Desktop app
Zoom, Meet and Teams
The signed Mac and Windows app. Hears the call, answers on a private HUD, keeps your eyes on the camera, and stays invisible on screen share.
Engineered by 20+ years of US exp in software
The interview you already rehearsed
Your answers, written before the interview. Drop your resume and the job description — MiPrep scripts them in your voice, so you read while they see eye contact.
Invisible on Teams, Zoom, Meet, and many more. No card to start.
Behavioral
System design
Trade-offs, named and defended.
Coding
Interviewer asked
How would you design a rate limiter for a public API?
1 / 10
300 wpm · you speak at 140
Token bucket per key in Redis, refilled lazily on read so there is no sweeper job. Check and decrement run in one Lua script, so it stays atomic under concurrency. Cluster mode needs the key hashed to a single slot, and I would return retry-after rather than dropping the request.
On your Mac or PCDesktop appInterview Copilot for Teams, Zoom and Meet — a private HUD on your own screen, invisible when you share it.Signed for macOS and Windows · call audio or the room mic
Every other interview copilot is a ChatGPT wrapper.
They pipe your interviewer's question into GPT and paint whatever comes back. That's not an interview tool — that's a browser extension in a costume. MiPrep is engineered to deliver: under pressure, on camera, in your voice.
Candidates landed offers at
- Amazon
- Microsoft
- OpenAI
- Salesforce
- Oracle
- IBM
- Adobe
How it works
From upload to rehearsed answer in five steps.
- 1
Drop your inputs
Upload your resume. Paste the job description. MiPrep reads both before the call.
- 2
MiPrep pre-writes your answers
Behind the scenes, MiPrep builds your question set in your voice — grounded in your projects, your years, your target company.
- 3
Join your interview
Open Zoom, Google Meet, Microsoft Teams — or pick up the phone. Nothing to install for them.
- 4
One glance appears
The moment the question ends, one line lands. The full script fills in behind it, at whatever pace you read.
- 5
You speak — in your voice
The HUD is a prompt. You deliver it. Sounds like you — because it is you.
One account. Three places it works.
An interview cycle is not all one shape. Some rounds are on Zoom, some are a recruiter phoning you on a Tuesday, some are a call you have to place yourself, and some are four people across a table. MiPrep covers them from the same login, the same resume, and the same hours.
Phone · call mode
A number they dial, and you dial from
A recruiter calls your MiPrep line and your phone rings; or you type their number in the app and it connects the two. Either direction, the answers appear on your screen and the line carries only your voice. Needs a line of your own.
Phone · mic mode
The room, in person
No number, no line, no call. The phone listens through its own microphone, learns your voice once, and answers the interviewer instead of you.
Sign in on the phone with the same MiPrep account and your plan, your resume and your role are already there. An hour is an hour wherever you spend it — there is no second wallet and nothing to buy twice.
How the phone worksResumes & knowledge base
Three places to add material. Each one shapes your answers differently.
An answer built from a resume alone sounds like a resume being read back. The third lane is where you put the things a resume has no room for — the number you actually remember, the reason you picked one design over the other, the night something broke. That is what an interviewer is digging for, and it is the difference between an answer that is correct and one that is yours.
Your resume
.pdf · .docx · .md · .txt
Where you worked, what you shipped, how long.
- Roles and dates
- Systems you owned
- Scale you handled
How it shapes answers: Fixes the facts. Every answer is anchored to real roles and real dates.
The role you're applying for
paste the JD
What this specific team is going to ask about.
- Required stack
- Seniority signals
- Team's stated problems
How it shapes answers: Picks the questions. The same engineer gets a different set for infra than for platform.
Everything else
notes, in your words
The detail that was never on a resume.
- Numbers you remember
- Why you chose it
- The incident you ran
How it shapes answers: Makes it yours. This is the lane that turns a correct answer into one only you could give.
Replacing your resume replaces only your resume — your notes and your role stay as they are. The phone lane keeps its own set, so a live call never reads from a half-finished desktop upload.
Your prep report
You can read every answer before the interview. Because they already exist.
