We extract company profiles, verified buyer reviews, product ratings, and merchant response metrics from Reviews.io. 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 Company Profiles objects from reviews.io. All fields typed and schema-versioned.
"company_name": "Acme Corp", "domain": "acmecorp.com", "overall_rating": 4.8, "review_count": 1452, "recommendation_pct": 94, "industry": "Retail"
| # | company_name | domain | overall_rating | review_count | recommendation_pct | industry |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Company Reviews objects from reviews.io. All fields typed and schema-versioned.
"review_id": "rev_8923471", "company_domain": "acmecorp.com", "reviewer_name": "Jane Doe", "star_rating": 5, "review_date": "2026-05-10T14:22:00Z", "verified_buyer": true
| # | review_id | company_domain | reviewer_name | star_rating | review_title | review_body |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Product Reviews objects from reviews.io. All fields typed and schema-versioned.
"review_id": "prev_11294", "product_name": "Wireless Mouse M300", "product_sku": "WM-300-BLK", "star_rating": 4, "review_title": "Good battery life", "verified_buyer": true
| # | review_id | product_name | product_sku | star_rating | review_title | review_body |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Branch Reviews objects from reviews.io. All fields typed and schema-versioned.
"branch_name": "Acme Corp London", "branch_id": "br_4401", "company_domain": "acmecorp.com", "city": "London", "overall_rating": 4.6, "review_count": 312
| # | branch_name | branch_id | company_domain | city | country | overall_rating |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Reviewer Profiles objects from reviews.io. All fields typed and schema-versioned.
"reviewer_id": "usr_99812", "reviewer_name": "John Smith", "total_reviews": 14, "average_rating_given": 3.8, "helpful_votes_received": 42, "verified_status": true
| # | reviewer_id | reviewer_name | total_reviews | average_rating_given | location | joined_date |
|---|---|---|---|---|---|---|
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Our Reviews.io scraper handles every layer of the platform: company listings, product reviews, sentiment tracking, and merchant response metrics, with JavaScript rendering and pagination traversal built in.
Capture overall ratings, recommendation percentages, review distribution, and industry categorisation across thousands of merchant domains.
Extract full review text, star ratings, verified buyer tags, and helpful vote counts paginated across the entire review history.
Isolate SKU level reviews, product ratings, and attached user generated content for detailed product feedback analysis.
Capture merchant replies, reply dates, and average response time metrics to audit customer service performance.
Extract localised branch reviews for multi-location businesses, complete with address details and branch specific ratings.
Extract source URLs for user generated images and video attachments uploaded alongside verified reviews.
Track reviewer history, total reviews submitted, and average rating given to identify serial complainers or brand advocates.
Capture custom tags and sentiment indicators applied to reviews by the platform or merchant.
Track rating trajectories across multiple domains in the same industry to map market positioning.
Brief in. Clean data out.
Provide domain lists, company URLs, or product SKUs. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, proxy rotation, and pagination logic for reviews.io.
Schema validation, null-rate checks, and sample review data verification before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Review platforms invest heavily in scraping detection. Here is how we stay resilient and deliver structured data consistently.
Our crawlers use residential ISP proxies with realistic browser fingerprints, randomised request timing, and full cookie session management trained on real user behaviour patterns.
Companies with hundreds of thousands of reviews require careful state management. We traverse deep paginations efficiently without dropping records or triggering rate limits.
We run full Playwright browser sessions with JavaScript execution to capture lazy loaded reviews, dynamic merchant replies, and interactive rating widgets.
For large merchant profiles, we maintain a hash index of last seen values. 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, schema drift, and coverage drops, responding before you notice.
Track negative sentiment spikes and monitor response times to protect brand equity.
Benchmark customer satisfaction against industry rivals to identify service gaps.
Analyse SKU level reviews to identify product defects and inform manufacturing improvements.
Monitor franchise performance across geographic locations using branch specific review data.
Ingest and display aggregated ratings on internal dashboards for executive visibility.
Audit merchant reply rates, resolution tone, and response latency to enforce SLA compliance.
"Reviews.io holds critical signals on merchant reliability and product quality. Aggregating this data across thousands of domains requires dedicated extraction infrastructure."
Most teams underestimate the investment required. Reliable Reviews.io scraping requires residential proxies, full JavaScript rendering, CAPTCHA handling, deep pagination traversal, and daily selector maintenance. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our reviews.io 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. Rotation happens per request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
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 reviews.io scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Reviews.io is generally permissible under applicable law. DataFlirt targets only public, non-authenticated company profiles and reviews. We do not extract personal data or violate GDPR.
We use specific traversal techniques and state management to navigate deep review histories, ensuring we capture the full catalogue of reviews without missing records.
Yes. We extract SKU level product reviews, including star ratings, text, and attached media, mapped back to the parent product.
Pipelines can be configured for daily, weekly, or hourly runs. Incremental diffing ensures you receive updates on new reviews and merchant replies rapidly.
Yes. We extract the full text of merchant replies along with the timestamp, allowing you to calculate response times and audit customer service tone.
Our packages start at a defined list of domains or product SKUs with scheduled delivery. Contact us with your target volume for a scoped quote.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off competitor audit or a continuous feed of new reviews across 5,000 domains, we scope, build, and operate the pipeline. Tell us what you need.
