Product tour
Follow one hire through Panelynx.
From a pasted job description to a signed offer. Every screen along the way, for one role: Senior Backend Engineer at an 80-person fintech.
SBSenior Backend Engineer · 80-person fintechWatch
The whole hire in 50 seconds.
Step 01 · Plan
Paste the JD. Get a structured plan.
We paste the job description for the Senior Backend Engineer role. Panelynx reads it and drafts a question plan against the rubric, grouped into timed sections, each question tagged by difficulty and the skill it tests. We tweak two, drop one, and the panel is ready to interview.
You will own distributed systems and API design for our ledger, with hands-on rate limiting, Redis, and Postgres at scale.
Comfortable on-call; partners closely with product on trade-offs.
Paste any JD. Panelynx extracts the must-have skills and maps them to rubric criteria.
Auto-drafted questions arrive grouped into timed sections, each tagged by difficulty and skill.
Edit freely. Pull from the question bank, reorder, or swap a question in one click.
Interview, Score, and Decide reuse this same template: annotated mock, three pins, first-person narration.
Step 02 · Interview
Score live, with the co-pilot beside you.
Maya joins the call. As she works through the rate-limiter question, we score against the rubric in real time while the co-pilot captures evidence and offers the follow-up we might have missed.
Q4 of 8 · System design
Design a rate limiter for a public API. Walk through the approach and trade-offs.
AI CoachStrong on trade-offs. Try probing: “Where does this break under load?”
Rubric check: “failure handling” not yet covered.
Per-question timing keeps the panel on plan, with a gentle nudge when a question runs long.
Score as the answer unfolds. Strengths and concerns attach to the exact question.
The co-pilot suggests the follow-up, you decide whether to ask it.
Step 03 · Score
See where the panel agrees, and where it does not.
Three of us interviewed Maya independently. Panelynx lines our scorecards up side by side, so the one place we disagree is obvious, and we reconcile it before the debrief instead of after the offer.
Every panelist's scores sit in one view, attributed and locked on submit.
Divergence is flagged automatically, so a four-versus-three never slips by unnoticed.
Reconcile the gap in the debrief, with the evidence right there to settle it.
Step 04 · Decide
Move to offer with the whole panel behind you.
Scores roll up into a clear recommendation. We send the offer, it routes through the approval chain, and once Maya signs we keep tracking how the hire performs.
Scores roll up into a single recommendation the whole panel can stand behind.
Offers route through your approval chain, with sign-off tracked at each step.
Outcome tracking closes the loop, so you learn which signals actually predicted the hire.
After ten hires
What you know after 10 hires.
The same structured data that runs each interview rolls up into a picture of how your hiring actually performs.
88% of panels now agree on the same candidate, up from 61% before shared rubrics.