Map the product range
Compare models, specifications and brand presence across an agreed set of categories.
All use casesFlipkart
INDIAN E-COMMERCE INTELLIGENCE
Flipkart
product data.
See the assortment.
Keep the offer in context.
Product identities, prices and published signals, structured for retail research that goes beyond the headline discount.
Separate price from promotion
Track listed prices without confusing exchange offers with unconditional discounts.
Follow changes over time
Build dated snapshots for assortment, price movement and published rating signals.
A REAL RESPONSE, NOT A MOCK DATASET
24 placements.
20 distinct products.
One television category page, collected through 2Captcha. Product IDs are deduplicated; repeated positions and promoted placements stay attached to the record.
Television category snapshot
20 unique products24 placementsINR listed pricesPIN code not set
| Product / model | Price shown |
|---|---|
| ₹55,990 | |
| ₹74,999 | |
| ₹62,999 | |
| ₹14,490 | |
| ₹46,990 |
1-5 of 20
1 / 4
No AI extraction call One category-page response All 20 records available
Samsung was the requested filter, not a guarantee of every result's brand. This response also contained promoted Sony and LG listings.
BETTER COMPARISONS START HERE
Same category.
Different kinds of evidence.
A clean dataset keeps product identity, commercial conditions and local availability distinct. That is what makes it useful downstream.
Identity, not just a title
Keep the product ID, model and listing ID. Retain repeated placements without counting the same product as a new item.
product_id + listing_id + placementsPrice, not a promise
Store listed price, reference price and offer labels separately. A conditional exchange saving is not the price everyone pays.
listed_price != checkout_totalStock, not serviceability
A public stock flag does not establish delivery to a particular PIN code. Location-specific checks need an agreed collection scope.
availability_state != delivery_availableLESS FETCHING. MORE USEFUL RECORDS.
Read the data
the page already carries.
Our tested HTML contained the product cards' structured state. A deterministic parser recovered the listing fields without an AI call or separate visits to all 20 products.
- 01 / ACCESS
Choose the right source
Assess approved official feeds first. For public-page collection, validate accessible HTML against the fields you need.
- 02 / STRUCTURE
Parse, match, validate
Read literal page data without executing its scripts. Check identifiers, deduplicate products and preserve offer context.
- 03 / DELIVER
Enrich only the gaps
Scope extra detail or location checks only where needed. Deliver an agreed schema and refresh it on your schedule.
Inside the collection test 10 OCT 2026 / DHAKA
A single raw HTML task through the 2Captcha Scraper API returned HTTP 200 and usable listing data. The parser reads window.__INITIAL_STATE__.pageDataV4.page.data, selects PRODUCT_SUMMARY widgets and decodes the literal JSON. It does not execute source scripts, call an AI model or export session and tracking data.
The result contained 24 product cards and 20 distinct product IDs. Some products appeared in both promoted and ordinary placements. The page advertised 279 results; this sample covers only the first response, not all results or the full catalogue. The HTML's JSON-LD listed different products, so it was not used to join names or prices to the visible product-card state.
A direct HTTP request also returned usable product-card data. For these fields, HTML plus deterministic parsing was the lowest-work route we verified. Use direct access where appropriate, or the provider where it meets your access requirements. One successful request is not a reliability benchmark or a guarantee of future unit cost.
We did not log in, solve a CAPTCHA, add a custom proxy, visit detail pages or verify delivery to a PIN code. The provider's debug price is not used as a billing source. Official affiliate feed access is a separate option to validate, not the origin of this sample.
CHOOSE THE WORKFLOW, NOT JUST THE TOOL
Research a few pages.
Or maintain a retail feed.
Collecting pages across many websites and maintaining high-volume data from one marketplace are different jobs.
Scraping API
Fetch accessible public content, then optionally analyse or classify it with AI.
- Your URLs, prompts and downstream logic
- Content-only mode does not use AI
- No login or CAPTCHA bypass
Managed Flipkart data
A source-specific pipeline around your products, required fields and refresh cadence.
- Source access and anti-bot feasibility assessment
- Product matching, validation and maintenance
- Agreed JSON, CSV or delivery API
For recurring volume, compare cost per usable record, including access, retries, enrichment and maintenance. A custom solution can be more economical for the right workload; coverage and permitted access are assessed before a commitment.
BEFORE THE FIRST DELIVERY
Questions that
shape the dataset.
Brand filters, repeated listings, conditional offers and what a stock label actually establishes.
Why are Sony and LG products in a Samsung-filtered sample?
The tested category URL requests Samsung, but the returned product cards also contain Sony and LG entries with ad metadata. We preserve each card's published brand and promoted status instead of inferring its brand from the URL. A brand-specific export can apply an explicit brand filter after collection; category-position research should retain the original placements.
Why are 24 product cards reduced to 20 records?
Four product IDs appeared more than once in this response. The sample groups cards by product ID while keeping every observed position, listing ID, promoted flag and placement price. Product-level fields come from the first occurrence. For seller or offer research, we agree an offer-level key rather than assuming one product means one offer.
Is the listed price the amount a customer will pay?
No. A listed price, struck-through reference price and conditional promotion are different values. Exchange offers are stored as labels, not automatically subtracted. Bank eligibility, delivery charges, selected variants and checkout conditions need separate validation. This snapshot is not a current checkout quote.
Does IN_STOCK mean delivery is available for my PIN code?
No. The listing's embedded state can say IN_STOCK without establishing serviceability for a particular address. No delivery PIN code was explicitly selected in this test. Delivery dates, local availability and stock quantities remain unverified; PIN-code-specific collection is separately scoped.
Can I use ratings as sales counts or model-specific review totals?
No. The sample keeps average rating, rating count and written-review count as separate source values. These are not unit sales. Some products can share rating aggregates across related variants; our category-page test does not establish the aggregation rules. Product-detail or review enrichment needs its own checks.
Would an official Flipkart feed be a better starting point?
Possibly, if you have approved access and the fields fit your use. Flipkart's affiliate documentation describes category feeds, delta feeds and product lookups for registered affiliates. We have not tested those endpoints with your account or verified current onboarding. Feed coverage, freshness and permitted use must be confirmed before choosing that route.
Do I get this JSON directly from your self-service Scraping API?
Not automatically. This sample uses a separate research parser to read product data from an actual 2Captcha HTML response, without AI. The self-service API returns accessible page content and supports optional AI processing. A maintained Flipkart schema, validation and scheduled JSON, CSV or API delivery belong to a managed project.
How do you prevent empty pages from becoming successful records?
We validate the response, supported product state, identifiers and usable records, not just HTTP 200. A challenge, empty shell or changed structure fails the extraction check. We keep failed collections distinct from out-of-stock products and agree retry budgets and coverage expectations before scaling.
YOUR PRODUCTS. YOUR DATA REQUIREMENTS.
Build a clearer
view of your market.
Share a few Flipkart URLs and the decisions your data needs to support. We'll assess access and agree a sample before scaling.
- Categories, brands and required fields
- PIN codes, if local availability matters
- Volume, refresh schedule and delivery format
Projects are accepted individually, subject to feasibility and available capacity. An enquiry does not reserve a solution or confirm delivery. Pricing and terms are agreed directly with our team.
A real person reviews your request. This form does not start a paid collection.
ScrapingBuddy is independent and is not affiliated with or endorsed by Flipkart. Collection scope and permitted use are assessed per project. Hero artwork is illustrative; the sample is a dated category-page observation, not a live price, checkout quote or delivery guarantee.