What We Learned from 500 Applicants, a Live Build Challenge, & Eight Weeks of AI-Native Interns

We handed Fifer™ to first-year developers, and they were productive in days.

This summer, CVP NextLabs launched Fifer University, our inaugural 8-week AI internship program. From June 1 to July 31, 2026, a cohort of early-career technologists worked alongside CVP mentors to build working prototypes on Fifer, our enterprise GenAI platform built for the public sector.

As we hoped, the work they produced was strong out of the gate. What surprised us was what the process revealed about hiring, mentorship, and what it takes to deliver AI in government.

Identifying & nurturing AI-native talent, AKA Evolving tried-and-true processes to get better outcomes.

EVALUATING TALENT

We skipped traditional interviews and ran a build challenge instead.

We didn’t follow a traditional hiring process for the 500+ applicants to Fifer University. There were no cover letters, no behavioral interview scripts, no prompts asking candidates to describe a time they overcame a challenge. After an initial screen, every advancing applicant received a technical challenge to build something real using AI tools. Then they demoed it live and made adjustments on the spot, right in front of us.

The reason is straightforward: the ability to describe work matters far less than the ability to do it, adapt it, and defend it under pressure. To identify the top talent, we needed to see how candidates prompted, debugged, reasoned about tradeoffs, and recovered when requirements shifted mid-demo.

IDENTIFYING THE RIGHT SKILLS

The build challenge taught us as much about hiring as it did about the candidates.

Every tech firm is now recruiting from the first true cohort of AI-native computer science graduates. This is a workforce that learned to code alongside a model, and many of them don’t remember a world where AI assistance was optional. That old world is the one that shaped recruiting and hiring processes across industry, and relying on yesterday’s standards to evaluate today’s talent overlooks the skills and qualities that matter most.

Here’s what the first year of Fifer University taught us about identifying AI-native talent:

These lessons have fundamentally changed how we recruit and evaluate tech talent: We’re hiring for adaptability, curiosity, and judgment because tools like Fifer have raised the technical floor for everyone. Candidates will see more real-world evaluations, more live problem solving, and far less reliance on the resume as a proxy for capability.

AI fluency is only the first hurdle. The right tools get you across the finish line. Strong mentoring wins you the race.

Our 2026 interns arrived with strong AI fluency. What they didn’t have was decades of experience delivering mission-critical technology and outcome-based solutions to the federal government. To succeed in their mission—to conceive and develop working prototypes that solve real federal challenges in mere weeks—they needed two key things: a secure, configurable AI platform, and mentors with deep technical and industry experience. We gave them both.

The AI platform: We designed and built Fifer specifically for regulated industries, with swappable models, patent-pending multi-agent retrieval, and an architecture that keeps sensitive data entirely within a client’s secure environment. And the expertise: CVP has been building AI products since 2008 and serving the federal government for much longer.

Pairing AI-native builders with experienced, mission-focused technologists is where education gets translated into real value. Aligned with CVP mentors, the interns used Fifer’s pre-built skills and agents to build mission-aligned, AI-powered solutions in weeks. Along the way, they learned valuable lessons:

• What it takes to earn the trust of a federal agency and its mission owners
• How to design for auditability and human oversight
• How to translate a technical capability into a business case that gets funded
• Why data classification, PII handling, and compliance are essential elements of innovation

The prototypes: What a purpose-built platform made possible in eight weeks.

The prototypes our interns produced this summer were proof-of-concept work built on publicly available data to explore the kinds of problems Fifer is designed to solve.

Each solution addressed a real operational problem and was built in mere weeks by a student with little to no government experience who got up to speed on Fifer within days.

Fifer University validated something we built into the platform from day one: building for the toughest constraints produces the most effective tool. 

We developed Fifer because the federal government deserves secure GenAI infrastructure and industry-leading AI solutions purpose-built for public sector and regulated industries. Our AI internship program put that to the test: give AI-native developers with no government experience a real platform and a real problem in an isolated internal environment, and see what they ship. The result speaks to how federal agencies will staff, scale, and sustain AI work over the next decade.

Fifer takes experienced federal developers & makes them faster…

Before we talk about the future, let’s talk about the present. For millions of working developers—including CVP’s, who have built careers embedded in federal missions, navigating government systems and complex environments—Fifer eliminates the barriers that would render their years of manual development expertise obsolete in the emerging era of AI-native developers.

Using Fifer, a developer who has never fine-tuned a model or stood up a vector database doesn’t have to. Model selection, security architecture, compliance guardrails—those decisions are already made and built in. Developers can show up to the problem they understand and build compliant, secure code faster than their early career selves could ever have imagined.

Fifer-powered federal developers ship production-ready solutions in days instead of weeks, so traditionally trained development teams can spend less time on infrastructure decisions and compliance configuration and more time doing work that requires human judgment.

Fifer fluency has become a force multiplier across federal delivery teams, compressing timelines without compressing quality.

…& brings AI-native talent up to mission speed.

Through our work with the federal government, CVP has already demonstrated that experienced developer teams don’t require AI retooling to optimize efficiency and outputs. Fifer’s real test was this:

Could AI-native developers without government or professional experience quickly deliver viable solutions to real federal problems?

It turns out that yes, they can. The first cohort of Fifer University’s AI internship program were productive within days: a real win for the interns, the program, and Fifer itself.

From a corporate perspective, that success is the function of three key factors that helped us identify, empower, and optimize true AI-native talent.