For the example job: Senior Backend Engineer, Platform at Spanwise
Five questions chosen from the job post and the gaps in the candidate’s resume. In the app you answer each one on camera; ASAI transcribes what you said and scores it. Below is the report exactly as ASAI wrote it.
Sample: The candidate, resume and answers were written by the ASAI team for this example job, and the answers were read aloud by a synthesised voice. The questions, transcripts, scores and feedback are ASAI’s real, unedited output.
STRONG
Interview debrief
SPSpanwise
A strong showing — you would hold your own here.
The substance is there; a few answers still lose the thread.
Fix this first
When discussing rate limiting, explicitly mention the algorithms used (e.g., token bucket) and how limits are communicated to clients.
Source: Highest-leverage fix
Questions scored
5 answered
Skipped
None
Time speaking
7m 02s
Pace
154 wpm
Section 01
Held up
In priority order
Fix #1 is the one carried into the verdict above.
Section 02
Speaking pace
Well paced154wpm
inside the 120–160 wpm band
Filler words
Creeping in2.3per minute
16 filler words in total — target under 2/min.
Time speaking
7m 02s
1,087 words across 5 answers · 1m 24s average
Length of each answer
Left to right, in the order you answered. Tallest bar = 1m 33s.
Section 03
Skill | How it was probed | Verdict |
|---|---|---|
| Apache Kafka | Probed in the scenario question regarding back-pressure and DLQ design. | Not demonstrated |
| Protocol Buffers | not probed in this session | Not demonstrated |
| Rate Limiting | Probed in the noisy neighbor question. | Partial |
Probed in the scenario question regarding back-pressure and DLQ design.
not probed in this session
Probed in the noisy neighbor question.
Section 04
Rubric & evidence
3 criteria, each quoted from your answerThe candidate excellently translated throughput and latency into business terms (dials for wait time and traffic capacity).
“I drew it as two dials. One was how long a seller waits to see their change, and the other was how much traffic we could absorb before things started queuing.”
The candidate understood the PM's lack of technical vocabulary and adapted the explanation to empower the PM to make the decision.
“I didn't explain it as throughput versus latency because those words meant nothing to him... That made it his decision, with the cost in front of him.”
The candidate successfully guided the PM to a consensus, though the exact facilitation process beyond showing the dials was brief.
“We agreed on small batches flushed every few seconds and a separate fast path for stock changes”
Answer structure
Full STAR structureClear context and business need.
“at Flipkart, I was on the search ingestion team and our product manager wanted price and stock changes to show up in search much faster.”
Clearly defines the technical trade-off.
“The honest trade-off was that we indexed in big batches because big batches were cheap. If we made every update go through on its own, freshness would improve, but the cost per event would go up”
Specific, creative action taken to explain the trade-off.
“I drew it as two dials. One was how long a seller waits to see their change, and the other was how much traffic we could absorb before things started queuing. I showed him what happened to each dial at three batch sizes”
Strong business and interpersonal results.
“After the changes, the delay came down from 40 minutes to about 90 seconds, and we still held the peak load. And, you know, the PM reused the two dials picture in his own planning review”
What you said
245 words in 1m 33sSo, at Flipkart, I was on the search ingestion team and our product manager wanted price and stock changes to show up in search much faster. At that point, the indexing delay was around 40 minutes. Um, the honest trade-off was that we indexed in big batches because big batches were cheap. If we made every update go through on its own, freshness would improve, but the cost per event would go up and we could fall behind at peak when we were handling around 40,000 events a second. So, I didn't explain it as throughput versus latency because those words meant nothing to him. I drew it as two dials. One was how long a seller waits to see their change, and the other was how much traffic we could absorb before things started queuing. I showed him what happened to each dial at three batch sizes, using numbers from our own load tests. That made it his decision, with the cost in front of him. We agreed on small batches flushed every few seconds and a separate fast path for stock changes, since an item that's out of stock but still showing in search is the worst case for a customer. After the changes, the delay came down from 40 minutes to about 90 seconds, and we still held the peak load. And, you know, the PM reused the two dials picture in his own planning review, which told me it had landed.
Highlighted words are the exact phrases the evaluator scored you on.
A stronger version of this answer
At Flipkart, our PM wanted price and stock changes to appear in search faster than our 40-minute indexing delay. The technical trade-off was throughput versus latency: single updates improved freshness but increased cost and risked queuing during our 40,000 events/sec peak. Knowing technical jargon wouldn't help, I drew the trade-off as two dials: 'seller wait time' and 'traffic capacity.' I presented three batch-size scenarios using load-test data. This empowered the PM to make an informed decision. We agreed on small batches flushed every [your X here] seconds and a fast path for stock changes. The delay dropped to 90 seconds while maintaining peak load capacity, and the PM even reused my 'two dials' analogy in his planning review.
Section 05
10 more questions from this job’s bank that this session never reached. Nothing here is scored — they are yours to rehearse, and a retake draws a fresh set rather than repeating what you just answered.
Tell me about a time you had to scale a PostgreSQL database or optimize queries for a table with hundreds of millions of rows. What specific techniques did you apply?
Explain a time you designed or operated a Kafka consumer architecture. How did you handle consumer groups, partitioning, and offsets to ensure reliable delivery?
Describe a complex data migration you led. How did you run the old and new systems side-by-side and verify data integrity before cutting over?
Walk me through a time you had to debug a complex issue in a Kubernetes environment, such as a crash-looping pod or resource starvation under load.
Describe your experience designing service-to-service APIs using gRPC and Protocol Buffers. How did you handle schema evolution and backward compatibility?
Walk me through a complex concurrency pattern you implemented in Go. How did you manage goroutine lifecycles, channels, and context cancellation to prevent leaks?
Tell me about a time you transitioned a system from a batch processing model to a real-time or streaming model. What were the hardest trade-offs you had to make?
Scenario: You need to migrate a large enterprise customer from our legacy batch ingestion to the new streaming pipeline with zero downtime. How do you plan the cutover and rollback strategy?
Walk me through a time you had to guarantee exactly-once or at-least-once delivery semantics in a distributed system. How did you handle retries and idempotency?
Tell me about a time you improved the observability of a system you owned. How did those improvements help catch an issue before customers noticed?
Debrief generated 15 Oct 2026, 4:04 pm
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