Pricing
Pay for interviews, not promises.
Everything here buys the same thing: an interview with Sarah, up to 30 minutes, report included. Your first is free. After that, pay for one, buy a pack that never expires, or subscribe for the lowest price.
All prices include GST. UPI, cards and net banking via Razorpay.
- Per interview₹299
- Warm-up pack₹250
- Placement prep pack₹200
- Drill pack₹180
- Starter plan₹225
- Pro plan₹200
Prices from the current catalog. Pay per interview at ₹299 needs no top-up; packs and plans bring it down.
1 interview free, and 1 interview more for every friend.
- 1 interview on signup: a full interview of up to 30 minutes, and its report.
- +1 interview each time a friend signs up with your link and completes their first interview, up to 10 friends.
- No card to start. Referral interviews never expire.
Sitting for placements? Ask and it's free.
Confidash is still early, and right now your feedback is worth more to me than your money. Tell me your college and when your drives start, and I'll top up your account.
Three ways to pay.
Same interview, same Sarah. Only the price per interview and how long they last change.
The lowest price per interview. They land at the start of every cycle and reset when the next one starts. Cancel any time from Billing.
Starter
Two interviews a month
₹449/month
2 interviews · ₹225/interview
- 2 interviews every month
- All interview types
- Full reports and transcripts
- Leaderboard
Pro
An interview every week
₹999/month
5 interviews · ₹200/interview
- 5 interviews every month
- Priority evaluation
- All interview types
- Full reports and transcripts
- Leaderboard
Buy once, use across the whole placement season. Pack interviews never expire, and the bigger the pack, the cheaper each interview.
Warm-up
A few rounds before the big day
₹749
3 interviews · ₹250/interview
Placement prep
Most popularA full season of practice
₹1,999
10 interviews · ₹200/interview
Drill
For serious repetition
₹4,499
25 interviews · ₹180/interview
₹299 for one interview, no commitment. One interview covers any length below and costs the same whichever you pick.
- 5 minA quick warm-up₹299Start 5 min
- 15 minOne quick round₹299Start 15 min
- 25 minA standard panel round₹299Start 25 min
- 30 minA full technical round₹299Start 30 min
Real-time conversational voice: interruptions, follow-ups, the closest to a live panel.
Read the transcript
- SarahHi John, this is Sarah from the interview. This is a general round, about five minutes, and we’ll start with a brief introduction. Tell me a bit about yourself, your background, interests, and what you’ve been building lately.
- CandidateHi Sarah, I’m John Doe, a final-year Computer Science student at Sunrise Institute of Technology, where I’ve maintained an 8.4 CGPA. I’m passionate about full-stack development and have recently worked on CampusConnect, a networking platform for students, where I led the development using Next.js and Node.js. I also developed LeafScan, a plant-disease detection web app utilizing machine learning with TensorFlow. In my recent internship at BrightApps, I focused on optimizing performance and implementing CI checks for React and Node dashboards.
- SarahOkay, let’s focus on CampusConnect since you mentioned it. Out of curiosity, what parts did you build yourself from scratch, and what was the hardest bug you ran into while implementing it?
- CandidateIn CampusConnect, I built the entire user authentication system and the real-time chat feature from scratch using Socket.io. The hardest bug I encountered was related to message synchronization, where messages were not consistently appearing in real-time for all users. After extensive debugging, I discovered an issue with how we were managing WebSocket connections, which I resolved by refining our event handling logic to ensure messages transmitted correctly across all clients.
- SarahGot it. You mentioned fixing message synchronization and the WebSocket connection handling. What exactly did you change in that event handling logic, and how did you verify that all clients were consistently seeing identical messages?
- CandidateI modified the event handling logic to ensure that each message sent from a user was acknowledged by the server before broadcasting it to other connected clients. I implemented an event confirmation system that allowed the server to track which messages had been sent and received. To verify consistency, I set up automated tests that simulated multiple users sending and receiving messages simultaneously, and I monitored the logs to ensure that all clients displayed identical messages in real-time. This thorough testing confirmed that our synchronization issue was resolved.
- SarahNice, that sounds solid. Let’s switch topics. For LeafScan, you mentioned a CNN and TensorFlow. How did you train and evaluate the model, and what kind of accuracy or confidence did you achieve before you felt comfortable shipping it?
