We extract dynamic premium rates, policy inclusions, claim settlement ratios, and hospital networks from Coverfox. 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 Health Insurance Plans objects from coverfox.com. All fields typed and schema-versioned.
"plan_id": "HLTH-4921", "insurer_name": "HDFC ERGO", "plan_name": "Optima Secure", "sum_insured": 1000000, "premium_amount": 14592, "claim_settlement_ratio": 98.2, "network_hospitals": 11400, "copay_pct": 0
| # | plan_id | insurer_name | plan_name | sum_insured | premium_amount | claim_settlement_ratio |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Term Life Quotes objects from coverfox.com. All fields typed and schema-versioned.
"quote_id": "TERM-8812", "insurer": "Max Life", "policy_name": "Smart Secure Plus", "cover_amount": 10000000, "policy_term": 40, "premium_annual": 12400, "claim_settlement_ratio": 99.5, "medical_test_required": true
| # | quote_id | insurer | policy_name | cover_amount | policy_term | premium_monthly |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Motor Insurance objects from coverfox.com. All fields typed and schema-versioned.
"vehicle_type": "Four Wheeler", "insurer": "ICICI Lombard", "plan_type": "Comprehensive", "idv_value": 450000, "total_premium": 11240, "ncb_discount": 20, "zero_depreciation": true, "roadside_assistance": true
| # | vehicle_type | insurer | plan_type | idv_value | own_damage_premium | third_party_premium |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Hospital Networks objects from coverfox.com. All fields typed and schema-versioned.
"hospital_id": "HOSP-9921", "hospital_name": "Apollo Hospitals", "city": "Bengaluru", "state": "Karnataka", "pin_code": "560076", "cashless_facility": true, "insurers_accepted": "['HDFC ERGO', 'Star Health', 'Care Health']", "accreditation": "NABH"
| # | hospital_id | hospital_name | address | city | state | pin_code |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Riders & Add-ons objects from coverfox.com. All fields typed and schema-versioned.
"rider_id": "RDR-114", "policy_id": "TERM-8812", "rider_name": "Critical Illness Plus", "rider_type": "Health", "premium_impact": 2400, "coverage_amount": 1000000, "waiting_period": 90
| # | rider_id | policy_id | rider_name | rider_type | premium_impact | coverage_amount |
|---|---|---|---|---|---|---|
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Our Coverfox scraper handles complex multi-step quotation forms, dynamic JavaScript rendering, and parameter permutation across demographic profiles to build comprehensive premium datasets.
Submit age, pin code, and medical history parameters to extract exact premium quotes across all listed insurers.
Extract side-by-side matrices of inclusions, exclusions, waiting periods, and room rent caps for comprehensive market analysis.
Input vehicle registration details to scrape Insured Declared Value and premium breakdowns across comprehensive and third-party plans.
Track historical and current claim settlement performance metrics for all major Indian insurers.
Extract the complete catalogue of cashless hospitals mapped to specific health insurance providers and geographical zones.
Capture dynamic pricing for zero depreciation, critical illness, and accidental death riders based on base policy parameters.
Map premium differences across tier-1 and tier-2 cities using automated pin code rotation.
Extract complex rule sets governing co-payments based on age brackets and zone classifications.
Capture medical test requirements and tobacco-user premium multipliers across term life products.
Brief in. Clean data out.
Provide demographic parameters, vehicle details, or target insurers. We design the extraction schema together.
We configure Playwright crawlers to handle form submissions, session tokens, and dynamic JS on coverfox.com.
Schema validation, null-rate checks, and premium outlier detection before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Insurance aggregators use complex state management and rate limiting to protect their pricing engines. Here is how we build resilient pipelines.
Coverfox requires multi-step form submissions to generate quotes. We maintain session state across requests to extract the final premium tables without triggering validation errors.
Premium tables and hospital networks load asynchronously via XHR. We use Playwright to intercept these payloads and extract clean JSON before it hits the DOM.
Scraping premium curves requires iterating through thousands of age, sum-insured, and pin-code combinations. Our orchestration layer distributes these requests efficiently.
Insurance aggregators monitor request velocity. We distribute form submissions across residential Indian IP pools to simulate organic user behaviour and bypass rate limits.
Insurers frequently update plan names and benefit structures. Our monitoring stack detects schema drift and alerts our engineers before your pipeline breaks.
Insurers monitor aggregator platforms to benchmark their premiums against rival products across demographic segments.
Actuaries analyse inclusion matrices and rider popularity to design new insurance products tailored to market gaps.
Analysts track the visibility and placement of specific insurers on aggregator platforms to gauge distribution strength.
TPAs map competitor cashless networks to identify gaps in their own hospital partnerships across geographies.
Insurers verify that their products are displayed with correct premiums and features on third-party platforms.
Research firms track premium inflation and coverage trends across health and motor segments over time.
"Insurance aggregators hold the ground truth for retail premium pricing in India, but accessing that data requires navigating complex multi-step quotation forms at scale."
Extracting quote data from Coverfox requires more than simple HTTP GET requests. It demands headless browsers, session state management, and parameter permutation across thousands of demographic profiles. DataFlirt handles the form submissions and IP rotation so your team can focus on actuarial analysis.
Everything supported by our coverfox.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
We orchestrate headless browsers to navigate Coverfox's multi-step quotation flows, handling dynamic inputs and session cookies.
Quote generation is highly sensitive to IP reputation. We route traffic through verified Indian residential IPs to ensure consistent response rates.
Pipelines run on AWS ECS with Airflow managing parameter distribution across thousands of parallel quote requests.
Data delivered to where your team already works — no new tooling required.
About coverfox.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available insurance quotes and plan details is generally permissible. DataFlirt targets only public, non-authenticated premium data. We do not extract personal user data or circumvent OTP walls.
We use Playwright to programmatically fill out forms, handle dropdowns, and manage session cookies, simulating a real user journey to reach the final premium tables.
Yes. We can configure the pipeline to iterate through a supplied list of pin codes to map geographical premium variations.
Pipelines can be scheduled daily, weekly, or monthly depending on your requirements. We recommend weekly runs to capture frequent insurer pricing updates.
We extract all structured data points displayed on the platform, including inclusions, exclusions, and waiting periods. Downloadable PDF policy wordings can be linked or downloaded upon request.
Our smallest packages start at a defined matrix of demographic profiles with weekly delivery. Contact us for a scoped quote.
Yes. We provide a sample run of up to 100 quote permutations as part of the pre-engagement scoping process to validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off hospital network dump or a continuous premium-monitoring feed across demographic profiles. Tell us what you need.
