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Solution

Every listing, every stockout, captured before your buyers notice

Real-time SKU coverage across competitors and channels. Detect stockouts the moment they happen, new listings the day they appear, and assortment gaps before competitors fill them.

<5 minp95 stockout detection latency
50M+skus monitored daily
99.9%pipeline uptime
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Assortment is a moving target.

Listings appear and disappear hourly. Variants get added, deprecated, hidden. SKUs go in and out of stock by city, by store, by seller. Most teams discover changes weeks late.

Stockouts cost more than the missing sale.

When your SKU is out of stock and a competitor's is not, you lose the basket, the loyalty cycle, and the platform's recommendation engine starts favoring the competitor's listing for the next ten purchases.

We monitor every SKU, every variant, every stock signal, across every market in scope.

Cycles as low as every 15 minutes. New listings flagged the day they go live. Stockouts caught the moment they happen. Delivered structured, deduplicated, in your schema.

Catalog presence at SKU level

Track which SKUs are listed, where, by which seller, with which variants. New listings, deprecations, hidden products, all captured.

Stockout detection in real time

Catch out-of-stock signals as they happen, by SKU and by location. Restocks the moment they hit. Loss-of-shelf events flagged before they cost you the basket.

Across every market, every seller

Same SKU, different stories per market and per seller. Captured per geo and per seller, not flattened into one signal.

Key insight

Out-of-stock for 24 hours costs more than a 24-hour price war. The customer who couldn't buy your SKU finds the competitor's, and the platform's recommendation engine remembers that for the next ten purchases.

How it works

The extraction pipeline

From target spec to your warehouse, every assortment and availability record passes through these stages. You see the output. We run everything in between.

01

Target spec

Categories, SKUs, sellers, geos, cadence, and schema locked from your pilot scope.

02

Source orchestration

Web, app, and seller-storefront extraction across every market in scope, in parallel.

03

Capture

Signed requests, anti-bot bypass, geo-routed sessions, app-layer where needed.

04

Validation

Schema, range, deduplication, decoy detection, listing-state normalization.

05

Delivery

CSV, JSON, REST, or direct push to your warehouse, in your spec.

Coverage

Platforms we monitor

Assortment and availability monitoring runs against the same competitive surfaces as pricing. Each platform exposes listing state and stock differently, and we handle each one specifically.

Marketplaces

Shopee, Amazon, Flipkart, Walmart, Lazada, Tokopedia. Mall/Preferred/regular sellers, full variant signal, and FBA-level stock attribution.

Quick commerce

Blinkit, Zepto, Swiggy Instamart, BigBasket, dark-store level inventory

Food delivery

Restaurant menu availability and item-level stock

D2C brand storefronts

Direct catalog and variant-level stock state

50M+SKUs monitored daily across markets and sellers

Data landscape

The data we extract

Every listing record from every monitored platform, normalized into one assortment-and-availability schema, delivered on your cadence.

Listing

SKU ID, seller ID, title, variants, attributes, images

Availability

In-stock status, stock level signal, restock detection, location tag

Lifecycle

Listing created, updated, deprecated, hidden, restored

Catalog metadata

Category path, brand, pack size, weight, dimensions

Sellers

Seller ID, type, rating, country, listing age

Sourcing

Platform, surface, market, capture timestamp

This is a representative sample of the data we extract. We customize every extraction to your exact requirements. If you need a data point not listed here, we will add it to your pipeline.

Sample output

What a single record looks like

This is a representative payload from a real assortment and availability extraction job. Field names, schema, and delivery format are scoped to your spec at pilot time.

