Sit a full technical interview from start to finish, with an AI interviewer that listens to your answers and follows up on them. Choose from six stages covering questions, live coding, system design, code review and more. At the end you get a written report that scores how you reasoned and communicated, not simply whether your code ran.
Not sure yet? Explore a sample dashboard and two scored sittings before you start.
6
Stages
1
Combined report
3
Readiness bands
System Design
Stage 04 · Deep dive
Interviewer
You picked a queue in front of the ingest service. What happens when a consumer falls behind?
You
Lag grows but writes stay safe. I'd alert on consumer lag and scale the group before the retention window becomes the risk...
Interviewer
Interview questions for
every track you're placing in.
Java · Python · JavaScript · TypeScript · C++
Node JS · React · Next JS · Angular · Vue
Spring Boot · Django · SQL · DBMS · REST APIs
More coming soon
Pick one stage to drill it, or stack several to run back-to-back like the real thing, with one combined report at the end.
The interviewer asks technical questions from your chosen focus area, and you answer however the question calls for it: speaking out loud, typing, or dropping into a code editor. Every answer you give shapes the next follow-up, so a thin answer gets probed and a strong one gets pushed further.
You are handed broken or unfinished code and a real test suite, and you fix or build it while talking through your reasoning. You can ask the interviewer clarifying questions at any point, exactly as you would in a real pairing session.
You walk the interviewer through a project you have actually built: what it does, how it is put together, and why you made the calls you made. Then the follow-ups begin, and surface-level familiarity gets exposed quickly.
You get a design prompt with real constraints and a canvas to draw on. You clarify the requirements first, then design the system while narrating each choice, before defending your scaling and failure-mode decisions in a deep dive.
A teammate's pull request lands on your desk. You read the diff, leave line-by-line comments, and decide whether to approve it or request changes. You are scored on the bugs and risks you catch, and on how constructively you raise them.
You build a small feature with an AI assistant beside you, the way most teams now work. The assistant helps, but you decide what actually ships, and afterwards you are coached on what you accepted, what you rejected, and what you verified first.
Each stage runs like its own interview room, with its own format, its own flow, and its own scoring. Here's exactly what you walk into.
The interviewer asks real technical questions drawn from your chosen focus plus the shared fundamentals every interviewer expects. You answer the way you would in the room: speaking out loud, typing when it fits, and dropping into a code editor when a question calls for it.
Nothing is scripted. Every answer you give shapes the next follow-up, so a shallow answer gets probed and a strong answer gets pushed further. It's the closest thing to sitting across from an interviewer who actually listens.
How it runs
The interviewer asks a question from your focus area
You answer by voice, text, or code, whatever the question demands
Follow-ups dig into your answer like a real interviewer would
Difficulty adapts to how you're performing
What's scored
You get working-but-broken or incomplete code and a real test suite. Your job is to fix it or build it out while thinking aloud, because the interviewer is listening to your reasoning, not just watching the tests go green.
You can ask the interviewer clarifying questions at any point, exactly like a real pairing session. Silent coding that happens to pass scores lower than clear reasoning that shows how you got there.
How it runs
Read the ticket and the failing tests
Ask clarifying questions before you commit to an approach
Code against the real test suite while narrating your thinking
Defend your approach when the interviewer pushes back
What's scored
You bring a project you've actually built and walk the interviewer through it: what it does, how it's put together, and why you made the calls you made. Then the follow-ups start.
The interviewer probes your architecture, your tradeoffs, and the decisions you'd make differently today. Surface-level familiarity gets exposed fast, which is exactly why this stage exists.
How it runs
Introduce your project and what problem it solves
Walk through the architecture and key decisions
Defend your choices under deep follow-up questions
Own what you'd change and why
What's scored
You get a design prompt with real constraints and a canvas to design on. You clarify requirements first, then draw the system while narrating every choice out loud.
Once the design is on the canvas, the deep dive begins: scaling, failure modes, bottlenecks, and the tradeoffs behind every box you drew. A diagram you can't defend is just a picture.
How it runs
Clarify requirements and constraints before you draw
Design the system on the canvas while narrating
Walk through data flow, scaling, and failure modes
Defend your tradeoffs in the deep dive
What's scored
A teammate's pull request lands on your desk. You read the diff, leave line-by-line comments, and call the verdict: approve or request changes.
You're scored on what you catch, from real bugs to subtle risks, and on how you say it. A correct comment delivered badly costs you in real teams, and it costs you here too.
How it runs
Read the full diff before commenting
Leave line-by-line comments where it matters
Separate blocking issues from nitpicks
Call the verdict and justify it
What's scored
You build a small feature with an AI assistant at your side, the way modern teams actually work. The assistant helps, but you stay in control of what ships.
Afterwards you get coached on your judgment: what you accepted, what you rejected, what you verified before trusting it. Blindly shipping AI output is exactly what this stage is built to catch.
