Direct evidence first
The agent starts with first-hand interview reports, engineering blogs, job descriptions, and public talks. Questions stay tied to source links so you can inspect the evidence yourself.
AI gathers your
interview resources. For under $0.50.
<$0.50
typical report
~3 minutes
typical runtime
Evidence links
on every question
How it works
From company name to evidence-backed questions
Four steps, one AI agent, about three minutes.
01 · Target
Name your target
Type the company you're interviewing with; that's all we really need. Add an interviewer to factor in their public talks and writing, then pick the rounds you care about: coding, system design, behavioral, or the whole loop.
Target
The company and role you are interviewing for, helps find relevant questions.
Company *
Company URL · preferred
https://google.com
Role / level · optional
Software Engineer, Full Stack
Years of Experience · optional
e.g. 2-4
Team / org · optional
Core
Location · optional
Bengaluru, Karnataka, India
Tech Stack · optional
Java, Python, Go, TypeScript, Angular
Job Description · optional
Full stack development across back-end (Java, Python, Golang, C++) and front-end (JavaScript, TypeScript, Angular). The Core team builds the technical foundation behind Google's flagship products.
Recruiter notes · optional
Recruiter screen done. Next: coding rounds on DSA, then a system design round.
Interviewers · optional
Komal Tanwani · Recruiter
linkedin.com/in/komaltanwani
Rounds to Gather
You can add more rounds later, from the finished report.
Algorithmic CodingSystem DesignDomain QuizTake-home ProjectPair ProgrammingBehavioralHR / CultureAdd round
Report Sections
Drop what you already know, you are only charged for what the run researches.
The companyThe loopSkills requiredInterview experiencesImpress the recruiter
Effort
How wide the agent searches, and its spend ceiling.
Lowup to 50Quick scan, fewer searches, the essentials only
Mediumup to 100Balanced, the default depth
Highup to 200Exhaustive, widest search, most questions
Under the hood
How the agent actually works
No black box. Here's exactly what runs when you hit gather, and where your credits go.
stripe interview process
→ loop format
stripe system design questions
→ system design round
stripe behavioral values
→ behavioral round
01
It plans before it searches
The agent resolves the company's real domain and drafts a set of targeted queries, each with a stated purpose, before a single search runs.
Engineering blogJob postingCandidate reviewConference talk
02
It reads the live web
Real-time search across engineering blogs, job posts, and first-hand candidate reviews, then it pulls the full pages that matter, never a stale cache.
CodingSystem DesignBehavioralCompanyInterview loopSkills
03
It researches only what you pick
Choose your rounds and report sections; the agent skips everything you switched off, enforced in code, so you never pay for evidence you didn't ask for.
Low3–5 queries · 8–15 Q
Medium4–8 queries · 15–30 Q
High8–12 queries · 30–50 Q
04
You set the depth
Low, Medium, or High tune how wide it searches and how many questions you get, from an 8-question scan to a 50-question sweep, each with a hard spend ceiling.
High2 sources
How do you guarantee idempotency on the payments API?
Stripe Engineering blog
Interview review · levels.fyi
05
Every claim is checkable
Each question ships with a confidence level and links to the exact source it came from. No black box; verify any question yourself.
The honest comparison
Why not just ask ChatGPT?
A general chatbot can guess at interview questions. Here's what it can't do that we do.
Asking ChatGPTInterview Resources
Where the answer comes from
Training data with a cutoff date, frozen months ago
The live web, researched the moment you hit gather
Can you verify it?
No sources; you take its word for it
Every question links to the exact evidence it came from
How sure is it?
Sounds equally confident whether right or wrong
A confidence level on each question; inferred content is labelled
How specific is it?
Generic advice for the role, not the company
Scoped to your company, chosen rounds, and report sections
What it costs you
A monthly subscription whether you interview or not
Metered credits with a hard cap per run; pay only when you gather
Start small, keep the spend capped, and only top up if the research is useful.
Simple, transparent pricing
No plans, no subscription, just credits. Every feature is included in every pack. A report costs what it costs to research: typically about 46 credits, under $0.50, and never more than 260.
Try it
Starter
A low-risk first look
$1.49one-off
100 credits
- 100 credits
- About 5 research reports
- Pinpointed questions with evidence
- Interviewer research
- PDF and JSON export
Most popular
Bundle
Stock up, stop topping up
$6.49one-off
550 credits
- 550 credits
- About 25 research reports
- Everything in Starter
- 50 bonus credits
Best value
Max
For a full interview season
$12.49one-off
1200 credits
- 1200 credits
- About 50 research reports
- Everything in Bundle
- 200 bonus credits
Prices in USD. You'll be charged in your local currency at checkout.
Metered billing
You're charged only what a run actually spends, never a flat fee.
Hard cap every run
A run can never spend past its effort ceiling or your balance.
No subscription
Buy credits once and spend them only when you run a report.