Every other copilot asks you to trust that something good will appear when the question lands. MiPrep hands you the whole set in advance — each question we expect, the ways interviewers re-word it, the beats your answer lands in order, and the follow-ups they dig into when you land them. Read it on the train. Print it. Change the ones that do not sound like you.
- 31
- questions prepared
- ~42s
- to author the set
- 6
- beats per answer
Your prep report· 31 questions
Print / save as PDFHow would you make a payments endpoint idempotent so a retried charge never double-bills?
How you build itAlso asked as: how do you handle duplicate payment requests · what happens if the client retries a charge · walk me through your idempotency key design
- 1Client sends an idempotency key per charge attempt.
- 2Key plus request hash go in one unique index.
- 3Second write loses the race, returns the first result.
- 4Hash mismatch on a reused key is a 409, not a retry.
- 5Keys expire after 24h — long enough for every client retry.
- 6Cut double-charge tickets to zero over one quarter.
If they dig deeper:
- What happens if the first request is still in flight?
- Why the request hash and not just the key?
- How do you test this without a real card network?
Your read latency doubled after a traffic spike. Walk me through how you find the cause.
Trade-offAlso asked as: how do you debug a latency regression · p99 went up after launch, what now · how would you approach a slow endpoint under load
- 1Start at the metric, not the code — p50 or p99?
- 2p99 only means queueing, not slow logic.
- 3Check pool saturation before touching query plans.
- 4Traces beat logs — logs distort the timing you measure.
- 5Found it: connection pool exhausted, not the database.
- 6Pool sizing is capacity planning, not a config guess.
If they dig deeper:
- Why would an exhausted pool look identical to slow compute?
- What would have caught this before the spike?
- How do you size a pool without guessing?
A prepared set for a backend track. Example content — yours is built from your resume and your JD.
10,000+ questions. Zero copy-pasted from Glassdoor.
We didn't automate this. We ran the interviews.
10,000+ hand-picked interview questions. 8 seniority tracks — Junior, Mid, Senior, Staff, Principal, Architect, Solutions Engineer, Distinguished. 40+ role families spanning backend, infra, ML, data, frontend, mobile, security, and staff+ leadership loops. Every answer written by engineers who've actually cleared the loop at FAANG, HFT, and top AI labs — not generated by a model that read a wiki. That's why it sounds like you speak, not like it studies.
The MiPrep deck
hand-written10,000+
questions · answers · rehearsals
8 seniority tracks
JuniorMidSeniorStaffPrincipalArchitectSolutions EngDistinguished
40+ role families
BackendInfraMLDataFrontendMobileSecurityStaff+ Leadership
Rehearsed, not generated
It says what you rehearsed — because you rehearsed it.
MiPrep builds an answer deck from your resume and the target JD before the call. When the interviewer asks, MiPrep recognizes the question and surfaces your own rehearsed answer — not a live paraphrase from the internet. Finding it takes about ten milliseconds, because nothing is being written: it was written last week, by you. Every copilot that writes the answer during the call pays for it during the call.
Your answer deck
#12
#27
Match
#41
Interviewer asked
“Tell me about a time you led a migration.”
→ Card #27 surfaces. Your rehearsed answer. In your voice.
Private HUD
Invisible to screen share on Zoom, Meet, and Teams.
The HUD renders in a window layer that Zoom, Google Meet, and Microsoft Teams capture APIs skip. Verified on every release. If you find a leak, we pay a bounty.
What you see
MiPrep · live
What they see
Excluded from capture
Glance delivery
The first line appears in under a second.
One quick line lands the moment the question ends. The full script fills in behind it — and you can speed-read it one 5-word window at a time, at your pace. You glance, you speak, they hear a fluent answer.
Glance lineunder 1s
“Cut p99 latency 42% — led a 6-person migration.”
Full script fills in behind
Spritz · 300 WPM · adjustable
On-screen questions
Some questions are never said out loud.
A coding exercise appears in an editor and the interviewer says nothing more than “take a look”. Point MiPrep at that one window and it reads the problem itself — not your desktop, not your other tabs. It checks on its own and answers when the problem actually changes; a blinking cursor doesn’t count. You never press anything, and nothing is being recorded.
