01
Unqualified candidates advance.
They pass keyword screens and waste your hiring manager's time in deep interviews.
42% of resumes contain claims with no supporting evidence.
Hire with the confidence of your best engineer.
0
Resumes screened
per role · per day
per role · per day
0
Structured report
turnaround after call
turnaround after call
0
Cheat detectors,
one trust score
one trust score
0
Cheaper than
senior engineer screens
senior engineer screens
—— Chapter 01 · The problem
Resumes lie.
Resumes lie.
Recruiters guess.
Engineers burn out.
You can't tell who is genuinely qualified from a resume. Engineers running every first round are too expensive — recruiters alone lack the depth. Unqualified candidates burn hours in your panels.
02
Questions lack depth.
Every interviewer asks something different. No one digs deep enough, no one stays consistent.
3.4× variance between two interviewers on the same role.
03
Resume skills don't stick.
Candidates claim expertise. The first follow-up exposes them repeating resume language verbatim.
1 in 3 "expert" claims collapse on the second probe.
—— Chapter 02 · The platform
One adaptive engine.
One adaptive engine.
Two reports.
Before the call, the Resume Report grades the candidate against the JD and flags every claim that lacks proof. After the call, the Interview Report grades the candidate on what they actually said — with evidence quotes, integrity signals, and a hire recommendation.
01 · Before the call
Pre-screen
Resume Report
We parse every resume against the JD, weight each claim by recency and proof, and pre-flag the claims that need probing in the live interview.
JD-fit score
87
9 of 11 anchors matched
CLAIM
React, 6 yrs
CLAIM
TypeScript, 5 yrs
PROBE
"Optimised LCP" — no metric given
PROBE
"System design at scale" — no scale named
Auto-routed to interview
→
02 · After the call
Strong hire
Interview Report
After 15–25 minutes, you get a per-skill breakdown with verbatim evidence, every integrity signal, and a clear Hire / Hold / Reject recommendation.
Overall
94
Hire
React depth96
System design82
Communication91
Integrity98
VERBATIM · 00:11:24
Once Cart stopped re-rendering on every scroll, the dropdown felt instant — saved roughly 40 unnecessary renders a second.
Sent to hiring manager
→
— Core engine
An adaptive interview engine.
An adaptive interview engine.
No other platform does this.
Sreeva doesn't follow a script. It follows the candidate. If they're an expert, it dives deeper. If they struggle, it pivots to find core strengths — every interview is a different conversation.
CANDIDATE
"We got render time down by about 38%."
SREEVA · ADAPTING
"What method measured that? What did you trade off — and can you walk me through the before-and-after profile?"
Follow-up
Metric claim checked
Evidence
Profiler named
Signal
Senior depth
Same question, new candidate — Sreeva would have asked something else
And underneath, the engine that powers both
Smart follow-ups01
It probes like a senior.
Hedge words, missing metrics, and unclear ownership — the engine probes for the number, the tool, the trade-off.
"basically"
"around"
no metric
→ probe
Integrity proctoring02
12+
cheat detectors,
one trust score
one trust score
Tab focus loss
Multiple voices
Paste events
Gaze drift
Cadence shift
Latency anomaly
Lip-sync delta
Browser identity
+4 more
Multilingual03
20 voices.
11 languages.
Candidates interview in the language they think in.
Englishहिन्दीதமிழ்తెలుగుবাংলা
मराठी+5
Economics04
1⁄15 the spend.
A fraction of senior-engineer screening cost — with 24/7 candidate-timezone coverage.
—— Chapter 03 · The flow
From JD to decision,
From JD to decision,
in four moves.
-
01
Upload your JD
Drop a PDF or paste a link. Sreeva reads the role and extracts the skills that matter most.
-
02
AI builds the interview
A custom question bank generated per JD. No templates. No recycled scripts.
-
03
Candidate is interviewed
15–25 minutes. Adaptive, calm, consistent. Probes vague answers. Pivots when honest.
-
04
You get the call
Per-skill scores, verbatim evidence, integrity flags, and a Hire / Hold / Reject call.
upload.sreeva.aistep 01
Drop the JD or
paste a link
paste a link
Extracted · 11 skill anchors
ReactTypeScriptSystem design
GraphQLPerformanceAccessibility
Testing+4
generate.sreeva.aistep 02
COREWalk me through the last performance issue you debugged in a React app.
COREExplain a TypeScript generic you've actually shipped, and what it solved.
PROBEYou list "optimised LCP" — what was the number before and after?
SCENEYour team's bundle is 3MB. Where do you cut, and what do you measure?
PROBEHooks vs render props — when did you regret one of those choices?
32 questions generated · adaptive variants enabled
i/8291.sreeva.ai live
SREEVA
You mentioned cutting bundle size by 40%. What was the measurement tool, and what tradeoff did you make first?
CANDIDATE
Lighthouse for the macro number, source-map-explorer for the breakdown. First tradeoff was killing moment.js — saved 67kb gzipped — but had to write a thin format helper.
00:08:42
report.sreeva.aistep 04
Interview #8291
Senior Frontend Engineer
94
SREEVA
Walk me through a performance issue you actually shipped a fix for — what was the metric, and what changed?
AANYA
— verbatim · 00:11:24
Once Cart stopped re-rendering on every scroll, the dropdown felt instant — saved roughly 40 unnecessary renders a second.
—— Chapter 04 · Why Sreeva
Screening that gets smarter
Screening that gets smarter
with every answer.
We don't score candidates. We verify them.
Aspect
Traditional screening
Sreeva AI
Interview style
✕Same questions for every candidate
✓Adapts in real time to every answer
Skill verification
✕You trust what they say
✓You see what they actually know
Candidate fraud
✕Anyone can show up and answer for them
✓Identity, behaviour and patterns verified
Interviewer bias
✕Depends on who shows up that day
✓Consistent evaluation every single time
Report quality
✕Gut-feel notes after the call
✓Structured score, evidence, recommendation
Cost
✕Expensive human screeners per interview
✓A fraction of the cost, no bottleneck
"
We replaced four engineer-hours per role with a sixty-second report. The signal is sharper — and the panel finally trusts the funnel.
VR
Vikram Rao
VP Engineering · pipeline of 3,200/mo
—— Chapter 05 · Pricing
Plans for every
Plans for every
hiring stage.
Start with one role. Scale adaptive interviews across teams once signal quality is proven.
Starter
$99
/mo
- 30 interviews included
- Up to 20 min per interview
- Top-up: $4.50/interview
Most Popular
Growth
$299
/mo
- 100 interviews included
- Up to 20 min per interview
- Top-up: $3.50/interview
- Ask Sreeva AI
Scale
$699
/mo
- 300 interviews included
- Up to 20 min per interview
- Top-up: $2.80/interview
Business
Custom
- 600+ interviews
- Custom interview duration
- Top-ups included or custom
- Ask Sreeva AI
Detailed feature comparison
All plans include adaptive AI, smart follow-ups, JD-grounded questions.| Feature | Starter | Growth | Scale | Business |
|---|
Ready when you are
Hire your next great
Hire your next great
engineer with confidence.
Schedule a thirty-minute walkthrough. We'll run Sreeva on a real role from your pipeline — live.