{
  "extracted_at": "2026-05-07T08:14:22Z",
  "platform": "blinkit",
  "surface": "app",
  "market": "IN",
  "city": "Bengaluru",
  "sku_id": "BLK-7842091",
  "matched_sku_id": "OUR-SKU-42",
  "product": {
    "name": "Tata Gold Premium Tea 1kg",
    "brand": "Tata",
    "category": "Beverages > Tea",
    "pack_size": "1kg"
  },
  "seller": {
    "id": "BLK-DARK-BLR-014",
    "name": "Dark Store Indiranagar",
    "type": "platform_owned"
  },
  "availability": {
    "in_stock": false,
    "stock_signal": "stockout",
    "last_in_stock_at": "2026-05-07T03:11:08Z",
    "restock_eta": null
  },
  "listing": {
    "first_seen_at": "2024-11-12T00:00:00Z",
    "last_updated_at": "2026-05-07T07:55:13Z",
    "status": "active"
  }
}

Schema

Field-level reference

Every record conforms to a stable schema. Your engineering team can integrate against this spec before the pilot starts.

extracted_atISO 8601

UTC capture timestamp

2026-05-07T08:14:22Z
platformstring

Source platform name

blinkit
surfaceenum

Where extracted (web, app, seller)

app
marketISO-3166

Market code

IN
citystring

City of capture

Bengaluru
sku_idstring

Source-platform SKU identifier

BLK-7842091
matched_sku_idstring

Your SKU mapped to this listing

OUR-SKU-42
product.namestring

Listing display title

Tata Gold Premium Tea 1kg
product.brandstring

Brand label

Tata
product.categorystring

Full category breadcrumb

Beverages > Tea
product.pack_sizestring

Pack or weight

1kg
seller.idstring

Source-platform seller identifier

BLK-DARK-BLR-014
seller.typeenum

platform_owned, mall, preferred, regular, third_party

platform_owned
availability.in_stockboolean

Available right now

false
availability.stock_signalenum

in_stock, low_stock, stockout, restocking

stockout
availability.last_in_stock_atISO 8601

Last timestamp the SKU was in stock

2026-05-07T03:11:08Z
availability.restock_etaISO 8601 / null

Platform-reported restock time

null
listing.first_seen_atISO 8601

When we first saw this listing live

2024-11-12T00:00:00Z
listing.last_updated_atISO 8601

Last platform update detected

2026-05-07T07:55:13Z
listing.statusenum

active, hidden, deprecated, restored

active

Delivery formats

How you receive the data

You define the format. We handle the rest.

CSV

Daily or hourly drops

Scheduled flat-file delivery. Clean, deduplicated rows with the columns you define.

{}
{}

JSON

Nested or flat schema

Structured JSON files for direct ingestion into your data pipeline or analytics tools.

API

Real-time access

REST API with real-time access to the latest extracted data. Webhook support included.

Direct warehouse

Zero-touch delivery

We push directly to your Snowflake, BigQuery, Redshift, or S3 bucket. Zero manual steps.

Custom setup

Talk to us

Need a different format, frequency, or integration? We build it for you at no extra cost.

Use cases

How teams put assortment and availability data to work

From pricing teams to category managers to operations leads, here are the most common ways assortment and availability data drives decisions.

Brand managers, assortment coverage

See every market and seller where your SKUs are listed. Identify gaps where you should be present but are not. Track competitor expansion in your category.

Supply chain, stockout response

Catch out-of-stock signals as they happen. Trigger replenishment workflows on real signals, not stockout reports that arrive days late.

₹

Category managers, competitor assortment intel

Track which SKUs competitors are launching, deprecating, or doubling down on. Spot category expansion plays before they impact share.

Trade marketing, share-of-shelf defense

Monitor your share of listings versus competitors per category and per market. Defend share before it slips.

Operations, seller compliance

Track which sellers carry your SKUs, at what tier, with what listing quality. Spot non-compliant sellers and content drift.

Leadership, assortment scorecard

Monthly board-ready coverage and stockout reports across categories, markets, and competitors. Trends, deltas, recovery times.

Tech specs

What we run at scale

Every assortment and availability engagement runs against these baseline specs. Your scope can move freshness, throughput, or geo coverage to whatever you need.