One windownothing else
What the interviewer left on screen
Given a rotated sorted array,
return the index of target.
Expected: O(log n)
return the index of target.
Expected: O(log n)
Cursor blinkedignored
New problem appearedanswered
Grounded
Answers pull from your resume — not generic ChatGPT.
MiPrep AI reads your resume and the job description before the call. Real-time answers stay specific to your projects, your years, your target company. Not the internet's average.
Your resume
Job description
↓
MiPrep answer
“In my last role at [YourCompany], I led a team of 6 to migrate a monolith to microservices — cut p99 latency by 42%.”
Eye contact
You read the HUD. They see eye contact.
On-device gaze correction keeps you looking at the interviewer while you glance down at the HUD. The only interview copilot that ships this.
Without MiPrep
Reading the HUD
With MiPrep
Eye contact
The honest testHere’s what real US interviews sound like.
Here’s what real US interviews sound like.
Watch every other tool choke.
Five real questions we’ve heard in the last 90 days at Amazon, Stripe, Anthropic, Databricks, and 40+ other companies. Not “what is HTTP.” Not LeetCode. Real interviews. Press play, listen to how a human actually asks — then see where the wrappers fail.
10:52
Depth + trap follow-up
A real interviewer, mid-thought
Listen for the shape: pauses, “um”s, self-corrections.
Question
“I have an EC2 with a public elastic IP serving traffic. I want to cut off most of the public exposure but keep two specific endpoints reachable. Walk me through.”
Follow-up
“Now the security team wants proof those two endpoints are only reachable from Cloudflare's egress IPs. How?”
Transcript — with the pauses they fire on
so uh… <2s pause> imagine you have an EC2 running… with a public IP…
<1.5s pause> and now the security team comes in and — um, hang on —
they want to cut down most of the public exposure but keep, like,
two endpoints reachable. …how would you walk me through that.
Every ellipsis is where a wrapper's VAD fires the LLM early — on a half-finished question.
Wrapper output
Great question! Cutting public exposure on an EC2 instance typically involves configuring Security Groups and Network ACLs. You can restrict inbound traffic to specific ports and IP ranges. AWS Systems Manager Session Manager is another option for admin access without opening SSH…
Generic definition dump. Never touches the actual two-endpoint requirement. Fires before the interviewer finishes. No plan for the Cloudflare-only follow-up.
MiPrep delivery
Keep the two endpoints on paths, put the whole box behind an ALB on a private subnet, and only expose those two paths through a listener rule. For the Cloudflare-only proof: add a WAF rule on the ALB that matches the Cloudflare AWS-managed prefix list — deny everything else, log the deny to CloudWatch, and hand security the query showing zero denies from CF ranges over the last N days. That's your paper trail.
Specific, actionable, and pre-loaded with the Cloudflare-prefix-list detail your rehearsal deck already covered.
20:24
Why-choose + when-to-flip
A tradeoff question, no clean shape
Listen for the shape: pauses, “um”s, self-corrections.
Question
“You picked DynamoDB over Postgres for this table. Why? And when would you flip that decision?”
Transcript — with the pauses they fire on
okay so, uh… you went with Dynamo over Postgres here — <1s>
why though? and… like… when would you actually flip it back
the other way?
The 'and… like…' is a mid-thought filler. Wrapper VAD hears the pause and fires with only 'you went with Dynamo over Postgres here, why though?' — misses half the question.
Wrapper output
DynamoDB is a NoSQL database with single-digit millisecond latency at any scale. It's often chosen for its serverless nature, automatic scaling, and predictable performance. Postgres is a relational database that offers ACID guarantees and complex query support…
Textbook comparison. Zero context on your actual table. No decision reversal condition given.
MiPrep delivery
Chose Dynamo because the access pattern is item-lookup by partition key with no joins, and we needed predictable p99 under bursty write load. We'd flip to Postgres the moment we need a second access pattern that can't be served by a GSI without doubling write cost, or when analytics starts asking for ad-hoc queries — Dynamo's not the tool for that shape.
