About EXIM Explorer

Built for Urvashi Industries · AP-28 · Archived ex-im.cloud trade data

What is this?

EXIM Explorer is a competitive intelligence dashboard built for Urvashi Industries (Fua) to analyze global trade data for stainless steel kitchen articles — specifically HS code 73239390.

The data was harvested from ex-im.cloud before the subscription expired. It includes shipment-level import records from buyers across Europe, Latin America, the Gulf, and Oceania — and export records from Indian competitors.

Data Coverage

Buyers discovered

9,620

Importers globally

Buyer shipments

182,495

Import records

Sellers discovered

263

Indian exporters

Seller shipments

751

Export records

Important caveat

Urvashi Industries intentionally suppresses its company name in Indian customs filings. This means Urvashi does notappear in normal search/listing results on ex-im.cloud. Only 5 public shipments are visible via direct URL. The 262 other Indian sellers are Urvashi’s visible competitors — her invisible peers are equally absent.

How to Read the Dashboard

Buyer

An importing company (e.g., IKEA Supply AG, Riba Smith). These are the customers who buy stainless steel kitchenware.

Seller

An exporting company, mostly Indian. These are Urvashi’s competitors. The data shows where they ship and who their customers are.

Shipment

A single customs record. One shipment = one container or one invoice line. It shows date, product, quantity, value, ports, and both parties.

HS Code

Harmonized System code. 73239390 = stainless steel kitchen/table articles. We also include nearby codes (732393xx, 821591xx, 821599xx) as ‘Our HSN’.

Unit Price

Declared price per unit in USD. This is what the buyer paid per kg/piece. Great for pricing intelligence.

Destination Port

The port where the goods arrived. Not the buyer’s office — the actual port of entry.

Origin Port

The Indian port where the goods shipped from (e.g., Mundra, Nhava Sheva).

Pages Guide

Dashboard

High-level overview. Top buyers, top sellers, country breakdown. Toggle ‘Our HSN’ vs ‘All HS codes’ to see how broad or narrow the filter is.

Buyers Directory

Searchable list of all importers. Filter by country or region. Sort by shipment count or value. Click any buyer to see their top suppliers and product mix.

Buyer Detail

Deep-dive into one importer. See who supplies them, what HS codes they import, and their full shipment history. Use this to identify which buyers are loyal to one supplier vs. shopping around.

Sellers Directory

Indian exporters ranked by activity. Urvashi is pinned at the top (anchor). Compare competitors by shipment volume and destination countries.

Seller Detail

Deep-dive into one Indian exporter. See their top customers and where they ship. Use this to spot market gaps Urvashi could enter.

Regions

Geographic coverage map. See which countries have been harvested and which are missing. Green = data present. Red = not on platform.

Cross-search buyers and sellers by company name. Minimum 2 characters.

Analytics

Business intelligence charts. Monthly trends, destination analysis, supplier rankings, price trends, buyer recency, and product mix.

Analytics Explained

Monthly Shipment Volume & Value

Bars = number of shipments per month. Line = total USD value. Use this to spot seasonal peaks (e.g., pre-Diwali rush, pre-Christmas stocking). If Urvashi is not shipping during peak months, that’s a missed opportunity.

Top Destination Ports

Where goods actually land. Hamburg, Port Kelang, and Penang are dominant. If Urvashi is not serving these ports, she may be missing the biggest markets.

Top Indian Suppliers (Buyer-Side View)

These are the exporters that appear most often in buyer import records. Key insight: many top suppliers are Chinese (Pingxiang Huashao, etc.), not Indian. This means Indian exporters like Urvashi are competing with China on price and volume.

Unit Price Trends

Average, minimum, and maximum declared unit prices over time. If average prices are dropping, the market is becoming more competitive. If Urvashi’s prices are above average, she needs a premium positioning story.

Buyer Recency

“Last 30 days” = actively importing. “Dormant (1y+)” = stopped importing. Dormant buyers who used to import heavily are prime reactivation targets.

Product Mix

Auto-classified from shipment descriptions. “General Kitchen” is the catch-all. If Urvashi specializes in chafing dishes but the market is mostly buying bowls, that’s a product strategy mismatch.

Known Limitations

Urvashi is invisible:Only 5 public shipments. Benchmarking against competitors is based on visible data, not Urvashi’s actual volume.
Seller-side data is sparse: Only 12 of 263 sellers have raw shipment data extracted. The supplier rankings use buyer-side records (much more complete) as a proxy.
Date range: Most data is from calendar year 2025. Earlier years have very few records.
Product classification is heuristic: Keyword matching on descriptions. Not perfect.
Unit prices are declared values: Customs filings may not reflect actual negotiated prices.
Free tier sleeps: If using Railway free tier, the app sleeps after 15 min idle. First load takes ~2-3 seconds.

How to Maximize This

1. Find dormant big buyers: Go to Analytics→ Buyer Recency. Look for buyers in “Dormant (1y+)” with high total value. These are warm leads.

2. Spot price-sensitive markets:Analytics → Unit Price Trends. If a destination’s average price is low, buyers there are shopping on price. If it’s high, they value quality.

3. Identify competitor weaknesses:Go to a competitor’s seller detail page. See which buyers they serve. If a buyer imports from 5+ suppliers, they’re not loyal — Urvashi can pitch them.

4. Find single-supplier buyers:Buyer detail → Top Suppliers. If a buyer gets 90% of shipments from one competitor, they’re either very loyal or locked in a contract. Approach cautiously.

5. Track seasonal demand: Monthly Trends. If Q3-Q4 is peak, Urvashi should plan production and outreach 2-3 months ahead.

6. Use the scope toggle:Every page has “Our HSN” vs “All HS codes”. “All HS codes” shows what else buyers import — cross-selling opportunities.

Technical Notes

Built with Next.js 16, React 19, TailwindCSS, better-sqlite3, and Recharts. Data stored in SQLite (~210 MB raw, ~92 MB deployed). Harvested via a custom Chrome extension and MCP server orchestrator. Hosted on Railway.