<5 min

p95 stockout detection latency

50M+

SKUs monitored daily

99.9%

Pipeline uptime

200+

Geos and markets covered

15 min

Minimum extraction cycle

99%+

Records passing validation

Challenges

Why assortment and availability data extraction is hard

If extraction were easy, you would do it yourself. Here is why it is not.

01

Listings change in three different ways

New SKU created. Existing SKU updated. Listing hidden, deprecated, or restored. Each requires different detection logic. A daily diff misses two of three.

02

Stockouts are state changes, not snapshots

Knowing a SKU is out of stock right now is half the answer. Knowing when it went out, how long it has been out, and the restock pattern is what supply-chain teams actually need.

03

Variants and attributes are messy

The same product has 20 variants with attribute schemas that vary by platform. Mapping them consistently across competitors is a data-engineering problem most teams underestimate.

04

Geo and seller dimensions multiply listings

Same SKU on Amazon has different stock signals per fulfillment center. On Shopee, different stock per Mall vs Preferred seller. On quick commerce, different stock per dark store. One SKU is many records.

05

Anti-bot at scale, every 15 minutes

Detecting a stockout in 5 minutes means polling the listing every 5 minutes. Polling every listing every 5 minutes across 20 platforms is an extraction load most providers cannot run reliably.

06

Building it in-house costs more than the data

Engineers, proxies, anti-bot tooling, monitoring, validation. Internal projects spend 6 to 12 months and still miss the stockouts that matter most.

Why us

Why Clymin for assortment and availability

We are not a tool. We are the team you call when the data matters too much to get wrong.

We solve what others can't

Listings and availability monitoring across 20+ platforms simultaneously, every 15 minutes, with stockout detection latency under 5 minutes. App-layer where the data lives, geo-routed where it differs, validated before delivery.

We prove it before you pay

Free pilot on your SKUs, your competitors, your markets. Sample data within 1 to 3 days. You evaluate against your own benchmarks before any commitment.

You pay only for data delivered

Per record, no setup fees, no per-SKU charges, no per-market charges. One metric: cost per record. If we don't deliver, you don't pay.

Your identity stays protected

We do not display client logos or name-drop. Assortment intelligence is sensitive. Your competitors should never know you are watching.

100B+

Data points extracted

24/7

Pipeline uptime

Real-time

Data delivery

100K+

Points of interest covered

Proven at enterprise scale. We operate continuous competitive intelligence infrastructure for one of the world's largest quick commerce platforms.

Industries served

Who buys assortment and availability data

The verticals where assortment and availability extraction creates the most leverage.

E-commerce MarketplacesQuick CommerceConsumer ElectronicsFMCG BrandsD2C BrandsOmnichannel RetailersBrand ProtectionHealthcare

Catch every stockout before it costs you the basket

Tell us your SKUs, your competitors, your markets. Pilot data in 1 to 3 days. No commitment.

Start a Free PilotBook a Meeting

FAQ

Product Assortment & Availability Monitoring data extraction FAQ

Every major e-commerce, quick commerce, marketplace, and brand site we are scoped to. Web, app, and seller storefronts. We add new platforms as part of the pilot at no additional cost.

p95 within 5 minutes of the listing flipping to out-of-stock. For critical SKUs you flag during pilot, we run higher-cadence cycles.

Yes. Daily catalog scans by category and seller catch new SKUs the same day. For categories where new-launch detection matters within hours, we run higher-cadence category sweeps.

Yes. Variants are captured as separate records, with their own SKU IDs, attribute sets, and stock signals.

Yes. Same matching pipeline used in Competitive Pricing Intelligence: attributes, brand, model, pack size, image hash where needed. Verified during pilot.

Yes. For quick commerce, dark-store ID is captured per record. For marketplaces with fulfillment-center attribution (Amazon FBA, Flipkart Smart), we capture the FC where exposed.

CSV, JSON, REST API, or direct push to your data warehouse: BigQuery, Snowflake, Redshift, S3. You define the schema.

We extract publicly available data. We do not extract authenticated user-level data without explicit account ownership. Use of extracted data is the customer's responsibility under their jurisdiction.