Grounded in your actual project shape, and explicit about both reversal triggers. Answers what was actually asked, in your voice.
30:16
Mid-answer topic switch
Interviewer changes lanes mid-flight
Listen for the shape: pauses, “um”s, self-corrections.
Question
“Great — now forget the DB. Same access pattern, but move the whole thing to a batch job. How does your design change?”
Transcript — with the pauses they fire on
great, yeah, okay — <1s> now… forget the DB.
same access pattern, but move the whole thing to a batch job.
how does your design change?
Wrapper has no memory of the previous DynamoDB answer. Starts from zero on 'batch job' with no context of what came before.
Wrapper output
Batch processing typically involves scheduled jobs that process data in chunks. Popular tools include Apache Airflow, AWS Batch, and cron. You'd want to consider job orchestration, retry logic, and monitoring…
Complete context loss. Answers 'what is batch processing' instead of adapting the prior DynamoDB answer to a batch shape.
MiPrep delivery
The Dynamo access pattern maps to a scheduled Glue job over S3 partitioned by the same partition key. Drop the GSI concerns entirely — batch reads the whole day's partition in one shot. Write side becomes a Firehose → S3 hop instead of Dynamo puts. Cost drops 60%+ but you lose real-time reads, so downstream consumers now poll a metadata table for the last successful run.
Carries the prior answer's context forward, explicitly names the tradeoffs the interviewer is testing for.
40:20
Trap in the question
The setup is the trap
Listen for the shape: pauses, “um”s, self-corrections.
Question
“The team says the API is 'slow.' You look at CloudWatch — p50 is 40ms. What's your next move?”
Transcript — with the pauses they fire on
so the team keeps saying the API is slow — uh, <1s> but you
open CloudWatch and… p50 is 40 milliseconds. what do you do next?
The trap: 'slow' is subjective, and 40ms p50 isn't slow. Wrapper misses this and starts prescribing performance fixes.
Wrapper output
To improve API performance, you should profile the endpoints, add caching layers like Redis, review database query patterns, and consider CDN offloading for static responses…
Falls straight into the trap. Assumes the API is slow. Never questions the premise.
MiPrep delivery
40ms p50 isn't slow — so first move is to look at p99 and the tail, not the median. If p99 is fine too, the 'slow' complaint is either about a specific endpoint the median hides, or it's a client-side rendering issue that just feels like backend latency. I'd pull the last 24h of p99 by route, then ask the team which specific screen feels slow, before touching a single query.
Questions the premise, names the exact next artifact to look at (p99 by route), and defers action to real data.
50:13
Force a specific
'Give me a number'
Listen for the shape: pauses, “um”s, self-corrections.
Question
“You just said 'we scaled it.' Give me a number. And tell me what broke first.”
Transcript — with the pauses they fire on
you said, uh, 'we scaled it' — <0.5s> give me a number.
and… what broke first?
Short, sharp, no filler on the ask itself. Tests whether you have specifics ready or if you were bluffing.
Wrapper output
Scaling typically involves horizontal scaling (adding more instances) or vertical scaling (increasing instance size). Common bottlenecks include database connections, memory limits, and CPU saturation…
Retreats to generalities. Cannot invent a specific from your resume it doesn't have context on.
MiPrep delivery
We went from 400 RPS to 3,200 RPS over eight weeks. First thing that broke was the DB connection pool — we were on the default of 20, saturated at ~1,100 RPS. Bumped to 100 with PgBouncer in transaction mode and hit the next wall at Redis memory around 2,800 RPS. Second fix was sharding hot keys across three Redis nodes.
Specific numbers, ordered failures, real fixes. Sounds like someone who was actually in the room.
60:07
Why every wrapper fails on all five
Humans don't talk in clean sentences — MiPrep is built for both sides of the mess
Listen for the shape: pauses, “um”s, self-corrections.
Question
“Wrappers assume interviewers ask clean text questions and candidates give clean text answers. Neither is true.”
