We extract motor, health, and life insurance premiums, policy inclusions, rider pricing, and insurer metrics from Turtlemint. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Health Insurance Quotes objects from turtlemint.com. All fields typed and schema-versioned.
"plan_id": "HLTH-HDFC-098", "insurer_name": "HDFC Ergo", "plan_name": "Optima Restore", "sum_insured": 1000000, "annual_premium": 12450.0, "network_hospitals_count": 10540, "co_pay_pct": 0, "scraped_at": "2026-05-12T09:14:00Z"
| # | plan_id | insurer_name | plan_name | sum_insured | monthly_premium | annual_premium |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Motor Insurance objects from turtlemint.com. All fields typed and schema-versioned.
"vehicle_reg": "MH-01", "make": "Hyundai", "model": "Creta", "variant": "SX Opt Diesel", "idv_value": 850000, "total_premium": 18450.0, "insurer_name": "ICICI Lombard", "zero_dep_cover": true
| # | vehicle_reg | make | model | variant | reg_year | ncb_pct |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Term Life Insurance objects from turtlemint.com. All fields typed and schema-versioned.
"insurer_name": "Max Life", "life_cover_amount": 10000000, "policy_term_years": 40, "monthly_premium": 1150.0, "claim_settlement_ratio": 99.51, "critical_illness_rider": 250.0, "waiver_of_premium": true
| # | plan_id | insurer_name | life_cover_amount | policy_term_years | premium_payment_term | monthly_premium |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Policy Features objects from turtlemint.com. All fields typed and schema-versioned.
"plan_id": "HLTH-HDFC-098", "feature_category": "Room Rent", "feature_name": "Single Private Room", "is_covered": true, "waiting_period_months": 0, "sub_limit_amount": "None", "updated_at": "2026-05-12T08:00:00Z"
| # | plan_id | feature_category | feature_name | is_covered | waiting_period_months | sub_limit_amount |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Insurer Profiles objects from turtlemint.com. All fields typed and schema-versioned.
"insurer_name": "Star Health", "category": "Health", "total_network_hospitals": 14000, "claim_settlement_ratio": 90.0, "solvency_ratio": 2.1, "customer_rating": 4.2
| # | insurer_id | insurer_name | category | total_network_hospitals | total_garages | claim_settlement_ratio |
|---|---|---|---|---|---|---|
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Our Turtlemint scraper handles the platform's dynamic quote generation, API payload simulation, and session management to extract accurate premium matrices across all major insurance categories.
Simulate user parameters like age, pincode, and vehicle IDV to generate and extract real-time premium quotes across all insurers.
Extract sum insured tiers, room rent limits, co-pay percentages, waiting periods, and maternity cover details for health policies.
Capture third-party liabilities, own-damage premiums, zero-depreciation add-ons, and engine protection riders based on vehicle variants.
Track life cover amounts, policy terms, critical illness riders, and accidental death benefit pricing across different age brackets.
Scrape cashless garage and network hospital lists per insurer, categorised by city and pincode.
Extract claim settlement ratios, solvency margins, and customer rating metrics for every insurer listed on the platform.
Map pre-existing disease waiting periods and specific policy exclusions to normalise comparisons across plans.
Track market share percentages, founding years, and broad category offerings for all listed insurance companies.
Run continuous pipelines to track premium changes, new product launches, and IDV depreciation shifts over time.
Brief in. Clean data out.
Provide demographic parameters, vehicle lists, or pincodes. We design the extraction schema together.
We configure Playwright crawlers, payload simulators, and session management to handle Turtlemint's React application.
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 rely on complex state machines and dynamic API calls. Here is how we extract data reliably without hitting rate limits.
Turtlemint does not serve static HTML quotes. Premiums are generated via complex API payloads requiring specific tokens, session IDs, and demographic inputs. We reverse-engineer these API calls to request data programmatically.
Generating a quote requires valid session cookies and CSRF tokens. Our infrastructure handles automated token generation, rotation, and lifecycle management to prevent 401 Unauthorized errors.
Aggregators aggressively rate-limit repetitive quote requests from datacenter IPs. We route traffic through Indian residential proxies to mimic genuine consumer traffic and bypass IP-based throttling.
Different insurers return policy features in different formats. We apply a strict normalisation layer to ensure 'Room Rent Limit' or 'Zero Dep' means the same thing across HDFC Ergo, ICICI Lombard, and Star Health.
Insurance APIs sometimes return default or error values when backend services fail. We implement statistical boundary checks to flag premium quotes that deviate significantly from expected ranges.
Insurance companies monitor competitor pricing across demographics and vehicle variants to adjust their own actuarial models.
Product teams identify missing features in their own policies by analysing the inclusion matrices of top-selling plans.
Data science teams ingest historical premium data to train predictive models for risk assessment and pricing elasticity.
Analysts track the visibility and placement of specific insurers on aggregator platforms to estimate market penetration.
Insurers audit Turtlemint to ensure their policies, riders, and settlement ratios are displayed accurately to consumers.
Brokerages track high-frequency changes in motor insurance IDV calculations and discount structures.
"Insurance aggregation platforms are complex state machines. Extracting accurate premium matrices requires simulating thousands of user profiles programmatically."
Most extraction attempts fail at the dynamic quote generation stage. Turtlemint requires precise payload structures, valid session tokens, and realistic input parameters. DataFlirt manages this state complexity so you can ingest clean premium data without fighting React forms and bot protection.
Everything supported by our turtlemint.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 complex React state management, cookie sessions, and token generation required to access dynamic quote APIs.
We maintain pools of Indian residential ISP proxies. Rotation happens per-request with sticky sessions to maintain token validity.
Pipelines run on AWS Lambda and ECS. Airflow handles demographic parameter iteration, dependency management, and SLA alerting.
Data delivered to where your team already works — no new tooling required.
About turtlemint.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly accessible, non-authenticated premium quotes and policy features is generally permissible. DataFlirt targets only public aggregator data. We do not extract personal user data, bypass OTP walls, or scrape purchased policy documents.
We programmatically simulate form inputs (like vehicle registration, age, and pincode) by reverse-engineering the API payloads. This allows us to request quotes at scale without manually rendering the UI.
Yes. You provide the demographic matrix (e.g., ages 25-55 across 10 major Indian cities), and we iterate through the combinations to generate comprehensive premium datasets.
No. We extract the initial and detailed quotes available before the mandatory OTP verification step. Final binding quotes requiring user authentication are not supported.
Pipelines can run daily, weekly, or monthly depending on your requirements. High-frequency tracking for specific vehicle models or health plans can be configured hourly.
We support the extraction of Motor (Car and Two-Wheeler), Health, and Term Life insurance categories available on the Turtlemint platform.
Yes. We offer sample datasets for a limited set of parameters (e.g., 5 vehicle models or 3 age brackets) during the scoping phase to validate schema and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off extraction of health policy features or continuous premium tracking across thousands of vehicle variants - we scope, build, and operate the pipeline. Tell us what you need.
