs, suppliers, and product categories made the process deeply time-consuming and difficult to scale through manual effort alone.
The core challenge was scale. They needed to dramatically increase the amount and quality of product content they produced, without simply hiring a much larger team or forcing suppliers to conform to yet another rigid data standard.
"A lot of this has been done by hand with best effort. We need something that can understand which attributes matter for which products, and do that at scale."
— IT Consultant
To make this workflow viable long-term, they identified a set of concrete requirements:
Taken together, these requirements ruled out incremental fixes. Hiring more analysts, relying more heavily on suppliers, or purchasing another static data source would not change the underlying constraint.
What they needed was a fundamentally different approach to product content, one that could scale human judgment without scaling human effort.
Rather than treating product content as something to outsource or "turn on," we worked together to design a workflow that reflected how their catalog actually functioned, and how it needed to scale.
The collaboration focused on turning messy, fragmented inputs into trusted product understanding, without disrupting the customer's existing systems or forcing suppliers to change behavior.
The customer brought deep domain knowledge about how their products were documented in the real world. Kaavio brought tooling to work with that reality at scale.
Together, they accepted a critical constraint: for many technical products, the most accurate source of truth is still a PDF spec sheet, not a clean data feed.
The workflow intentionally combined:
Rather than declaring a single "golden source," the system evaluated all sources together, allowing facts to emerge through synthesis.
A key decision was to start from the customer's catalog as it existed, rather than asking the team to define rules.
With access to the full catalog, Kaavio's system could:
"With the full catalog, we can use the customer's own data, which massively improves consistency and data quality."
— Stephen Perkins, Lead Engineer at Kaavio
This made their past effort an asset.
Rather than handing off raw output, the teams designed a review-and-approval loop that fit the customer's operational reality.
This ensured:
While the initiative is still in early stages, the customer team has already seen clear, qualitative shifts in how product content work gets done, and what is now possible at scale.
Before Kaavio, enrichment work required analysts to manually locate, interpret, and enter product information, often repeating research that already existed somewhere else.
In early reviews of Kaavio-generated output, the customer team saw a dramatic change in how much work was required before human review even began.
"When we were reviewing the sheets...the amount of data we didn't have to go and get, and that was clean, correct and in the sheet — astronomical. So have you saved us time? Yes. You've moved us over into a quality check position instead of a doing position."
— Senior Product Analyst
This marked a fundamental shift in the role of the customer's product data team:
Another early indicator came from data accuracy and edge cases. As they pushed the system with difficult products and incomplete inputs, the team saw fewer breakdowns than expected.
"We are trying very hard to stump you, and we have not yet been able to do that."
— Product & Pricing Department Manager
This reinforced a key insight from the collaboration: using the customer's existing catalog as context significantly improved consistency and reduced rework, even across complex and heterogeneous categories.
Although e-commerce was the initial driver, early results confirmed that improvements to product content flow through the organization.
Because enriched data feeds the customer's PIM and downstream systems, the team now sees a clear path toward:
As they plan for future growth, product data is no longer viewed as the primary constraint. It has become a strategic asset, supported by a workflow that can evolve over time.