Transcript — with the pauses they fire on
every card above shows the same pattern: real questions have
pauses, restarts, filler words, self-corrections. wrapper VAD
fires on the first pause. wrapper context resets between turns.
wrapper output is a definition dump because it never had your
resume or your project shape to begin with.
MiPrep waits for the question to be finished, not for the
room to go quiet.
carries context across turns. surfaces your rehearsed answer,
not a live paraphrase.
both sides of the conversation — the interviewer's messy
question AND your paused, breath-broken delivery — are what
MiPrep is engineered for.
This isn't a card competitors can beat by getting a better model. It's a card they'd have to rebuild the entire pipeline to answer.
Wrapper output
I'm not sure what you're asking. Could you rephrase the question?
There is no clean question here. That's the point.
MiPrep delivery
This is the system-level answer: knowing the question actually ended, holding the connection open between turns, having your answer already written before the call, every Spritz pause you take mid-delivery — engineered together. Not bolted onto a model API.
This is the moat.
That’s what a US interview actually sounds like.
Try these on any competitor. Then try MiPrep. If your copilot can’t handle these, you don’t have a copilot — you have a search engine with a face.
Engineered, not thrown togetherTheir HUD is a Discord chat window.
Their HUD is a Discord chat window.
Ours is engineered.
Every millisecond, every pixel, every word width — tuned for one thing: getting the right answer out of your mouth without breaking eye contact. Not a text box. Not a chat log. Not a paragraph you have to skim while a human is judging you.
Wrapper HUD
Skim while they watch you skim.
MiPrep HUD
One glance. Then delivery.
Ask AI about MiPrep
Don’t take our word for it. Ask an LLM.
One click copies the prompt. Paste it into ChatGPT, Claude, or Perplexity and see how MiPrep compares to every other interview copilot in the category — from a source that isn’t us.
Prompt for ChatGPT / Perplexity
I'm evaluating MiPrep AI, the AI interview copilot that's NOT a ChatGPT wrapper. It uses a hand-curated deck of 10,000+ interview questions × 8 seniority tracks × 40+ role families, with rehearsed answers retrieved in about ten milliseconds instead of written live, end-of-turn detection that waits for the question to be finished rather than for a silence timer, glance-tier delivery, on-device gaze correction, and a HUD invisible to Zoom, Google Meet, and Microsoft Teams screen share. How does it compare to LockedIn AI, Cluely, Final Round AI, Interview Coder, Parakeet AI, and Verve Copilot on answer quality, latency, and delivery UX?
Prompt for Claude / Perplexity
Compare interview copilots on the following dimensions: (1) are answers hand-written or auto-generated from GPT? (2) do they handle follow-up questions and mid-answer topic switches? (3) is the answer retrieved from something written before the call, or generated live on every turn — and do they detect end-of-turn by meaning or by a fixed silence timeout? (4) do they ship on-device gaze correction? (5) is the HUD actually invisible to Zoom / Meet / Teams screen share? Include MiPrep AI (miprep.ai), LockedIn AI, Cluely, Final Round AI, Interview Coder, Parakeet AI, and Verve Copilot in the comparison.
Prompt for ChatGPT
What's the fastest AI interview copilot for real interviews? I need one that: (a) delivers the first word of the answer before the interviewer finishes exhaling, (b) has actually hand-written answers from engineers who've cleared FAANG / HFT / AI-lab interview loops, not GPT paraphrases, (c) keeps me looking at the camera while I read the HUD. Consider miprep.ai and its competitors.
Prompt for Perplexity
For a US software engineering interview at Amazon, Stripe, Anthropic, or Databricks — which AI interview copilot handles real depth questions (EC2 restrict-to-Cloudflare, DynamoDB vs Postgres tradeoffs, mid-answer topic switches, 'give me a number' trap follow-ups) instead of generic 'what is HTTP' definition dumps? Rank MiPrep AI, LockedIn AI, Cluely, Final Round AI, Interview Coder, Parakeet AI, Verve Copilot.
