We extract dynamic premium quotes, cashless garage networks, hospital directories, and policy documentation from Digit. 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 Motor Quotes objects from digitinsurance.com. All fields typed and schema-versioned.
"vehicle_make": "Hyundai", "vehicle_model": "Creta", "rto_code": "KA-01", "idv_value": 850000.0, "base_premium": 12450.0, "ncb_discount_pct": 20, "zero_dep_addon": 3200.0, "total_premium": 18467.0
| # | vehicle_make | vehicle_model | rto_code | registration_year | idv_value | base_premium |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Health Plans objects from digitinsurance.com. All fields typed and schema-versioned.
"plan_name": "Digit Health Care Plus", "sum_insured": 1000000.0, "age_band": "31-35", "family_size": "2A+1C", "base_premium": 14200.0, "room_rent_limit": "No Limit", "waiting_period_months": 24, "total_premium": 16756.0
| # | plan_name | sum_insured | age_band | family_size | base_premium | room_rent_limit |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Network Hospitals objects from digitinsurance.com. All fields typed and schema-versioned.
"hospital_id": "HOSP-8492", "hospital_name": "Manipal Hospital", "city": "Bengaluru", "state": "Karnataka", "pincode": "560017", "cashless_facility": true, "latitude": 12.9591, "longitude": 77.6474
| # | hospital_id | hospital_name | address_line1 | city | state | pincode |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Cashless Garages objects from digitinsurance.com. All fields typed and schema-versioned.
"garage_id": "GAR-3910", "garage_name": "Trident Hyundai Service", "city": "Bengaluru", "pincode": "560025", "authorized_brands": "['Hyundai']", "four_wheeler_support": true, "latitude": 12.9716, "longitude": 77.5946
| # | garage_id | garage_name | address | city | state | pincode |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Travel Insurance objects from digitinsurance.com. All fields typed and schema-versioned.
"destination_region": "Schengen", "trip_duration_days": 15, "traveler_age": 32, "medical_cover_usd": 250000, "flight_delay_cover": true, "base_premium_inr": 1850.0, "total_premium_inr": 2183.0, "quote_timestamp": "2026-05-12T10:15:00Z"
| # | destination_region | trip_duration_days | traveler_age | medical_cover_usd | trip_cancellation_cover | baggage_loss_cover |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our pipeline automates the complex form submissions required to extract premium quotes across thousands of demographic and geographic permutations.
Automated form filling for RTO codes, vehicle makes, models, and registration years to extract IDV and premium matrices.
Iterate through age bands, family sizes, and sum insured values to map base rates and total premiums.
Extract entire directories of network garages, including authorised brands, geo-coordinates, and contact details.
Scrape cashless hospital lists by city and state to track network density and empanelment status.
Capture premium variations based on destination zones, trip durations, and medical cover limits.
Isolate the cost of specific riders like zero depreciation, engine protection, or maternity cover across base plans.
Download and parse PDF policy wordings, terms, and conditions into structured text blocks for NLP analysis.
Capture latitude and longitude coordinates for service centres and hospitals to build proximity models.
Run daily or weekly quote generation pipelines to detect rate changes and promotional discounts.
Brief in. Clean data out.
Provide input variables like RTO codes, vehicle models, or age bands. We design the extraction matrix.
We configure Playwright scripts to navigate quote funnels, handle dynamic DOM elements, and manage session tokens.
Schema validation, premium outlier detection, and network list completeness checks before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting rates from modern insurance platforms requires heavy browser automation and session management. Here is how we handle Digit's infrastructure.
Digit's quote generation relies on multi-step React forms that validate inputs client-side. We use full Playwright browser sessions to simulate user input, trigger validation events, and navigate the funnel to reach the final premium breakdown.
Quote endpoints often require temporary session tokens generated during the initial page load. Our pipeline captures these tokens and headers, applying them to subsequent API requests to speed up data extraction without rendering the full UI every time.
Generating thousands of quotes from a single IP triggers immediate blocks. We distribute form submissions across a pool of Indian residential IPs, keeping request velocity below threshold limits per node.
Insurance quotes return highly nested JSON with base rates, taxes, and optional riders. We flatten and normalise this data into strict relational schemas, ensuring every output row is ready for analytical querying.
We hash the output of specific quote permutations. Subsequent runs only flag when a premium or IDV calculation changes, providing your actuaries with a clean ledger of rate adjustments over time.
Insurtech startups and legacy carriers monitor Digit's pricing across key demographics to adjust their own underwriting models.
Comparison portals ingest raw rate tables to populate their platforms without relying solely on official API partnerships.
Data science teams analyse IDV depreciation curves and add-on pricing strategies to reverse-engineer competitor risk models.
Healthcare and auto-service networks analyse hospital and garage distribution to identify gaps in their own cashless networks.
Product managers track new rider introductions, policy wording changes, and deductible tiers to inform new insurance products.
Consultancies track the expansion of Digit's service networks across tier-2 and tier-3 cities to model market penetration.
"Digit Insurance calculates premiums dynamically based on thousands of variables. Capturing this rate matrix requires high-concurrency form automation, not simple HTTP requests."
Most teams underestimate the compute required to map insurance pricing logic. Extracting quotes at scale requires full browser sessions, automated form filling for RTO and IDV variables, and bypass mechanisms for rate limiting. DataFlirt absorbs that complexity so your actuaries can focus on pricing models rather than maintaining web scrapers.
Everything supported by our digitinsurance.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 manages concurrency and input matrices, while Playwright handles the complex DOM interactions required to generate quotes.
We route requests through Indian residential IPs to ensure location-based pricing logic triggers correctly and rate limits are avoided.
Airflow schedules matrix runs across AWS ECS clusters, ensuring thousands of quote permutations complete within required timeframes.
Data delivered to where your team already works — no new tooling required.
About digitinsurance.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly accessible quote generation tools and network directories is generally permissible. DataFlirt extracts only public, non-authenticated pricing and location data. We do not bypass OTP walls or extract personally identifiable information (PII). Clients should consult legal counsel regarding their specific use cases.
We require a defined input matrix (e.g., a list of 5,000 specific vehicle variants and RTO codes). We distribute these inputs across our cluster, using Playwright and session reuse to generate quotes concurrently.
We target the web application endpoints. In most cases, the web platform and mobile app rely on the same underlying pricing APIs, allowing us to capture identical rate data.
Depending on the matrix size, we can run daily, weekly, or monthly pipelines. A standard matrix of 10,000 permutations typically completes within a 4-hour window.
Yes. We can configure the pipeline to select specific add-ons during the quote process, capturing the marginal cost of zero depreciation, engine protection, or maternity covers.
Our pipelines are monitored 24/7. If a DOM change breaks the extraction flow, our alerting system flags the failure, and our engineering team updates the Playwright scripts to restore service.
Yes. We provide a sample run based on a small subset of your input variables to validate schema structure and premium accuracy before full deployment.
20-minute scoping call. Pilot dataset within the week. Production within two. Provide your target variables and we will build the infrastructure to extract Digit's rate tables and network directories at scale.
