Does this compete with Karat or CodeSignal?

No. Karat and CodeSignal own the live coding loop; we own the screen before that. Intrvio works well after the resume filter and before a live technical loop — it lets you decide whether the candidate actually thinks like an engineer, before you spend the time coding with them.

Do you do live coding?

No, by design. A voice-driven structured interview measures system-design reasoning, production ownership, and collaboration far better than coding tests do. Keep using Karat, CodeSignal, or HackerRank for coding.

Isn't this too basic for senior engineers?

The competencies are the same, but the scoring bands shift. From a senior we expect not just naming trade-offs, but anticipating second-order consequences. The rubric is calibrated to seniority.

How do you catch AI-generated answers?

Candidates speak aloud, and follow-up probes adapt to the answer content. A pasted answer falls apart under follow-up; by the second or third probe, GAIA's questions diverge from anything an off-screen tool can pre-compute.

Is this EU AI Act compliant?

Yes — it is positioned as a screening assistant, not an automated decision-maker. All scores are human-reviewable and audit-logged. See /eu-ai-act-compliance.

Backend, frontend, mobile — all the same pack?

The competency framework is shared, but the question pool branches by specialization. For a mobile candidate, follow-up probes branch into React Native or iOS-specific trade-offs.

Can I add my own questions?

Yes. The base pack ships with 16+ questions per role; you can add custom questions and they will be evaluated against your custom rubric.

Use case

AI interview for engineering

TLDR

This page is for engineering leaders hiring software engineers, SRE, mobile, data, and ML candidates. The positioning is explicit: Intrvio is the screen between the resume filter and a live coding loop. Karat and CodeSignal own the live coding loop; we run the structured "does this candidate actually think like an engineer" screen before you spend live engineering time with them.

GAIA evaluates against four competencies: technical depth, system design reasoning, quality ownership, and collaboration. This framework follows Google's structured-interviewing guidance and Karat's 2026 "Human + AI" engineering rubric white paper, both of which emphasize scoring observable behaviors rather than intent or style.[1][2]

Core competencies

1. Technical depth

Core implementation, architecture decisions, debugging, and trade-off judgment.

Sample question: Tell me about a technically difficult system or feature you built. What trade-offs did you make and how did you validate the result?

Scoring anchor: names specific technologies and why they were chosen, discusses alternatives, and provides a validation method (load test, prod metric, A/B).

2. System design

Designs scalable, maintainable systems with clear constraints; anticipates failure modes.

Sample question: How would you design a reliable service for this role's main workflow? Walk through data model, APIs, failure modes, and observability.

Scoring anchor: asks for constraints early, draws a clear data model, names at least two failure modes, and proposes concrete observability metrics.

3. Quality ownership

Testing, observability, reliability, and production accountability.

Sample question: Describe a time you found or prevented a production-quality issue before it affected users.

Scoring anchor: takes ownership of a specific bug or regression, names the observability/test gap, and explains the systemic fix (postmortem, runbook update).

4. Engineering collaboration

Works clearly across product, design, and engineering peers.

Sample question: Tell me about a time you disagreed with another team on a technical direction. How did you resolve it?

Scoring anchor: paraphrases the opposing position fairly, uses shared evidence or a prototype, mentions documenting and following up on the resolution.

Sample interview flow

How GAIA screens a backend engineering candidate in about 40 minutes:

1. Opening (3 min). Stack, last role, most recent shipped project.
2. Deep dive (8 min). Asks about the hardest thing they shipped; probes implementation detail and validation.
3. System design (10 min). Open-ended design problem; data model, APIs, failure modes.
4. Prod incident (5 min). A real incident or near-miss; ownership and systemic fix.
5. Collaboration example (5 min). Disagreement or cross-team dependency; resolution structure.
6. Stack depth (4 min). Specialization-specific follow-ups (DB internals, concurrency, networking).
7. Candidate questions (3 min). The quality of their questions is itself a signal.
8. Closing (2 min). Next steps: live coding loop.

What signals matter most

Meta-analytic work on structured interviewing finds the strongest predictors for engineering candidates roughly in this order:

Structured interview combined with a work sample (combined validity ≈ 0.63)[3]
Structured interview alone (≈ 0.42)[1]
Work sample alone (≈ 0.33)
General mental ability test (≈ 0.31)
Personality test alone (≈ 0.10–0.20)

Practical takeaway: this page supports the first signal — structured interview. Karat or CodeSignal supplies the next stage's work sample.

Common interviewing pitfalls for this role

Asking trivia questions. "What's under a hash map?" is leftover from textbook exams. Ask about trade-offs in real systems instead.
Coding at the wrong stage. Coding inside a screening interview lowers throughput. Structured reasoning is the better filter at this stage.
Confusing stack expertise with scope. An engineer moving from React to Vue takes about two weeks; reasoning transfers, framework knowledge does not.
Avoiding disagreement signals. Candidates who only volunteer agreement examples often have collaboration gaps. The disagreement story is the signal.

Sample rubric snippet — system design (BARS)

Score	Behavioral anchor
5	Surfaces constraints early, draws a clear data model, names two or more failure modes, sizes capacity numerically, and discusses rollout and rollback strategy.
4	Well-structured design with concrete components and APIs; failure modes are surfaced shallowly or scale estimates are missing.
3	Produces a working design but does not discuss alternatives; observability is not addressed.
2	Jumps straight to APIs or libraries; no data model and no trade-off reasoning.
1	Tries to reframe the question, does not ask for constraints, or gives a generic "split into microservices" answer.

Frequently asked

[1] McDaniel, M. A., Whetzel, D. L., Schmidt, F. L., & Maurer, S. D. (1994). The validity of employment interviews: A comprehensive review and meta-analysis. Journal of Applied Psychology, 79(4), 599–616.
[2] Karat (2026). Human + AI Technical Interview Rubrics for Modern Hiring. Google re:Work, A guide to structured interviewing for better hiring practices.
[3] Schmidt, F. L., & Hunter, J. E. (1998). The validity and utility of selection methods in personnel psychology. Psychological Bulletin, 124(2), 262–274. See also Sackett, Zhang, Berry, & Lievens (2022) on revised validity estimates after correcting for indirect range restriction.