How Daye North America Drove $1.5M in Sales Impact with NinjaCat AI Agents

Daye North America (DNA) manages a portfolio of outdoor power equipment brands across retail, eCommerce, direct-to-consumer, and dealer channels in the U.S. market. Their e-commerce team is small, experienced, and built to move fast. 

When the business needed to scale its analytical and strategic output without expanding headcount or agency budgets, DNA equipped their existing experts with NinjaCat's AI agent platform, and built an operation capable of scaling results that reflects the team's talent, not size.

The outcome: an 80% reduction in manual data analysis, 15+ hours saved per week on product page copy, 75% faster SEO content production, and $1.5M+ in measurable sales impact driven by inventory intelligence — with the selling season still underway.

The Challenge: Reacting to Data, Rather Than Responding

DNA's e-commerce team had no shortage of data. What they had was a synthesis problem.

Point-of-sale figures, inventory levels, geo-performance, paid media data, and customer feedback all lived in separate systems. Pulling those sources together required manual work, the kind built on multi-tab spreadsheets, entered by hand, that took days to compile and produced a picture already outdated by the time it was finished. 

With no dedicated analyst on the team, the work fell on e-commerce managers already responsible for strategy and execution.

"By the time you put it all together, it's two weeks later and things have moved," says Sandra Oono-Thomas, VP of eCommerce at Daye North America. "We were always responding, never reacting."

Operating two weeks behind in digital commerce is not a minor inconvenience — it means media spend might be allocated against conditions that no longer exist, inventory decisions could happen  after demand signals have already peaked, leaving Sandra and her team permanently managing consequences rather than setting strategy. 

With the pace of dataflows increasing for DNA, and the size of the team staying still, prioritizing the depth of real-time analysis on business intelligence opportunities was an area of concern.

Why NinjaCat: Stronger Visibility, Reliable Velocity

DNA's e-commerce team functions as the internal driver of AI adoption at the company — actively seeking out emerging technology and bringing it back to the business. They had been in conversation with NinjaCat for roughly a year before the timing aligned. After reconnecting at a conference in fall 2025, the team entered a 30-day proof of concept.

Two things distinguished NinjaCat from the start. 

First, the team at DNA was in the product immediately, no extended sales process, just hands-on access to explore what was possible. Second, Bob, NinjaCat's AI agent builder, gave DNA the ability to build and customize their own AI agents for marketing without relying on outside technical resources.

"NinjaCat makes that easy," Sandra says, describing the process of standing up agents configured specifically to DNA's brands, channels, integrations, and data sources. 

"If we were just to make an agent by ourselves, we wouldn't even know how to start. But because we have the tool through NinjaCat, we can."

Within the first 30 days of the proof of concept, the team had clear visibility into the potential of the platform; POS trends at the category and product level, geo-level inventory analysis surfacing problems before they became crises, and the ability to adjust paid media strategies, promotions, and assortment decisions in near real time. Full deployment across all agent groups followed directly from the proof of concept.

AI Agents: Orchestrating Toward Outcomes

DNA organizes their NinjaCat agents into five functional groups: eCommerce sales support, digital shelf, paid media, digital content/SEO, and UX/UI. Each team member works within their respective area — but the agent architecture is built for connection across those functions, not operation in isolation.

"My one agent is the digital strategist and her sub-agents are all of his," Sandra says, describing the layered structure her team built inside NinjaCat. 

"One of my co-workers will have a POS agent, but he'll have a geo agent that feeds into his main agent. So we are creating this network, both in between team members, and in NinjaCat."

Three to four team members are active builders inside the platform, each developing depth in different functional areas, with their individual agent networks wired together so the outputs from one agent feed directly into another. The capability to build and refine agents is distributed across the team, not concentrated at the top.

"I'm actually not the super user," Sandra says. "I would say there's two others on our team that are even better at using the agents. We lean on each other for support and ideas."

AI agents built and deployed inside NinjaCat:

The AI agents DNA has deployed within NinjaCat don't operate independently. Outputs from one feed directly into another. 

A geo-level view of inventory risk informs media activation decisions; weather signals connect to ROAS targets; customer review analysis flows into content production. 

"It's not just rain," Sandra says, describing the variables the agents can hold. "It's precipitation. It's drought. Location. Yard size. NinjaCat gives us the capability to analyze a lot of different factors." 

What previously took a week of manual work to compile — ad platform data, GA4, Shopify, geo POS trends, cross-brand performance — now runs in a couple of hours.

The reach of the agent network has extended beyond the e-commerce function. Outputs now flow to internal sales teams, product teams reviewing customer feedback, and retail partners who use the ground-level inventory visibility in their own buying decisions.

"We can output information that helps us with our content," says Sandra, of the voice of customer agent, "or we can output information to the product team about what people are saying about the product."

The Results

DNA's business is highly seasonal and acutely weather-dependent — demand for outdoor power equipment moves with precipitation levels, drought cycles, and regional growing conditions, not a predictable calendar. 

Before NinjaCat, out-of-stocks at the retail level were discovered after revenue was already lost. By connecting POS trends, weather signals, and geo data inside NinjaCat, Sandra's team shifted from a reactive inventory model to a proactive one — identifying where and when inventory was needed across distribution fulfillment centers weeks ahead of demand, and alerting buyers before gaps formed at the shelf.

The result from DNA’s use of AI to discover inventory intelligence illustrates what the NinjaCat platform enables when multiple agents are working in concert.

"For the first time, we could actually see what's going on," Sandra says, describing what the connected agent view made possible. "Otherwise, you're just getting a glimpse here and a glimpse there, but it’s a ton of effort to put it all together."

The $1.5M figure is a mid-season measurement. The selling season is not over.

What Made AI Agents Work

DNA did not hire AI specialists. They did not build a dedicated data science function or expand their agency roster. Their team already consisted of experienced professionals, people with deep brand, retail, and e-commerce expertise — and DNA made the choice to equip their people with an AI agent infrastructure capable of multiplying the effort and output.

Sandra is precise about the relationship between domain expertise and agent performance. 

"AI will amplify you," she says, referencing the principle her team built their approach around. "So if you're an expert in this, it will amplify this. But it won't amplify what you don't know."

AI agents surfaced patterns because Sandra's team knew which patterns to look for. 

The weather agent moved media spend because the team understood how precipitation and drought conditions translate into purchase behavior in the outdoor power equipment category. The inventory agent protected revenue because the team already knew where seasonal demand spikes created exposure. 

NinjaCat's platform gave the team the speed and scale to act on expertise they had spent years developing — expertise no platform can supply on its own.

"You have to know what you're looking for and what questions to ask that will actually benefit the business," Sandra says. "That comes from experience, expertise, all of those things."

For brands and agencies carrying the same operational weight DNA carried — more data than their team can synthesize, more channels than their reporting cycles can cover, more decisions than manual analysis allows — NinjaCat's agent platform is built for that problem, at scale. 

The entry point is a 30-day proof of concept. DNA's team had measurable results before the 30 days were up.

To learn more about how Sandra is leveraging AI agents with her team, listen to her episode on the NinjaCat podcast, "The Marketing Leader's Guide to AI Agents"

[ Book a demo to see what NinjaCat's AI agents can do for your team ]

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