Transparency
Where the questions actually come from
We search the public web, classify what we could reliably read, and synthesize questions from evidence only. Useful unreadable links are kept separately for you to open.
Live search plan
stripe backend interview experiencequery
Search targetspublic web only
Glassdoor
Blind
LeetCode Discuss
Forum threads
Personal write-ups
Engineering blogs
Job postings
Interviewer talks & open source
Goal
First-hand signals first
Bias
Interview evidence over fluff
Rule
No private or scraped data
No scraping restricted pages. We do not bypass logins, paywalls, robots controls, CAPTCHAs, or other access restrictions.
Evidence gate
Then we count what came back
A result only counts as evidence when it contains reliable, substantive content—not merely a title, thin snippet, or navigation blurb. Link-only resources never create claims, citations, summaries, or confidence. When evidence is thin, the agent broadens its search instead of pretending the report is complete.
Evidence threshold before shipping
Thin reports broaden instead of pretending they are complete.
No evidence citation, no confidence
If a question comes back without a citation to readable evidence, its confidence is forced to Low. A discovery link that must be opened manually never counts.
When there's no public data
How we research small, early-stage startups
Most people aren't interviewing at Google. When a company is twenty people with no Glassdoor page, the agent doesn't shrug and hand you an empty report — it changes what it goes looking for.
acme labs interview experience
Direct evidence
GlassdoorNo reviews
LeetCode DiscussNo threads
BlindNothing found
2 / 3substantial sourcesBelow threshold
Public interview data is thin — researching founders, funding stage, and similar companies...
So it goes looking for four things instead
01
Founder background
Where the people who built it came from. A CTO who spent four years at Stripe carries Stripe's interview instincts into their own loop.
02
Funding stage & size
Seed and Series B interview nothing alike. Fourteen engineers run a different loop than four hundred.
03
Comparable companies
How similar-stage companies building similar things actually run their interviews.
04
Role norms
What a loop for your role and your stack typically looks like at a seed-to-Series-B startup.
Everything this second pass produces ships tagged Inferred and can never claim High confidence — capped in code after the model answers, not asked of it. You'll always know which questions came from someone who actually sat the interview, and which are a reasoned read of how this company probably runs one.
Transparency
Why trust the research?
The product shows what it found, labels what it inferred, and caps confidence when the evidence is thin.
Read before trust
The report tells you what is solid, what is inferred, and why.
Confidence is not decoration here. It is how the product separates direct evidence from thin signals and forces the cautious answer when the data does not deserve more.
Direct evidence
Source-linked questions stay inspectable.
Sparse data
Fallback paths are labeled, not hidden.
Confidence rules
No citation means no inflated certainty.
Sparse-data fallback
When public interview data is thin, the run broadens into founder background, company stage, comparable companies, and role norms for early-stage startups. That fallback is called out instead of hidden.
Enforced confidence rules
No citation means no high confidence. Inferred questions cannot be marked High, even if the model tries. Those limits are applied after generation, not left as polite instructions.
Selected under-the-hood details
What counts as evidence
A result only counts as evidence when it contains reliable, substantive content—not merely a title, thin snippet, or navigation blurb. Link-only resources never create claims, citations, summaries, or confidence. When evidence is thin, the agent broadens its search instead of pretending the report is complete.
What inferred means
Built from proxy signals rather than a first-hand account of interviewing here. An inferred question can never carry High confidence — see how we source questions above.
What happens for thin companies
Everything this second pass produces ships tagged Inferred and can never claim High confidence — capped in code after the model answers, not asked of it. You'll always know which questions came from someone who actually sat the interview, and which are a reasoned read of how this company probably runs one.
Coverage examples
Prepare for top tech companies and early-stage startups
StripeGoogleMetaAmazonNetflixAirbnbUberLyftShopifySquareCoinbaseDatabricks
FigmaNotionSlackDropboxOpenAIAnthropicPerplexityVercelRampCursorMistralScale AI
FAQ
Frequently Asked Questions
You enter the company you're interviewing with, select the interview rounds you're preparing for, and our AI researches publicly available information to pinpoint the questions you might face. Each question comes with evidence and prep notes.
A note from the builder
I built this after one too many evenings lost to Glassdoor threads and half-updated Reddit posts, trying to guess what an interview would actually cover. A coaching call cost more than the job hunt could justify, and a generic question bank never knew which company I was even talking to.
So Interview Resources does the digging I used to do by hand, shows its sources instead of asking you to trust it, and costs less than a coffee per report. If the public data on a company is thin, it says so instead of pretending otherwise. That's the whole promise: real research, shown honestly, priced fairly.
— Sayan
SD
Sayan De
Builder, Interview Resources
Know the questions before you walk in.
AI-researched, evidence-backed reports for under $0.50 each. No subscription.
$1.49 gets you enough credits to see whether the research is useful for your next interview.