HunchVue: AI-Assisted Technical Interviewing Platform

The Challenge

Engineers use AI tools on the job every day, but technical interviews hadn't caught up — platforms either blocked AI use outright, testing candidates on a skill they'd never actually use unaided, or had no way to see it happening at all, leaving interviewers to guess whether a candidate got outside help, muted themselves, or switched tabs to another tool. Both options made the evaluation less trustworthy, right as AI-assisted engineering was becoming the norm. HunchVue needed to let candidates work the way they really would on the job, while giving interviewers hard evidence of exactly when and how they did it.

Research & Discovery

The feature set was driven by direct research rather than assumption: competitive analysis against existing technical interview tools to find where they broke down for real-world engineering work, close iteration with the developers building the platform to keep scope technically grounded, and client-facing demos that surfaced what interviewers actually needed to trust an AI-assisted evaluation. Each major feature was validated as a hypothesis before being committed to — proving out whether it changed interviewer or candidate behavior in the way we expected before investing further.

The Process

The proctoring system didn't start as a granular log — the earlier shape was closer to a single "flag suspicious activity" signal. That wasn't enough on its own: a flag with no context just makes an interviewer suspicious without giving them anything to act on. The design shifted to logging discrete, timestamped events instead — tab switches, copy/paste, video and mic toggles, even opening AI Assist — so an interviewer can scroll back and see exactly what happened, in order, rather than trusting a single vague alert.

The same logic shaped collaboration: rather than a raw shared screen, we landed on a "Follow Participant" model, letting an interviewer track a candidate's active file and cursor directly instead of just watching a static view.

The Solution

Five capabilities anchor the platform, each designed and validated independently:

Solution

Multi-File Project Environments — Create real-world coding interviews in a production-like environment where candidates work with frontend, backend, and full-stack applications using actual project structures.

Real-Time Live Collaboration — Enable collaborative coding interviews and pair programming with real-time visibility into coding activity, debugging workflows, and candidate collaboration in a shared workspace.

Integrated AI Assist — Conduct AI-native technical interviews with configurable AI assistance, enabling AI-assisted developer evaluation and complete interviewer visibility into every technical decision.

Database & SQL Tools — Assess database expertise with a dedicated SQL interview tool supporting live query execution, schema exploration, data modeling exercises, and real-world challenges.

Interviewer Controls & Proctoring — Protect assessment integrity with secure interview workflows, built-in proctoring, anti-cheating controls, activity monitoring, and real-time suspicious activity alerts.

Impact & Reflection

HunchVue has onboarded 20+ clients since launch, validating that the core design — realistic multi-file environments with transparent AI use and interviewer trust controls — holds up across real interview processes, not just internal testing.

The product is still early in go-to-market. The next phase of design work depends on real usage data, direct customer feedback, and how the interviewing landscape evolves — especially around AI-assisted coding, which is moving fast enough that today's design assumptions will need to be revisited.