How Evident Built a Lean AI-Native Engineering Screen

Evident wanted to keep hiring lean without manually screening every engineering applicant. Aptora turned a relevant investment-platform problem into an AI-enabled technical assessment for its first candidate pool.

The Problem

Evident is a private-markets investment platform without a dedicated HR team, so application review and early screening fall directly on a lean operating team. LinkedIn produced a large candidate pool, but manually reviewing profiles and interviewing applicants one by one would pull the team away from the business.

AI created an additional challenge. Evident did not want to ban it or treat it as a shortcut. The team wanted engineers who could use AI as a tool, write effective prompts, validate the output, and still make sound technical decisions themselves.

The Solution

1. Aptora built a domain-relevant task

Instead of using a generic algorithm question, Aptora created an Investment Portfolio Management API task. Candidates had to build a TypeScript and SQLite backend for creating portfolios, managing holdings, and calculating performance.

The task tested relevant reasoning through business rules for portfolio names, stock symbols, share and price values, duplicate holdings, and invalid inputs.

2. Candidates could use AI

Candidates were encouraged to use the Aptora agent while working. That made AI part of the evaluation rather than something the team had to police. Evident could see whether candidates gave useful instructions, checked generated code, tested edge cases, and recovered when the first approach did not work.

3. Evident added evidence before interviews

The assessment was sent to about 20 applicants after the initial LinkedIn screen. Each candidate worked in the same environment and against the same requirements, giving Evident a consistent way to compare backend fundamentals, domain reasoning, code quality, and AI usage before committing team time to interviews.

The Outcome

Evident used Aptora to screen about 20 applicants and hire a high-performing engineer. The assessment helped the team keep the process lean while evaluating candidates through domain-relevant work and effective AI use.