01
Hiring bar
Turn the role brief into a measurable standard
Puddle converts the role brief into rubric dimensions, signal criteria, and examples your team can calibrate against.
Rubric dimensions
Strong and weak evidence
Role-specific probes
Technical hiring infrastructure
Turn engineering hiring into an evidence-backed system.
Puddle helps teams define role-specific rubrics, run structured AI video screens, and review candidates through recordings, transcripts, and rubric-backed evidence.
From hiring bar to review packet.
Role rubric
Set
Technical depth
Collaboration
AI fluency
Hiring bar
Live screen10 min
P
Puddle screen
Candidate packet
Recommendation
Transcript
Rubric notes
Build evidence
Reviewer output
Trusted by the next generation of companies.
TeloraHow it works
From hiring bar to reviewer output.
Define the standard once, run every screen against it, and give reviewers the evidence trail behind the next decision.
01
Hiring bar
Turn the role brief into a measurable standard
Puddle converts the role brief into rubric dimensions, signal criteria, and examples your team can calibrate against.
Rubric dimensions
Strong and weak evidence
Role-specific probes
Role rubric
Set
Technical depth
Collaboration
AI fluency
02
Puddle screen
Run a focused screen against the bar
The interviewer asks role-linked questions, captures the recording and transcript, and follows up when an answer needs more signal.
10-minute screen
Transcript capture
Adaptive follow-ups
Live screen10 min
P
03
Reviewer output
Hand the panel evidence, not hunches
Reviewers get the recommendation, confidence, timestamps, rubric notes, and the reason behind the next action.
Recommendation
Timestamped evidence
Reviewer action
Candidate packet
Recommendation
Transcript
Rubric notes
Build evidence
Review packet
Every recommendation shows its work.
Puddle converts a 10-minute video interview into a role-specific review packet: rubric observations, question coverage, authenticity signals, and the evidence behind the final recommendation.
10-minute screen output
Scorecard for Prakul Singh
A rubric-specific report with enough evidence for the hiring manager to agree, disagree, or replay the original answer.
12/16
Score
10%
AI risk
4/4
Coverage
Dimension
Evidence note
Systems reasoning
3 / 4Prakul isolated the failure to queue fan-out, named retry and idempotency risks, and proposed audit logging before being prompted. He needed one follow-up to quantify throughput thresholds.
Ownership
2 / 4He took a half-scoped onboarding bug from reproduction to shipped fix, including migration notes and reviewer signoff. The follow-through was clear, but the example stayed narrow.
Product judgment
3 / 4He tied consent copy, auth states, and room readiness to candidate trust, then chose a smaller launch path over rebuilding the ATS flow. One reviewer-calibration tradeoff came late.
Communication clarity
4 / 4He gave compact answers with concrete timestamps, tools, constraints, and outcomes. Follow-ups were direct, and the final root-cause explanation was easy to quote in reviewer notes.
Scorecard
Rubric notes tied to your criteria
Every screen becomes dimension-level observations with notes that explain the review.
Final scores
12
out of 16
Systems reasoning3 / 4
Ownership2 / 4
Product judgment3 / 4
Communication clarity4 / 4
PuddleStart with one role and a small pilot cohort.
We will map your rubric, source and screen candidates, and hand your team the evidence needed to meet the best people in person.