- CandidateFor LeafScan, I trained the CNN model using a dataset of plant images labeled with various diseases. I used TensorFlow to build and optimize the model, experimenting with different architectures and hyperparameters. After multiple training iterations, I achieved an accuracy of around 92% on the validation set. I felt comfortable shipping the model after extensive evaluation, including cross-validation and confusion matrix analysis, which confirmed its ability to generalize well across unseen data.
- SarahOkay, 92 percent, nice. But what dataset did you use, and how did you define your evaluation? For example, what was the validation set size, and what did your confusion matrix show about specific classes the model struggled with?
- CandidateFor LeafScan, I used the PlantVillage dataset, which contains thousands of labeled images of different plant diseases. I split the dataset into 80% for training and 20% for validation, ensuring a diverse representation of classes. The confusion matrix revealed that the model struggled particularly with a few classes, such as bacterial blight and leaf spot, where it had higher false-negative rates. This insight led me to augment the training data for those specific classes to improve overall performance.
- SarahThat’s a good detail. When you say you augmented the data for those specific classes, what kind of augmentation did you apply? Was it rotations, scaling, or something else? And how did you confirm it actually helped, not just increased accuracy?
- CandidateI applied several augmentation techniques, including rotations, horizontal flips, and slight zooming, to increase the diversity of the training data for the underperforming classes. To confirm that the augmentation helped, I conducted a controlled experiment by training the model on both the original dataset and the augmented dataset separately. I then compared their performance on the validation set, and the augmented model showed a noticeable reduction in false negatives for the problematic classes, as well as an overall increase in accuracy, indicating that the data augmentation was beneficial.
- SarahOkay, thanks for that. Let’s pivot to your internship. In BrightApps, you mentioned cutting a slow report query from four seconds to three hundred milliseconds. What exactly did you change, and how did you measure the improvement?
- CandidateTo optimize the report query at BrightApps, I analyzed the SQL execution plan and identified several inefficiencies, including missing indexes and unnecessary joins. I added appropriate indexes and restructured the query to reduce complexity, which significantly improved performance. I measured the improvement by running the query multiple times before and after the changes, using a performance monitoring tool to track execution time, confirming the reduction from four seconds to three hundred milliseconds consistently.
- SarahOkay. That’s clear. Let’s move to your internship again. You also mentioned adding CI checks that caught regressions before release. How did you design those checks, and what kind of regressions did they catch?
- CandidateI designed the CI checks by implementing automated testing for both unit tests and integration tests within our CI pipeline using tools like Jest and Cypress. These tests covered critical functionalities, including user interactions and API responses. The checks caught several regressions, including broken UI components after updates and issues with API endpoints returning incorrect data, preventing these errors from reaching production and ensuring a smoother release process.
However you pay, this is the interview. The candidate answering is a scripted test profile, not a real student.
Which one?
- One interview before the big day
- Pay per interview₹299 for one interview, any length up to 30 minutes. No top-up, nothing left over.
- A season of practice on your own schedule
- An interview packCheaper per interview, and pack interviews never expire. Use them this month or after the semester.
- An interview every week until placements
- A monthly planThe lowest price per interview. Starter is two interviews a month; Pro is an interview every week.
Pricing questions.
Is GST included?
Yes. Every price on this page includes 18% GST. Nothing is added at checkout.
Do interviews expire?
Pack interviews and referral interviews never expire. Plan interviews are for the month: they reset when the plan renews and do not roll over.
What if I end an interview early?
One interview covers any length you pick (5, 15, 25 or 30 minutes) and costs one interview either way, so give yourself the full 30 minutes when you can. Leave before you answer anything and the interview goes back to your balance.
Can I cancel a plan?
Any time, from Billing. The plan stays active until the end of the month you have paid for, and nothing is charged after that.
How do I pay?
Through Razorpay: UPI, credit and debit cards, and net banking.
Is there a free option?
Yes. Every account starts with 1 interview free, enough for one full interview and its report. Each friend who signs up with your link and finishes their first interview adds 1 interview more, up to 10 friends.
Students: tell me about yourself and I'll unlock it.
No payment, no card. I read every one of these myself. Use the same email you signed up with so I can find your account.
The first interview is on us.
Do one interview free, read the report, then decide how much practice you want.