AmbitionBox Scraper — Reviews, Salaries & Interviews

India's #1 employer-intelligence dataset. Extract employee reviews, salary benchmarks, and interview Q&A from AmbitionBox.com — the largest employer review platform in India with 100k+ companies.

Use cases

• HR competitive intel — benchmark culture, salary, and retention signals across Indian IT firms (TCS, Infosys, Wipro, HCL, Cognizant…)
• Employer brand monitoring — track your company's review velocity, sub-rating trends, and employer response rate over time
• India market research — understand compensation norms, work policies (WFH/WFO/Hybrid), and talent pain points by role and city
• Recruitment analytics — filter reviews by job profile, location, employment type, and experience to pinpoint candidate concerns
• Interview prep / HR tooling — extract interview questions, difficulty ratings, and outcome signals per role

What you get

Reviews

One row per employee review with:

• Overall rating (1–5) + 7 sub-ratings: Salary, Skill Development, Job Security, Promotions, Work Satisfaction, Company Culture, Work-Life Balance
• Pros text and Cons text (the core free-text content)
• Reviewer firmographics: job profile, location, division, employment type, work policy (WFO / WFH / Hybrid), years continued
• Trust signals: verified badge, contested flag, helpful counts, featured flag
• Dates: published, modified
• Optional fields that populate only when the reviewer/employer provides them: workDescription, benefits, employerResponse, currentJob (AmbitionBox leaves these empty on most reviews — they are passed through when present, not fabricated)

Company benefits (dataType: company_benefits)

One summary row per company with the structured employee-benefits data AmbitionBox publishes at the company level — the populated source of benefits info (the per-review benefits field is almost always empty):

• employeeBenefits: list of {name, count} — each perk and how many employees reported it (e.g. Office cab/shuttle ×4052, Free meal ×640, Stock options ×56)
• totalBenefitTypes, benefitsNarrative, companyName, totalReviewCount

Salaries

One row per salary report:

• Role name, experience range, CTC (min/max/avg in INR), location, report date

Salaries (dataType: salaries)

Per-role salary aggregates: role name, experience range, CTC (min / max / avg in INR), data points, location.

Note: reviews (the default) and salaries are the fully-supported, reliable surfaces. interviews is best-effort — AmbitionBox renders interview pages differently and may return no rows for some companies.

Sample output (review)

{
  "dataType": "review",
  "companySlug": "tcs",
  "reviewId": 79820728,
  "overallRating": 5,
  "subRating_salary": 1,
  "subRating_skillDevelopment": 4,
  "subRating_jobSecurity": 5,
  "subRating_promotions": 1,
  "subRating_workSatisfaction": 5,
  "subRating_companyCulture": 5,
  "subRating_workLifeBalance": 5,
  "pros": "Good work life balance and supportive team. TCS provides strong Job security...",
  "cons": "Salary growth and promotion process can feel slow compared to market standards...",
  "jobProfileName": "Process Associate",
  "locationName": "Kolkata",
  "workPolicy": "WFO",
  "verified": true,
  "publishedAt": "2026-05-25T12:04:33Z",
  "parse_confidence": "HIGH",
  "scrapedAt": "2026-06-09T06:13:21Z"
}

Input

Field	Type	Default	Description
companies	list	required	Company slugs (e.g. tcs, infosys) or AmbitionBox URLs
dataType	string	reviews	What to scrape: reviews, salaries, interviews, or all
maxItemsPerCompany	integer	100	Max records per company per data type
proxyConfiguration	object	RESIDENTIAL IN	Apify proxy (India residential required for reliable access)
maxConcurrency	integer	4	Parallel requests

Finding company slugs

The slug is the URL-safe company name on AmbitionBox. Examples:

• TCS → tcs (from ambitionbox.com/reviews/tcs-reviews)
• Infosys → infosys
• Wipro → wipro
• HCL Technologies → hcl-technologies

Pass the full URL or just the bare slug — both work.

vs other AmbitionBox scrapers

	This actor	memo23/ambitionbox-reviews	getdataforme/ambitionbox-reviews	thirdwatch/ambitionbox
Reviews (7 sub-ratings)	✅	✅	✅	partial
Full reviewer firmographics (role/location/division/policy)	✅	✅	partial	❌
Trust signals (verified / employer response / helpful)	✅	partial	❌	❌
Salaries surface	✅	❌	❌	✅ (salary-only)
parse_confidence quality flag	✅	❌	❌	❌
45 fields per review	✅	~40	~20	n/a

FAQ

Do I need a proxy? Yes — AmbitionBox requires a residential proxy for reliable access. Apify RESIDENTIAL IN (India) is prefilled by default and required; no external proxy key needed.

