We extract dynamic quote pricing, policy tiers, coverage limits, and inclusions from Cover-More. 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 Quote Pricing objects from covermore.com. All fields typed and schema-versioned.
"destination_country": "Japan", "start_date": "2026-10-01", "end_date": "2026-10-15", "traveler_age": 34, "plan_name": "Comprehensive", "premium_amount": 145.5, "currency": "AUD", "excess_amount": 250
| # | quote_id | destination_country | start_date | end_date | traveler_age | policy_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Policy Tiers objects from covermore.com. All fields typed and schema-versioned.
"plan_name": "Basic", "region": "AU", "cancellation_limit": 0, "medical_limit": "Unlimited", "luggage_limit": 2000, "rental_car_excess": 0, "covid_cover": false, "is_active": true
| # | plan_id | plan_name | region | cancellation_limit | medical_limit | luggage_limit |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Inclusions & Exclusions objects from covermore.com. All fields typed and schema-versioned.
"category": "Delayed Luggage", "inclusion_text": "Essential clothing and toiletries", "condition_type": "Time-based", "waiting_period": "12 hours", "excess_amount": 0, "sub_limit": 750, "evidence_required": true
| # | policy_id | category | inclusion_text | exclusion_text | condition_type | waiting_period |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for PDS Metadata objects from covermore.com. All fields typed and schema-versioned.
"document_id": "PDS-AU-COMP-2025", "region": "AU", "effective_date": "2025-01-01", "pdf_url": "https://www.covermore.com.au/pds/comprehensive.pdf", "version_number": "v4.2", "language": "en", "product_type": "Leisure Travel"
| # | document_id | region | effective_date | pdf_url | version_number | file_size |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Medical Conditions objects from covermore.com. All fields typed and schema-versioned.
"condition_name": "Asthma", "automatically_covered": true, "screening_required": false, "premium_loading": 0, "condition_category": "Respiratory", "age_limit": 59, "notes": "Must have no hospital admissions in past 12 months"
| # | condition_name | automatically_covered | screening_required | premium_loading | exclusion_applied | condition_category |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our Cover-More scraper navigates multi-step quote engines, handles dynamic form state, and extracts structured policy data across all regional domains.
Automate form inputs for destination, dates, and traveler demographics to generate and capture the full pricing matrix.
Extract limits, sub-limits, and excess options across Basic, Comprehensive, and Annual Multi-Trip plans.
Scrape data across covermore.com.au, covermore.co.nz, covermore.co.uk, and other regional domains using local residential IPs.
Map automatically covered conditions versus those requiring custom screening and premium loading.
Monitor Product Disclosure Statements for version updates, effective date changes, and structural policy shifts.
Capture premium adjustments for optional extras like winter sports cover, motorcycle riding, and rental car excess reduction.
Identify premium fluctuations and limit adjustments over time with hash-based diffing on daily runs.
Handle complex React and Angular state machines within the quote engine using full Playwright browser sessions.
Run thousands of quote permutations concurrently to build comprehensive competitor pricing models.
Brief in. Clean data out.
Provide destination lists, date ranges, and demographic profiles. We design the extraction schema together.
We configure Playwright form automation, proxy rotation, and session management for the quote engine.
Schema validation, null-rate checks, and premium outlier detection before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Insurance providers use complex multi-step forms and session tokens to prevent automated quoting. Here is how we bypass these restrictions.
The Cover-More quote engine relies on client-side state. We use Playwright to execute full browser sessions, interacting with the DOM exactly as a human would to generate valid quote tokens.
Multi-page quote forms break if the IP address changes mid-session. Our residential proxy infrastructure maintains sticky sessions for the duration of the quote generation flow.
Cover-More alters pricing and availability based on the user's origin. We route requests through residential IPs matching the target domain region to ensure accurate, localised premium data.
Generating thousands of quotes triggers API rate limits. We distribute the workload across a vast proxy pool, randomising request intervals and user-agent profiles to blend with normal traffic.
Insurance frontends update frequently. We employ multi-layer fallback chains for element selection, ensuring a minor CSS change does not disrupt the pricing extraction pipeline.
Insurance underwriters track Cover-More premiums across thousands of demographic and destination permutations to optimise their own pricing models.
Travel comparison sites maintain up-to-date pricing and policy limits without relying on slow or rate-limited official APIs.
Actuaries analyse destination risk tiers and age-based premium loading to identify market trends and adjust internal risk frameworks.
Product managers monitor changes to inclusions, medical condition screening rules, and optional add-ons to identify coverage gaps in the market.
Regulatory teams track PDS updates and disclaimers to ensure industry-wide compliance and monitor shifting legal definitions of coverage.
New entrants use historical pricing data and tier structures to design competitive travel insurance products for specific regions.
"Travel insurance pricing is highly dynamic, gated behind complex multi-step quote engines. We automate the inputs to extract the pricing matrix at scale."
Scraping Cover-More requires managing complex state machines: inputting destinations, dates, and traveler demographics to generate valid quotes. DataFlirt handles the form automation, session management, and rate limiting so your team receives clean pricing matrices without building the infrastructure.
Everything supported by our covermore.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.
Playwright handles JavaScript rendering, cookie sessions, and multi-step interaction flows required to generate valid insurance quotes.
We maintain pools of residential ISP proxies with sticky session support, preventing IP-based blocks during the quote generation process.
Pipelines run on Kubernetes and AWS Lambda. Airflow manages the execution of thousands of quote permutations with strict dependency tracking.
Data delivered to where your team already works — no new tooling required.
About covermore.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available pricing and policy information is generally permissible under applicable laws. DataFlirt targets only public, non-authenticated quote engines and PDS documents. We do not extract personal data, breach policyholder portals, or violate privacy regulations. Clients should review terms of service and consult legal counsel for specific use cases.
We distribute requests across a large pool of residential proxies, maintaining sticky sessions for the multi-step form process. Request timing is randomised to mimic human behaviour, preventing rate limit triggers and IP bans.
Yes. We support extraction from covermore.com.au, covermore.co.nz, covermore.co.uk, and other regional variants. We route traffic through local IPs to ensure accurate, region-specific pricing and policy terms.
Our automation scripts use robust, multi-layer fallback selectors. If a DOM structure changes, the pipeline automatically attempts alternative selection methods. We monitor error rates closely and patch automation scripts promptly.
Yes. Every pipeline run produces timestamped records. By providing a static list of quote permutations (destination, dates, age), we can track premium fluctuations and highlight changes via our diffing engine.
Our smallest packages start at a defined set of quote permutations (e.g., 5,000 specific scenarios) with weekly delivery. For larger matrices or real-time API integrations, we price based on compute volume and delivery frequency.
Absolutely. We provide a sample run of up to 500 quote permutations as part of the pre-engagement scoping process, allowing you to validate schema fit and data accuracy before committing.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need daily quote benchmarking or a full extraction of policy limits - we scope, build, and operate the pipeline. Tell us what you need.
