The AI-Native Blueprint: Stacker's Adoption of elvex

A Content Distribution Platform Running on Data

Stacker operates as a content distribution platform connecting brand journalism to local publishers. Think AP or Reuters, but sourcing from brands who invest in quality, non-promotional content. When a brand produces a story, Stacker vets it, repackages it, and distributes it across a network of over 3,000 publishers in the US and Canada. Typical distribution results in about 150 republications, generating hundreds of high-authority backlinks that are valuable to brands seeking uplift in SEO or GEO visibility.

The business runs on data: which stories perform, which publishers pick them up, how many pageviews each generates, and what content trends are emerging. Getting that data into the hands of the people who need it has always been the challenge. A critical part of Stacker’s value proposition is that they can tell brands what types of stories will perform—and that their recommendations are data backed.

Using elvex to go from "We should do something with AI" to a fully shipped AI-powered product

Brands constantly ask Stacker what types of stories they should write. Previously, this required Stacker's content strategists to manually analyze network trends and client goals, then make recommendations. The process was valuable but slow, and it did not fit into the workflow brands actually wanted.

Stacker believed that if they could find a platform that would allow their team to rapidly prototype agents connected to their data, they could find the workflow that proved valuable to their clients, narrowing the time required to produce insights, and then iterate that into their own product.

This is why Stacker chose elvex:

1. Data and integration connectivity. They could connect every agent to Stacker's proprietary analytics database, HubSpot, Gmail, Google Sheets, and more.
2. Speed to working workflows. Teams could build functional agents in moments, not quarters.
3. Shared workspaces. When one team member builds something useful, others can access and learn from it.

Over roughly 90 days, they tested a concept that combined client profile data with network-wide performance trends to generate story recommendations. When it worked, they handed the validated concept to engineering. 

"We did it as a prototype to prove value. Then we gave it to the smart engineers to flesh it out within our product," Ken Romano, SVP of Product says.

"Sparks" now ships content recommendations for clients every Monday morning, with client acceptance and approval above 85%. 

Going beyond prototypes—dozens of agents running daily across the company

The success of “Sparks” led to a momentum change within Stacker: what else could we do with AI? What does approaching work from an AI-native perspective look like? 

And unlike Sparks, many of these new ideas didn’t require integration within their product—they were agents used for internal productivity, run entirely within elvex. Today, Stacker runs dozens of specialized agents across departments. 

Sales Enablement
One account executive who had been "in and out" with elvex became the most active user in two weeks once he saw how it connected to his actual prospecting workflow and HubSpot data. 

He built an agent that acts as the Sales team’s messaging strategist. It pulls client information from HubSpot, drafts personalized outreach, generates post-call follow-ups from Fathom recordings, and recommends content to share based on performance data. The integrations matter: when the agent can reference actual pickup numbers and reader counts from the analytics database, the outreach becomes credible rather than generic.

A "Seller Research" agent handles pre-call preparation, analyzing prospect websites for content fit and competitive positioning. Instead of scrambling before meetings, salespeople get structured intelligence briefs.

Analytics & Reporting
The "Story Insights" agent (nicknamed "Shovel") gives anyone on the team the ability to query story performance, client metrics, and content recommendations without knowing SQL. 

A separate "Vizbot" agent analyzes how Stacker's distributed content performs in AI search results across ChatGPT, Gemini, Claude, and Perplexity. For a company whose value proposition increasingly connects to GEO and LLM visibility, measuring this matters.

Editorial & Content Strategy
The editorial team runs an "MSN Performance Analyzer" that tracks performance patterns across title structures, content topics, and formats. When headlines with specific patterns start outperforming, the team knows to lean in. When a format shows fatigue, they back off.

A "Content Strategy Bot" helps develop data-driven content plans, benchmarking performance and identifying gaps in coverage.

Partner Management
Agents handle partner performance reporting, publishing partner follow-ups, and lead qualification questions like "Do they publish syndicated content?" for prospecting new publishers.

Results

For Stacker, elvex is the layer where AI adoption actually happens. Most workflows stay in elvex because they work. Some, like Sparks, graduate to the core product when they need to be customer-facing. Both paths deliver value.

"Originally it took about 67 hours to brainstorm content ideas for 100 clients," said Stacker's Content Strategy Lead, Tamara Sykes. "That dropped to about 33 hours. We also freed up time from our data scientists ... each ad hoc query that would come in would take at least 10 minutes to build a query, and would be caught up in a queue. After rolling out a self-service bot via elvex, users are now getting immediate responses in 1-2 minutes."

Here's how Stacker benefits from elvex:

• Dozens of specialized agents running daily across sales, analytics, editorial, and operations
• 50% faster content recommendations, saving dozens of hours per week per analyst
• Sales enablement: Prospecting and follow-up workflows connected directly to HubSpot and real performance data
• Analytics democratization: Natural language access to years of distribution data, delivered in Slack
• Accelerated Sparks product development: Internal workflow validated and shipped as customer-facing product feature, launched at 85% positive feedback

Stacker went from "we should do something with AI" to dozens of working agents and a shipped customer feature in just a few months. The difference was giving people a platform where they could build real workflows themselves, connected to real data, without waiting in line.