SAMPLE — demonstration data.
This is not a real candidate. The structure, rubric scoring, and transcript format match exactly what Vettika delivers after every interview. Role and rubric are drawn from the product's seeded "Senior Backend Engineer" campaign.
Sample Scored Interview Report
Weighted rubric score across 4 criteria. Recommendation threshold for this campaign: ≥ 7.0 advance, 5.0–6.9 review, < 5.0 decline.
Rubric scores
Evidence (from transcript): Described an end-to-end ingest service ("LiveKit events → Go consumer → Neon, ~40k events/hr"); named the first failure mode unprompted (consumer lag during reconnect storms) and the fix (idempotent upserts + backpressure).
Evidence (from transcript): Walked through a real query-plan regression: seq scan after a data-shape change, diagnosed via EXPLAIN (ANALYZE, BUFFERS), fixed with a partial index; quantified before/after (1.4 s → 30 ms).
Evidence (from transcript): Explained eventual-consistency contract to a "non-engineer PM" cleanly; paused to ask a clarifying question before answering Q3 rather than guessing.
Evidence (from transcript): Pushed back well once (scope of on-call ownership) but overclaimed on Kafka experience; when the agent followed up, admitted it was "mostly NATS, one Kafka migration." Honest on the follow-up, but the initial claim needed the push.
Strengths
- Concrete production examples for every systems question — no hypotheticals.
- Quantifies outcomes without prompting (latency numbers, event volumes).
- Treats the interviewer's follow-ups as collaboration, not interrogation.
Flags for the human round
- Probe Kafka/NATS depth directly — initial answer overstated, corrected only on follow-up.
- On-call ownership expectations: candidate wants RFC-to-ship ownership but hedged on pager duty.
Transcript excerpt
Full transcript attached to every real report; the candidate receives their transcript and score too.
AI: Walk me through a Go service you designed end-to-end recently.
Candidate: The closest is our event-ingest path. LiveKit fires webhook events, a Go consumer validates and batches them, writes to Neon. Around forty thousand events an hour at peak…
AI: What broke first?
Candidate: Reconnect storms. When the socket flapped, we'd get duplicate deliveries and the consumer fell behind. We made the writes idempotent — natural key plus upsert — and added backpressure so the queue drains before we accept more.
AI: You mentioned Kafka earlier — how much of that pipeline was Kafka in production?
Candidate: …Honestly, mostly NATS. I did one Kafka migration at my last job, but day-to-day it was NATS.
Every report includes: full transcript, per-criterion scores with evidence quotes, weighted overall score against your rubric, strengths, flags, and a recommendation. The candidate keeps their transcript and sees their own score.
Want this report for your own candidates?
First 3 interviews free. No card. No subscription.