We extract company profiles, contact details, reviews, and service catalogues from Herold.at. Delivered as clean JSON, CSV, or Parquet to S3 or BigQuery.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Business Profiles objects from herold.at. All fields typed and schema-versioned.
"herold_id": "AT-938210", "company_name": "TechRepair Wien GmbH", "category": "Electronics & Gadgets", "sub_category": "Computer Repair", "vat_number": "ATU12345678", "founding_year": 2014, "employee_count": "10-49"
| # | herold_id | company_name | category | sub_category | description | vat_number |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Contact Details objects from herold.at. All fields typed and schema-versioned.
"herold_id": "AT-938210", "phone_primary": "+43 1 2345678", "email_address": "office@techrepair-wien.at", "website_url": "https://techrepair-wien.at", "street_address": "Mariahilfer Strasse 123", "postal_code": "1060", "city": "Vienna"
| # | herold_id | company_name | phone_primary | phone_secondary | email_address | website_url |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Ratings & Reviews objects from herold.at. All fields typed and schema-versioned.
"review_id": "REV-847291", "herold_id": "AT-938210", "reviewer_name": "Klaus M.", "rating_score": 4.5, "review_text": "Fast smartphone repair, fair prices.", "review_date": "2025-11-14", "platform_source": "Herold"
| # | review_id | herold_id | reviewer_name | rating_score | review_text | review_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Services & Brands objects from herold.at. All fields typed and schema-versioned.
"herold_id": "AT-938210", "service_list": "['Screen Replacement', 'Battery Change', 'Data Recovery']", "brand_list": "['Apple', 'Samsung', 'Sony']", "payment_methods": "['Cash', 'Credit Card', 'Apple Pay']", "languages_spoken": "['German', 'English']", "opening_hours": "Mo-Fr 09:00-18:00"
| # | herold_id | service_list | brand_list | payment_methods | languages_spoken | certifications |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search Results objects from herold.at. All fields typed and schema-versioned.
"keyword": "electronics store", "location": "Vienna", "position": 3, "herold_id": "AT-938210", "company_name": "TechRepair Wien GmbH", "sponsored_placement": false, "scraped_at": "2026-01-12T14:22:10Z"
| # | keyword | location | position | herold_id | company_name | sponsored_placement |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Herold.at scraper navigates regional directories, category pagination, and contact reveals. We handle proxy rotation, session management, and rate limits.
Company name, description, VAT number, founding year, and employee count scraped directly from the business listing page.
Extract phone numbers, email addresses, and website URLs, handling JavaScript click-to-reveal mechanisms where required.
Parse street addresses, postal codes, cities, and regions into structured fields for easy mapping and CRM import.
Capture star ratings, review text, and owner responses across all paginated review pages for sentiment analysis.
Target specific categories like consumer electronics, IT infrastructure, and gadget repair with deep sub-category mapping.
Extract and normalise complex opening hour schedules, including holiday closures and special appointment times.
Scrape the list of supported brands and specific services offered by retailers and repair shops.
Monitor organic versus sponsored visibility for specific keywords across Austrian cities and postcodes.
Run continuous pipelines that detect new business registrations, closed stores, and updated contact details.
Brief in. Clean data out.
Provide search terms, postcodes, or category URLs. We map the extraction schema to your specific requirements.
We configure Scrapy crawlers, Austrian proxy rotation, and CAPTCHA handling for herold.at.
Schema validation, null-rate checks, and data normalisation routines run before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage.
Directory scraping requires bypassing rate limits and pagination caps. We manage the infrastructure so you get clean records.
Herold.at restricts access based on geographic location and request volume. We utilise residential proxy pools located within Austria to maintain access and avoid IP bans.
Directory search results are often capped at a certain number of pages. We implement sub-grid search strategies using postcodes and narrow keywords to extract the full dataset.
Many email addresses and phone numbers are hidden behind JavaScript events to deter basic scrapers. Our Playwright integration executes these scripts to capture the underlying data.
Addresses and opening hours are often formatted inconsistently by business owners. We apply regex and parsing logic to normalise these into strict schema definitions.
If Herold updates its DOM structure, our Prometheus and Grafana alerting stack notifies our engineers immediately, ensuring minimal downtime for your data feed.
Sales teams extract contact details for IT service providers and electronics retailers to build targeted outreach campaigns.
Consultancies map the density of specific businesses across Austrian regions to identify underserved markets.
Retailers monitor competitor opening hours, service offerings, and brand partnerships.
Brands aggregate reviews across repair shops and retailers to measure customer satisfaction and service quality.
Marketing operations teams append missing VAT numbers, employee counts, and founding years to existing CRM records.
Agencies track search visibility and sponsored placements for local businesses to pitch SEO and marketing services.
"Herold is the definitive registry for Austrian businesses, but extracting clean B2B contact lists requires navigating strict pagination and bot detection."
Building a directory scraper is straightforward. Maintaining it across layout changes, regional proxy blocks, and JavaScript contact reveals is complex. DataFlirt manages the infrastructure, CAPTCHA solving, and schema mapping so your team can focus on lead scoring and market analysis.
Everything supported by our herold.at 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 and deduplication. Playwright handles JavaScript rendering and interaction flows required for hidden contact details.
We maintain pools of residential ISP proxies specific to the DACH region to ensure high success rates against Herold's geo-fencing.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and dependency management. State is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About herold.at scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available directory information is generally permissible, provided it does not breach specific Terms of Service or overload the target servers. DataFlirt extracts only public business data and adheres to ethical scraping practices. Clients must ensure their use of the data complies with GDPR regulations regarding B2B marketing.
Herold often requires a user interaction to reveal phone numbers or emails. We use Playwright to simulate these clicks and execute the necessary JavaScript, capturing the data exactly as a human user would.
Yes. We can configure the pipeline to iterate through a specific list of Austrian postcodes, cities, or administrative divisions to ensure complete coverage of your target area.
When a broad category search returns more results than the pagination allows, we automatically segment the search by adding geographic constraints or sub-categories until all underlying records are accessible.
We support one-off historical dumps, monthly refreshes, or weekly incremental updates depending on your requirement for data freshness.
Yes. We parse raw address strings into structured fields for street, postal code, and city, making it immediately usable for your mapping or CRM software.
20-minute scoping call. Pilot dataset within the week. Production within two. Need 5,000 electronics retailers or a continuous feed of new IT service registrations in Vienna? We build and operate the pipeline.