How many reviews can I get? AmbitionBox paginates ~20 reviews/page with hundreds of pages for large employers (TCS has 115k+ reviews). Set maxItemsPerCompany to cap per company.

What does parse_confidence mean? HIGH = all core fields populated (rating, pros/cons, sub-ratings, role, location, date); MEDIUM/LOW flag partially-filled reviews so you can filter on data quality.

Is this affiliated with AmbitionBox? No — it's an independent tool that extracts publicly available review data.

Use with AI agents (MCP)

{
  "mcpServers": {
    "apify": {
      "url": "https://mcp.apify.com/?actors=bovi/ambitionbox-reviews-scraper",
      "headers": { "Authorization": "Bearer <YOUR_APIFY_TOKEN>" }
    }
  }
}

Pricing

Pay per result (each review or salary row counts as 1 result).

Technical notes

AmbitionBox is a Next.js SSR application. This scraper extracts structured data from the embedded __NEXT_DATA__ JSON block — no fragile DOM parsing, no class-name selectors. Data is fully structured before it leaves the server.

The site requires a residential proxy for reliable access. Apify RESIDENTIAL IN (India) proxies are required and prefilled by default — the scraper will not work without them. No external proxy key needed.

More scrapers from our toolkit

Building a data pipeline? These actors pair well with this one — each runs on your own Apify account with the same pay-per-result pricing, no subscription:

• Indeed Employer Intelligence
• Meta Ads Library Scraper
• Saas Pricing Monitor

Chain any of them together from the Integrations tab (the Run succeeded trigger) to build a multi-step workflow — one actor's output feeds the next.

Usage statistics

This Actor creates a small, content-free summary at the end of each run. It is used only to monitor reliability and improve this Actor. A copy is saved as USAGE_STATS in your own Apify key-value store, so you can see the exact record created for your run.

Set disableUsageStats to true in the input to opt out. Nothing is sent then; your USAGE_STATS record only says that statistics were disabled.

Only these fields are recorded:

• schema version, Actor name and build number;
• UTC start and finish hour (not a precise timestamp);
• run duration, number of results and time to the first result, each as a coarse range;
• whether the result was empty, the end status, and an error type from a fixed list;
• memory setting and counts of charged events;
• names of the input fields you set, never their values;
• the selected option for input fields that offer a fixed list of choices (for example a sort order).

We do not collect input text, search terms, URLs, domains, usernames, email addresses, names, proxy credentials, tokens, scraped records, output items, raw error messages, stack traces, or hashes of any of those values. Records are kept for no longer than 13 months, used only as aggregated operational statistics, and never sold or shared.

Additional fields (Phase 2)

This Actor also records your Apify user ID, whether Apify marks the account as paying, the size range of list inputs, the selected country when the input offers a fixed list of countries, and one category from a fixed Actor taxonomy. We use these fields only for aggregate reliability, repeat-use and cross-Actor analysis; reports suppress any cell with fewer than five distinct users.

The same disableUsageStats: true input flag turns these fields off too. The user ID is removed after 13 months; we do not export, sell, share, or attempt to re-identify this data.

Run-outcome signals (v2)

To learn whether a run did what it was asked to do, the record also holds a few more coarse ranges and yes/no flags. None of them contains content:

• the result limit you asked for (a range, when the input has one) and what share of it was delivered;
• results delivered per input item you listed (a range);
• output quality as ranges: how fully the result fields were filled, the share of rows that look like errors, the share of duplicate rows, and how many different fields appeared. These are counted in memory while results are saved; no result content is kept;
• how the run was started (console, API, schedule, webhook, another Actor);
• how it ended: stopped by you, timed out, reached the requested limit, stopped by the charge limit, and how many times the platform moved the run;
• if this Actor reports it: how many items to process worked or failed (ranges) and one failure reason from a fixed list;
• a short code made from the names of the input fields you set, never their values.

Repeat-run fingerprint (v2)

When your Apify user ID is recorded (see above), the record also holds an 8-character one-way code made from your input (proxy settings left out) and this Actor's name. It only lets us see that the same account ran the same input again soon after an unsatisfying run; we never see the input itself. It is stored only in the database, never published, and reports use it in aggregate with the same five-user minimum. It is the one exception to the statement above that no hashes are collected, and disableUsageStats: true turns it off.