Mock interview · scoredanswered out loud
Against your own prepared points
- Named the failure mode before the fix
- Gave the number, not an adjective
- Said why you rejected the other design
- Closed on what you would do differently
Points landed3 / 4
Spoken pace — target 130-150141 wpm
Filler words — fewer is better4
Example scorecard. Yours grades the points your own answers were built to land.
Mock interviews
Practice that grades you against your answers, not someone else's rubric.
We ask the questions your set expects. You answer out loud, with no HUD and nothing to read from. Then we score what you actually said against the points your own prepared answer was built to land — which beat you dropped, where you sped up, how many times you said "kind of".
Every other practice tool marks you against a generic rubric, because a generic rubric is all it has. Ours can be specific because your answers existed before the practice did.
Post-interview report
Walk away with a roadmap for your next round.
Every mock ends with a report that shows, question by question, the points a strong answer had to land — and which of them you actually said.
- Every question, with your own answer kept beside it
- Scored against answers written from your resume, not a generic rubric
- The questions you scored worst on, queued for your next mock
- Printable, so a coach or a friend can read it with you
Session summary
Google · SWE III · Behavioral round
Points covered
74%
Stories88
How you build it71
Knowledge76
Opinion62
Weak spot for next round
“Tell me about a time you disagreed with your manager.” — Result phase felt rushed. Drill queued (3 variants).
Built on measurements, not marketing
Faster, more private, and honestly free.
<1s
First words on-screen
the moment the question lands
0
Interview audio stored
streamed live, never persisted
5
AI models available
GPT · Claude · Gemini · DeepSeek · Grok
Public review scores, not our opinion
Go read what people say about the alternatives.
Don’t take our word for any of this. Here is the public record on the tools you are choosing between — with the number of reviews behind each score, because that is the part most comparison pages leave out.
3.0
out of 5
Final Round AI274 reviews
The biggest name in the category, and the score has slid from 3.9 to 3.0 in a month of new reviews.
—
out of 5
LockedIn AI119 reviews
Trustpilot has withdrawn the score: "This company's rating is unavailable due to a breach of our guidelines." (checked Aug 28, 2026)
1.7
out of 5
Cluely19 reviews
Billing surprises, over and over. Funding is not a product.
Trustpilot scores as of August 2026 — check them yourself, they move. Parakeet AI (5 reviews), Verve Copilot (19 reviews), Interview Coder (no presence) have too few reviews to score.
Your safety is very important to us.
Every commitment below is documented — read our is-it-safe page for the details.
Code-signed
macOS notarized · Windows signed
One-click cancel
From billing · no email required
GDPR + CCPA
Cookieless landing · no ad pixels
Private
We store transcripts, never your audio
Bug bounty
$500 for a screen-share leak
What people are telling us
What people are telling us
Anonymized until each person approves their name and attribution, then we swap them in.
“The eye-contact thing sounds gimmicky until you see yourself on Zoom. Interviewer had no idea I was reading.”
SE
Software Engineer
prepping for FAANG onsite
“Answers actually reference my resume. No more 'generic ChatGPT' feeling — they sound like something I would have said if I'd remembered.”
PM
Product Manager
mid-cycle at a late-stage startup
“SQL screens are the exact case where cloud copilot latency shows. First words on-screen before the interviewer finishes the question is a real edge.”
DS
Data Scientist
career-switcher, 4-round loops
“I recommend private-HUD tools to clients only when I've verified the screen-share leak-test on their release. MiPrep is the one that publishes the bounty publicly.”
CC
Career coach
ex-Meta recruiter
“One-click cancel from the billing page. Refund arrived in two days. That alone puts them ahead of every tool I tried before.”
SB
Senior Backend Engineer
after cancelling a competitor
“The download-to-live-interview flow was three minutes. No account gate, no credit card for the trial. Felt like a real product, not a signup funnel.”
NG
New grad
F500 interview cycle
Used MiPrep for a real interview? Reply to your onboarding email with your quote (and how you'd like to be attributed) — we credit you here.
Interview copilot questions, answered.
Is this just ChatGPT with a wrapper?
