We extract flight schedules, dynamic pricing, seat availability, and fare classes directly from Air India. 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 Flight Schedules objects from airindia.com. All fields typed and schema-versioned.
"flight_number": "AI101", "origin_airport": "DEL", "destination_airport": "JFK", "departure_time": "2026-10-12T02:20:00Z", "arrival_time": "2026-10-12T07:30:00Z", "duration_minutes": 910, "aircraft_type": "Boeing 777-300ER", "stops": 0, "code_share": false
| # | flight_number | origin_airport | destination_airport | departure_time | arrival_time | duration_minutes |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Fares objects from airindia.com. All fields typed and schema-versioned.
"flight_number": "AI101", "departure_date": "2026-10-12", "cabin_class": "Economy", "fare_basis": "YLE14IN", "base_fare": 42500.0, "taxes": 12450.0, "total_fare": 54950.0, "currency": "INR", "seats_remaining": 4, "refundable": false
| # | flight_number | departure_date | cabin_class | fare_basis | base_fare | taxes |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Route Network objects from airindia.com. All fields typed and schema-versioned.
"origin_code": "DEL", "origin_city": "New Delhi", "destination_code": "LHR", "destination_city": "London", "distance_km": 6714, "flight_frequency": "Daily", "direct_flight": true, "operator": "Air India", "terminal_origin": "T3", "terminal_dest": "T2"
| # | origin_code | origin_city | destination_code | destination_city | distance_km | flight_frequency |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Baggage & Amenities objects from airindia.com. All fields typed and schema-versioned.
"flight_number": "AI101", "cabin_class": "Economy", "check_in_allowance": "2PC", "cabin_allowance": "7KG", "excess_baggage_fee": 2500.0, "wifi_available": false, "meal_included": true, "entertainment": "Seatback Screen", "power_outlets": true
| # | flight_number | cabin_class | check_in_allowance | cabin_allowance | excess_baggage_fee | wifi_available |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Availability Status objects from airindia.com. All fields typed and schema-versioned.
"flight_number": "AI101", "departure_date": "2026-10-12", "booking_class": "Y", "status_code": "Available", "waitlist_allowed": true, "available_seats": 9, "last_updated": "2026-05-12T09:14:00Z", "equipment": "77W"
| # | flight_number | departure_date | booking_class | status_code | waitlist_allowed | available_seats |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Air India scraper navigates complex booking flows, handles dynamic pricing updates, and bypasses airline anti-bot systems to deliver accurate schedule and fare data.
Extract origin, destination, departure times, arrival times, and aircraft equipment across the entire Air India network.
Capture base fares, taxes, surcharges, and total prices across multiple currencies and passenger types.
Extract distinct pricing and availability for Economy, Premium Economy, Business, and First Class cabins.
Identify flights operated by Star Alliance partners but marketed under Air India flight numbers.
Monitor remaining seat counts per booking class to estimate load factors and demand curves.
Capture check-in and cabin baggage allowances mapped to specific fare families and routes.
Extract complex itineraries including layover durations, terminal changes, and minimum connection times.
Track assigned aircraft types for specific routes to monitor fleet deployment and capacity changes.
Isolate base fare from YQ surcharges, airport taxes, and government fees for accurate pricing analysis.
Run daily network-wide schedule syncs or high-frequency polling on specific high-value routes.
Brief in. Clean data out.
Provide origin-destination pairs, travel dates, or specific flight numbers. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, manage stateful booking sessions, and handle Akamai bot protection.
Schema validation, null-rate checks, and price-outlier detection before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Airlines deploy aggressive anti-scraping measures. Here is how we maintain stable extraction pipelines for Air India.
Air India uses enterprise bot protection to block automated traffic. We route requests through residential proxies and spoof TLS fingerprints to ensure high success rates.
Flight searches require maintaining state across multiple steps: selecting dates, passengers, and cabins. We use Playwright to execute these flows exactly like a human user.
Fares and seat availability are loaded dynamically via API calls after the initial page load. Our infrastructure captures these network payloads directly.
We maintain a hash index of last-seen fares per route. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Every run emits structured logs. We alert on null-rate spikes, missing routes, and schema drift, responding before you notice.
Online Travel Agencies monitor direct-channel pricing to ensure parity and optimise their own markups.
Rival airlines track Air India pricing on overlapping routes to adjust their own revenue management algorithms.
Meta-search engines use structured schedule and fare data to populate their flight comparison tools.
Aviation analysts track flight frequencies, deployed aircraft types, and estimated load factors to model route profitability.
Enterprise travel managers monitor historical pricing trends to negotiate better corporate rates.
Consultancies track network expansion, capacity deployment, and code-share agreements to evaluate market share.
"Air India pricing changes dynamically based on load factors and booking curves. Capturing this data requires navigating complex, multi-step search flows."
Airlines protect their inventory data fiercely. Reliable extraction from airindia.com requires defeating advanced bot protection, maintaining stateful booking sessions, and parsing complex fare rules. DataFlirt manages this infrastructure so you receive clean, structured flight data without the operational overhead.
Everything supported by our airindia.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 crawl orchestration and deduplication. Playwright handles JavaScript rendering, stateful booking sessions, and interaction flows.
We maintain pools of residential ISP proxies to bypass airline bot protection. Rotation happens per-request with sticky sessions for multi-step searches.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About airindia.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available flight schedules and pricing data is generally permissible. DataFlirt targets only public, non-authenticated information. We do not extract personal PNR data or circumvent authentication walls. Clients should review applicable terms and consult legal counsel.
We use residential ISP proxies, full Playwright browser sessions with realistic TLS fingerprints, and stateful session management to navigate the booking flow reliably.
Yes. Our pipeline supports complex routing, capturing layover durations, terminal changes, and combined fare rules for multi-leg journeys.
We configure pipeline cadence based on your requirements. High-priority routes can be polled hourly, while full network schedule syncs typically run daily.
Yes. We extract separate pricing, availability, and fare rules for Economy, Premium Economy, Business, and First Class where available on the route.
Yes. We parse the fare breakdown to isolate base fare, YQ surcharges, airport taxes, and other fees into distinct structured fields.
Our smallest packages start at a defined list of 500 origin-destination pairs with daily delivery. Contact us with your route list for a scoped quote.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a daily schedule sync or continuous price monitoring across 5,000 routes - we scope, build, and operate the pipeline. Tell us what you need.
