We extract PC component listings, pricing signals, store inventory, and user reviews from inet.se. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Product Listings objects from inet.se. All fields typed and schema-versioned.
"sku": "5413289", "name": "ASUS GeForce RTX 4090 24GB TUF Gaming OC", "brand": "ASUS", "category": "Datorkomponenter", "price_sek": 24990.0, "stock_web": 14, "rating": 4.8, "review_count": 42
| # | sku | name | brand | category | sub_category | price_sek |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Campaigns objects from inet.se. All fields typed and schema-versioned.
"sku": "5413289", "price_sek": 22990.0, "regular_price_sek": 24990.0, "discount_pct": 8, "campaign_name": "SweClockers Helg", "member_price": false, "price_timestamp": "2026-05-12T09:14:00Z"
| # | sku | price_sek | regular_price_sek | discount_pct | discount_abs | campaign_name |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Store Inventory objects from inet.se. All fields typed and schema-versioned.
"sku": "5413289", "store_name": "Sisjön", "store_id": "store_got_sis", "in_stock": true, "stock_quantity": 3, "demo_ex_available": false, "last_updated": "2026-05-12T09:15:22Z"
| # | sku | store_name | store_id | in_stock | stock_quantity | shelf_location |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews objects from inet.se. All fields typed and schema-versioned.
"review_id": "rev_849102", "sku": "5413289", "author": "Nils P.", "rating": 5, "review_date": "2026-04-18", "verified_purchase": true, "helpful_votes": 12
| # | review_id | sku | author | rating | review_date | review_title |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Datorbyggare Data objects from inet.se. All fields typed and schema-versioned.
"sku": "1903921", "component_type": "Motherboard", "socket_type": "AM5", "form_factor": "ATX", "memory_type": "DDR5", "compatibility_flags": "['ryzen_7000_ready', 'pcie_5.0']", "price_sek": 3490.0
| # | sku | component_type | socket_type | form_factor | wattage | memory_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Our scraper handles every layer of the platform: component listings, dynamic pricing, physical store inventory, and the review corpus with JavaScript rendering and session management built in.
Name, brand, specifications, warranty details, and every metadata field inet.se surfaces extracted at the SKU level.
Capture current price, regular price, campaign details, and member specific pricing timestamped per crawl.
Extract stock levels across all physical stores including Sisjön, Sveavägen, and Malmö.
Full review text, star ratings, helpful vote counts, and verified purchase flags paginated across all review pages.
Extract socket types, form factors, and compatibility flags directly from the PC builder logic.
Track organic position for any category or keyword with sorting parameters applied.
Accurate parsing of SEK currency formats, Swedish specification terms, and local date formats.
Monitor weekend campaigns, SweClockers specials, and limited time discounts.
Run one-off bulk exports or configure continuous pipelines at hourly or daily cadences.
Brief in. Clean data out.
Provide SKU lists, category URLs, or keyword sets. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and session management for inet.se.
Schema validation, null-rate checks, price-outlier detection, and sample reviews before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Modern retail sites invest in scraping detection. Here is how we stay resilient and why teams choose managed infrastructure over DIY.
Retail bot detection operates on TLS fingerprints and IP reputation. Our crawlers use residential ISP proxies with realistic browser fingerprints and full cookie session management trained on real user behaviour patterns.
Product pages and store stock widgets rely on client-side rendering. We run full Playwright browser sessions with JavaScript execution capturing data that headless HTTP clients miss entirely.
Our selector strategy uses multiple fallback chains per field so a frontend layout change does not break your data pipeline overnight.
For large catalogues we maintain a hash index of last-seen values per field. Subsequent runs only push diffs reducing compute cost and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, price outliers, schema drift, and coverage drops.
Electronics retailers monitor pricing and campaign windows to reprice and protect margin.
Brands audit product placement, review sentiment, and category saturation.
Supply chain analysts track stock depth across physical stores to model demand velocity.
Analysts track new entrant launches and specification trends in the PC component space.
ML teams use structured electronics datasets to train recommendation engines and NLP classifiers.
Procurement teams correlate stock depth indicators with campaign dates to improve forecasting models.
"Inet.se holds the most accurate pulse on the Swedish PC and electronics market, but extracting multi-store stock and historical pricing requires dedicated infrastructure."
Most teams underestimate the investment required: reliable inet.se scraping requires Swedish residential proxies, full JavaScript rendering for dynamic stock widgets, CAPTCHA handling, and anomaly monitoring. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our inet.se scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration, deduplication, and retry logic. Playwright handles JavaScript rendering, cookie sessions, and interaction flows.
We maintain pools of residential ISP proxies across SE regions. Rotation happens per-request with sticky sessions where required.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About inet.se scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from inet.se is generally permissible under applicable law. DataFlirt targets only public, non-authenticated product, pricing, and review data. We do not extract personal data or circumvent authentication walls.
We use residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. Our selectors have multi-layer fallback chains so DOM changes do not break the pipeline.
Yes. We extract real-time inventory counts and availability status for all physical retail locations including Sisjön, Sveavägen, and Malmö.
Real-time streaming pipelines achieve sub-60-minute latency for price and availability signals on a defined SKU set. Full catalogue refreshes at daily cadence complete within a 4-8 hour window depending on size.
Yes. We extract socket types, form factors, and compatibility flags exposed by the PC builder logic allowing you to map component relationships.
Our smallest packages start at a defined category or SKU list with weekly delivery. For larger catalogues or custom schema requirements, we price based on volume and delivery frequency.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off product catalogue dump or a continuous price-monitoring feed across 45K SKUs, we scope, build, and operate the pipeline. Tell us what you need.
