How GiveCampus Cut Sourcing From an Hour to 10 Minutes

Building an AI-Forward Recruiting Function

The GiveCampus People team has embraced AI as a practical way to expand what a small team can do. They’re using it to streamline everyday work, build internal tools, and rethink workflows that once required far more time or technical support. 

Recruiting is following the same playbook.

When Abby Skinner, Senior Recruiter, joined the company in early 2025, it had roughly 80 employees and she was largely running recruiting on her own. Since then, GiveCampus has grown to around 135 employees, while Abby completed 65 hires last year and managed nearly 20 distinct roles at once at its peak.

As hiring expanded, GiveCampus began building more specialization into the function. Andrea Jajcanin joined as its first dedicated Technical Recruiter, taking ownership of technical hiring while Abby shifted her focus to sales, GTM, and other business functions. 

Together, they now make up a two-person recruiting team supported by an increasingly AI-native recruiting stack. Juicebox plays a key role in that stack, already helping cut down sourcing time for roles from one hour to 10 minutes, and helping the team make two new hires.

Evolving Beyond a Recruiter-of-One Model

Before Juicebox, sourcing leaned heavily on a legacy, manual sourcing tool, inbound applicants, and referrals.

The process required significant manual effort and gave Abby a relatively narrow view of the available talent market. As GiveCampus expanded hiring across roles ranging from machine learning and infrastructure to sales, product, and finance, the team wanted a sourcing workflow that could give two recruiters more leverage without adding another layer of complexity.

Abby had been exploring solutions for a while and couldn’t find the right fit. She revisited Juicebox a year after starting the search and found a very different product:

“By the time I took a demo, Juicebox had added so many features that it felt like a different product. I knew it would actually save me time, not just add another login to my day.”

The pace of development continued after GiveCampus became a customer. Abby says one of the things that has “continuously impressed” her is how quickly Juicebox iterates on customer feedback.

“When we give feedback, we actually see it show up in the product. It’s not just lip service.”

That responsiveness fit naturally with a team already accustomed to experimenting with AI and continually refining how it works.

Making AI Part of the Day-to-Day Recruiting Workflow

Juicebox is now embedded in how GiveCampus approaches sourcing. As Abby puts it:

“The Juicebox Agent is at the core of how I source now.”

Across the team, Juicebox supports candidate discovery, outreach, and reporting. Abby uses Search when she wants to source manually, builds sequences for candidate outreach, and A/B tests AI-generated messaging against her own. The drafted responses can also retain context from earlier candidate conversations and adapt to Abby’s voice.

“The drafts remember context from earlier conversations and sound like me. I save a surprising amount of time, and the outreach still feels personal, not templated.”

GiveCampus is also experimenting with Juicebox MCP alongside Claude as part of its recruiting reporting workflow. Abby and Andrea have started exploring Intake as well, looking at how it can fit into the way she kicks off and manages searches.

The same workflow also dramatically reduces the time they spend on sourcing. 

Depending on how difficult a role is to fill, Abby estimates that a sourcing session used to take anywhere from 30 minutes to an hour. With an Agent, that work now takes about 10 minutes.

The team is already seeing that increased capacity translates into hires. GiveCampus has made two hires through Juicebox so far, a Senior Machine Learning Engineer and a Senior Data Engineer, with several more Juicebox-sourced candidates moving through active pipelines across other open roles.

Taken together, these workflows give the team more room to keep pushing on what AI can take on. Rather than using it for one isolated task, GiveCampus is steadily weaving it into the recruiting process wherever it can create more capacity for a small team.

Finding Candidates Their Legacy Tool Wasn’t Surfacing

Speed is only part of the shift. Juicebox has also expanded the pool of candidates GiveCampus can reach.

Abby says manual sourcing in Juicebox is surfacing strong candidates who either were not present in their legacy sourcing tool or simply were not being surfaced there.

“That’s probably the most valuable shift, since it’s not just faster, it’s finding people I wouldn’t have found before.”

GiveCampus is putting that broader reach to work across a wide range of searches, from machine learning engineers, senior data engineers, and software engineers to product managers, designers, engineering managers, finance and business engineering roles, account executives, and BDRs. The team can move from a highly specialized technical search to a business role without rebuilding its sourcing motion each time.

That broader visibility gives GiveCampus more flexibility to pursue specialized talent while keeping the sourcing process manageable for a two-person team.

Building a Recruiting Function That Can Keep Evolving

The GiveCampus recruiting function has become more specialized as the company has scaled, with the team continuing to build AI into sourcing, outreach, reporting, and intake.

That reflects a broader operating philosophy across GiveCampus. AI is helping small teams take on work that once required more time, more manual effort, or deeper technical support.

Juicebox has become part of that model inside recruiting. 

Today, it helps GiveCampus compress sourcing work from as much as an hour to around 10 minutes while uncovering candidates the team was not consistently finding before. As the function continues to mature, Juicebox has unlocked what this two-person team can accomplish.