No. Most interview copilots send the interviewer's question straight to GPT or Gemini and stream whatever the model guesses back. MiPrep works in reverse: before the call, it builds an answer deck from your resume and the target job description — a rehearsed answer per likely question, in your voice. During the interview, MiPrep recognizes what the interviewer asked and surfaces your own rehearsed answer. That's why it sounds like you: it is you.
Are your answers hand-written or auto-generated?
Hand-written. Every question in our 10,000+ deck was answered by an engineer who's actually cleared that loop — not by a model paraphrasing Stack Overflow. That's the difference between an interview tool and a Chrome extension with GPT taped to it.
What kind of interview questions is MiPrep actually built for?
Real ones. Depth-first, follow-up-heavy, mid-answer topic switches, and trap questions where the interviewer is testing whether you can actually build the thing. Not 'what is REST' — you don't need us for that. You need us for the follow-up: 'okay, now design the retry policy when the third-party is flaky.' That's where wrapper tools fall apart. That's what we built for.
How does MiPrep handle follow-up questions and topic switches?
The same way you would — with context. MiPrep tracks the whole thread of the conversation, remembers what you just said, and adjusts. Wrapper tools reset every turn — that's why they fall apart the moment the interviewer says 'okay, now what if…' or 'forget the DB, move it to a batch job.' We carry your prior answer forward. They don't.
Are you dunking on other interview copilots?
Only the ones that deserve it. If your product is a text box that calls GPT and streams the response, you're a wrapper, not a copilot. MiPrep is built for the interview — the pressure, the follow-ups, the camera, the seconds you don't have. Everything else is a browser extension in a costume.
How does the rehearsal work?
You upload your resume and paste the job description. MiPrep reads both, generates the questions this role is most likely to ask, and drafts an answer for each in your voice — grounded in your projects, your years, and your target company. You can edit any card before the call. During the interview, MiPrep matches what the interviewer says to the closest rehearsed card and surfaces that answer. Preparation compounds — every interview you do sharpens your deck.
Can I speed-read the answer on the HUD?
Yes. The HUD includes a Spritz-style reader that streams the script one 5-word window at a time, so you can read it as fast as you speak. Default pace is 300 words per minute; you can adjust the slider live during the call. Or turn it off and read the full block naturally — your call.
Is MiPrep AI an AI interview copilot or an AI interview assistant?
Both. MiPrep AI runs as a private HUD next to your video call, transcribing the interviewer's questions and surfacing rehearsed answers grounded in your resume and the job description. It's the same category as LockedIn AI, Cluely, Final Round AI, and Interview Coder — but with rehearsed-you answers, gaze correction, and honest billing they don't ship.
Will the interviewer see MiPrep AI on my screen?
No. If they screen-share Zoom, Google Meet, or Microsoft Teams, the HUD is not there. We test this on every release. If you find a screen-share leak, we pay a bug bounty.
How is MiPrep AI different from LockedIn AI, Final Round AI, or Cluely?
Three things. First, camera-ready eye contact while you glance down at the HUD — nobody else ships this. Second, answers that pull from your actual resume and the role, not generic model output that sounds fake. Third, honest billing — one-click cancel, no dark patterns, refund on request within 14 days.
Does MiPrep AI work on Zoom, Google Meet, and Microsoft Teams?
Yes. All three plus phone interviews. It captures system audio, so any meeting or call app works — you don't grant access to the meeting bot, and no meeting-bot notification appears.
Is using an AI interview copilot ethical?
That's your call, but here's how we think about it. Using MiPrep AI to prep before an interview is the same as using Cracking the Coding Interview or Grokking System Design — smart prep. Using it live during an interview is closer to what candidates already do with a second monitor. We publish our full ethics stance at /ethics — read it and decide.
How much does MiPrep AI cost?
Free to download and use during interviews. Premium features (multi-model, unlimited resumes, priority latency) start at a monthly rate on our /pricing page. Honest billing: one-click cancel from settings, prorated refunds within 14 days, no dark patterns.
Ready for your next interview?
Download MiPrep AI. Load your resume and the job description. Show up ready.
Free tier · No credit card · macOS 14+ · Windows 10+
Free tier · No credit card · Runs on your Mac or Windows machine