How it runs
Read the feature spec and plan your approach
Direct the AI assistant with clear, scoped prompts
Review and verify everything before you keep it
Ship a result you can fully explain
What's scored
Start by telling us which role you are preparing for, whether that is backend, frontend, data or another track. Your interview is drawn from the areas that role is actually assessed on.
Narrow things down to the specific area you want to be tested on. Your interview pulls questions from that focus, plus the shared fundamentals every interviewer expects you to know regardless of specialism.
Choose a single stage if you want to drill one weakness, or stack several to run back-to-back the way a real interview loop works. Stack them and you get one combined report covering the whole sitting rather than separate scores.
Every stage is scored on named dimensions with published anchors, then rolled into a readiness band recruiters understand. Cross-stage skills are tracked across everything you run.
You are still building towards interview standard, and the report tells you precisely which stage is costing you the most so you know where to put your practice time.
You are performing at the standard service and consultancy interviews actually ask for, and the report shows which areas would need lifting to reach product-company level.
You are performing at the standard product companies expect from an entry-level engineer, across both your technical answers and the way you communicate them.
The habits interviewers actually remember, tracked across every stage you run.
Finish a full sitting and earn a Plaicer Verified card: a public page carrying your readiness level and the role you sat it for, plus your overall score, your percentile and a breakdown by sub-domain. Share it to LinkedIn in one click, or have an educator endorse it.
Answer out loud where it counts. Your speech is transcribed by your browser's built-in speech service; we store text transcripts, not audio.
Paige reads your whole interview history, spots your focus areas, and works on them with you between sittings. Ask her what to focus on next, where you're weakest, or how to lift your system design stage. She coaches you; the interviewer stays in the room.
The room is ready when you are.
Before a company interviews you, most of them send a timed coding test first, and that is where the majority of candidates are filtered out. The DSA round is that test: four formats matched to real online assessments, hidden tests that only run on submission, and coaching on where your time actually went.
Four assessment formats from a 45-minute two-problem screen up to a 90-minute four-problem round.
Solve in Python, Java, C++ or JavaScript, with every problem verified in your language before you see it.
Scored on correctness, time management, edge-case discipline and code quality, then coached on each problem.
Java, Python, JavaScript, TypeScript, C++ and Node JS are covered today. Your Interview Q&A and Live Coding stages run in the language you choose, and every sitting also draws the fundamentals interviewers expect: SQL, DBMS and REST APIs. More languages are being added.
Yes. React, Next JS, Angular and Vue are covered on the frontend side, with Spring Boot and Django on the backend. Pick your framework track and the interviewer draws questions from it alongside the core language, the same way a real placement interview mixes both.
It is a full practice run of the interview a company would actually put you through, sat end to end rather than read about. An AI interviewer asks you questions, listens to your answers, and follows up on what you said. You speak, type and code your way through it, and at the end you get a written report on how you performed.
Interview Q&A covers technical questions with adaptive follow-ups. Live Coding gives you broken or unfinished code and a real test suite. Project Deep Dive has you defend a project you built yourself. System Design gives you a canvas and a design prompt. Code Review puts a teammate's pull request in front of you. AI Feature Build has you ship a small feature alongside an AI assistant while staying in control of the result.
No. Run a single stage if you want to drill one specific weakness, or stack several to run back-to-back the way a real interview loop works. Stack them and you get one combined report covering the whole sitting instead of separate scores.
For most stages, yes, and it is the point. Interviewers score how you explain your reasoning as much as what you produce, so silent work that happens to be correct scores lower than clear reasoning that shows how you got there. You can still type or drop into a code editor whenever a question calls for it.
It depends on how many stages you stack. A single stage is a short sitting; a full six-stage loop takes considerably longer and is closer to what a real onsite feels like. You choose the shape when you build the interview.
For the Project Deep Dive stage, yes. Bring something you have actually built, because the follow-ups go into your architecture, your tradeoffs and what you would do differently now. Surface-level familiarity with someone else's project gets exposed quickly. The other five stages need nothing from you in advance.
Each stage is scored on its own criteria, and every stage also scores the skills that cut across all of them: communication, question-asking, independence and depth under probing. You get written coaching on each stage rather than a bare number.
Three bands that translate your score into something useful. Developing means you are still building towards interview standard and the report names which stage is costing you most. Service-ready means you are performing at the level service and consultancy interviews ask for. Product-ready means you are at the standard product companies expect from an entry-level engineer.
Yes. Every interview you sit is kept with its scores and feedback, so you can see which parts are improving across attempts and which are not moving.
A friend cannot easily keep pushing when your answer is thin, and will rarely tell you honestly that your communication was the weak part. The interviewer here adapts to your answers, probes shallow ones, and the report separates your technical accuracy from how well you explained it, which is usually the more useful signal.