We extract carrier comparisons, premium estimates, coverage tiers, and review data from The Zebra. 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 Auto Quotes objects from zebra.com. All fields typed and schema-versioned.
"zip_code": "78701", "vehicle_make": "Toyota", "vehicle_model": "Camry", "vehicle_year": 2022, "carrier_name": "Progressive", "monthly_premium": 142.5, "annual_premium": 1710.0, "coverage_type": "Comprehensive", "deductible_amount": 500
| # | zip_code | vehicle_make | vehicle_model | vehicle_year | carrier_name | monthly_premium |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Home Quotes objects from zebra.com. All fields typed and schema-versioned.
"zip_code": "78701", "property_type": "Single Family", "year_built": 2015, "carrier_name": "State Farm", "dwelling_coverage": 350000, "liability_coverage": 100000, "deductible": 1000, "monthly_premium": 89.0, "annual_premium": 1068.0
| # | zip_code | property_type | year_built | carrier_name | dwelling_coverage | liability_coverage |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Carrier Reviews objects from zebra.com. All fields typed and schema-versioned.
"carrier_id": "C-1042", "carrier_name": "Geico", "reviewer_name": "Sarah M.", "star_rating": 4.5, "review_date": "2026-03-14", "policy_type": "Auto", "helpful_votes": 12, "verified_customer": true
| # | carrier_id | carrier_name | reviewer_name | star_rating | review_date | review_text |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Coverage Tiers objects from zebra.com. All fields typed and schema-versioned.
"tier_name": "Better", "bodily_injury_limit": "50k/100k", "property_damage_limit": "50k", "uninsured_motorist": true, "comprehensive_deductible": 500, "collision_deductible": 500, "roadside_assistance": true, "rental_reimbursement": false
| # | tier_name | bodily_injury_limit | property_damage_limit | uninsured_motorist | comprehensive_deductible | collision_deductible |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Discount Profiles objects from zebra.com. All fields typed and schema-versioned.
"carrier_name": "Allstate", "discount_name": "Safe Driving Bonus", "discount_type": "Telematics", "estimated_savings_pct": 15, "driver_requirement": "Clean record for 6 months", "stacking_allowed": true, "verification_needed": true
| # | carrier_name | discount_name | discount_type | estimated_savings_pct | driver_requirement | vehicle_requirement |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our insurance scraper handles every layer of the platform: multi-step quote funnels, dynamic pricing models, state-level compliance logic, and the review corpus - with JavaScript rendering, session management, and anti-bot circumvention built in.
Automated navigation through complex, multi-step React quote funnels. We supply demographic and vehicle matrices to generate accurate premium tables.
Map premiums across 41,000+ US zip codes to build comprehensive geographical pricing models for auto and home policies.
Iterate systematically over make, model, and year combinations to track how vehicle risk profiles affect carrier pricing.
Extract rate differences between Geico, Progressive, State Farm, and regional carriers for identical driver profiles.
Standardise Basic, Better, and Best coverage tiers across carriers into a unified schema for accurate apples-to-apples comparison.
Capture telematics, multi-policy, good student, and safe driver discount structures to reverse-engineer competitor pricing strategies.
Extract customer satisfaction scores, claims experience text, and verified customer tags across the entire carrier database.
Handle state-specific minimum coverage requirements automatically when generating quote requests across different jurisdictions.
Maintain secure cookie state across complex quote funnels without triggering rate limits or bot protection systems.
Brief in. Clean data out.
Provide target zip codes, vehicle matrices, or demographic profiles. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for zebra.com.
Schema validation, null-rate checks, premium-outlier detection, and sample outputs before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Insurance aggregators invest heavily in scraping detection to protect their rate tables. Here is how we stay resilient - and why teams choose managed infrastructure over DIY.
The Zebra uses advanced bot protection that operates on TLS fingerprints, browser headers, and IP reputation. Our crawlers use US residential ISP proxies with realistic browser fingerprints and full cookie session management.
Insurance quotes require navigating deep, stateful React forms. We run full Playwright browser sessions with JavaScript execution to fill forms, handle AJAX transitions, and extract the final rate tables.
Every carrier displays coverage limits and deductibles differently. Our extraction layer normalises these disparate formats into a clean, unified schema so you can query Progressive against Geico instantly.
For large zip code matrices, we maintain a hash index of last-seen premiums per profile. 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, form breakage, schema drift, and coverage drops - responding before you notice.
Insurance carriers monitor competitor pricing across thousands of zip codes to adjust their own rate filings and protect market share.
Actuarial teams compare their internal risk pricing models against live market quotes to identify margin opportunities.
Strategy teams analyse carrier dominance and pricing floors in new geographical regions before launching products.
NLP models process carrier reviews to track claims satisfaction and identify competitor service weaknesses.
Product managers track how competitors structure multi-policy and telematics discounts to optimise their own offerings.
Carriers monitor which competitors consistently win the top recommendation slot on The Zebra for specific driver profiles.
"The Zebra aggregates the US insurance market, but extracting those rate tables requires navigating complex, stateful funnels that block standard crawlers instantly."
Most teams underestimate the investment required: reliable insurance data extraction requires residential proxies, full JavaScript rendering for multi-step React forms, CAPTCHA handling, and anomaly monitoring. DataFlirt absorbs that complexity so your actuaries and engineers can focus on the analysis - not the infrastructure.
Everything supported by our zebra.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.
Scrapy handles orchestration and data normalisation. Playwright handles the heavy JavaScript execution required to navigate stateful insurance quote forms.
We maintain pools of US residential ISP proxies. Rotation happens per session with sticky cookies to ensure quote funnels complete successfully.
Pipelines run on AWS ECS for sustained form-filling workloads. Airflow handles scheduling, matrix iteration, and SLA alerting.
Data delivered to where your team already works — no new tooling required.
About zebra.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available aggregate rate data is generally permissible. DataFlirt targets only non-authenticated, generic profile quotes. We do not extract personal data, circumvent authentication walls, or input real PII. Clients should review terms of service and consult legal counsel for specific use cases.
We use full Playwright browser sessions to programmatically fill demographic and vehicle data, handle React state transitions, and wait for carrier API responses before extracting the final rate table.
Yes. We accept zip code matrices and distribute the form-filling workload across our container infrastructure to map rates nationally or regionally.
Data freshness depends on the size of your input matrix. Small regional profiles can be updated daily. National 41,000+ zip code sweeps typically run on a weekly or monthly cadence due to form-filling latency.
Yes. We extract the full corpus of carrier reviews, including star ratings, review text, policy type, and helpful vote counts.
Our smallest packages start at a defined matrix of zip codes and vehicle profiles with monthly delivery. For larger national matrices or continuous monitoring, we price based on compute volume.
No. We only use generic, anonymised demographic profiles to generate aggregate estimates. We never input real Social Security Numbers or trigger hard credit inquiries.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a baseline carrier comparison or continuous rate monitoring across 40,000 zip codes - we scope, build, and operate the pipeline. Tell us what you need.
