WEB DATA, ENGINEERED
Crawl Anything.
Build Everything.
AuraCrawl turns any public webpage into clean, structured data — just give us a URL and describe what you need.
{
"product": "Adidas Regular Fit Men’s T-shirt (Navy Blue)",
"size": "Small",
"price_inr": 789,
"mrp_inr": 999,
"rating": 3.98,
"in_stock": true
}
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AURA STRIKE
Built to get through Akamai, DataDome & PerimeterX-class defenses
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AURA VISION
Understands any page structure — no selectors, no schemas
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AURA BUILD
Clean, validated JSON on every delivery
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DELIVERY
Custom-scoped engagements, delivered by the engineers who build them
THE PIPELINE
See it. Strike through it. Build it.
Every engagement runs the same three-stage pipeline. You describe the data; the pipeline handles access, understanding, and structure.
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page pick an element
Aura Vision
See it
Reads and understands any webpage. Identifies the data you asked for regardless of markup structure — no CSS selectors, no rigid schemas to maintain.
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defenses none cleared
Aura Strike
Strike through it
Gets through the anti-bot defenses protecting hard targets — Akamai, DataDome, PerimeterX-class systems. However hardened the target, access is our problem, not yours.
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record buildingvalidated
Aura Build
Build it
Structures extracted results into clean, validated JSON. Typed fields, enforced formats, delivered through your preferred channel.
THE HARD PART
The open web fights back. You never see the fight.
A fingerprint scored as automated. A challenge rotated overnight. Markup reshuffled at 2am. A 200 that carries nothing at all. That is the surface every extraction actually meets, and absorbing it is the job — what reaches you is a validated record.
UNDER THE HOOD
Built like infrastructure, not like a script
A request takes the same path every time, and a run that dies resumes from where it stopped. Both of these are live — the run below can be killed and resumed.
- Entry router fetch · render · strike
- Render fleet headless workers
- Extraction semantic anchors
- Validation schema gate · drift
- Delivery s3 · warehouse · api
A record enters on the left and is only delivered once it clears the gate. Nothing reaches you unvalidated.
Press kill, then resume. Every target is recorded the moment it finishes, so a resumed run re-fetches only the one that was in flight.
Illustrative run. The resume behaviour is real; the target count is not a specific job.
THE PLATFORM
What you are actually buying
Six things an engagement gives you. Every panel below is operable — select a row, a destination, or a mode and watch what the pipeline actually does with it.
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[ 01 ]
Reliability
Data you can put in front of a customer.
Access, rendering, and IP strategy are all owned by us rather than stitched together from resellers. Records that fail validation are re-collected before delivery, not shipped with a caveat.
schema gate select a rowDelivered — typed, normalised, deduped.
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[ 02 ]
Extraction
Ask in a sentence. Get the fields.
Aura Vision resolves a plain-language request against whatever structure the page happens to use — including markup that buries the value you actually need behind scripts and layout noise.
extracted one pass -
[ 03 ]
Scale
Volume is an engineering problem, not your problem.
Concurrency, geo-aware routing, and session handling are sized to your target list during scoping. Pushing harder changes our capacity plan, not your integration.
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[ 04 ]
Delivery
Lands where your stack already lives.
A hosted endpoint, an S3 or GCS drop, a warehouse table, or a webhook stream. Deltas are deduplicated between runs, so you ingest what changed rather than re-processing the world.
deliver to pick a destinationWritten as partitioned files, on your schedule.
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[ 05 ]
Composability
Three collection modes, one response shape.
Plain fetch, full render, or hardened-target mode — chosen per source during scoping. The records that come out are shaped identically, so your parsing code never learns which path a page took.
collection mode same shape either wayGET /records?mode=fetch — for open sites.
identical response shape in all three -
[ 06 ]
Maintenance
Target changes are our incident, not your outage.
Layout changes and anti-bot rotations are watched and fixed on our side. If a source genuinely breaks, you hear it from us with the run that caught it — never from a silently emptying table.
USE CASES
What teams build on AuraCrawl
One pipeline, many jobs. Each engagement is scoped to your sources and delivered on your cadence.
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Review Intelligence
Reviews are the highest-signal customer data most companies never collect systematically.
Learn more -
E-commerce Intelligence
Pricing, assortment, and availability move daily on Indian marketplaces and quick-commerce apps — and they move by pincode, not nationally.
Learn more -
Market Research
Public web data answers market questions faster than panels and surveys — if you can collect it at scale.
Learn more -
Competitor Monitoring
Competitors signal constantly: price changes, new hires, product launches, shifting ad copy.
Learn more -
AI Agents
Agents that act on the web need fresh, structured observations of it — and they fail quietly when a scraper returns HTML soup.
Learn more -
RAG Pipelines
A RAG system is only as current as its corpus.
Learn more
INPUT → OUTPUT
A URL and a sentence. That is the whole spec.
No selector maintenance, no schema negotiation. Describe the data in plain language; get back records you can ship to production.
REQUEST
urlhttps://www.amazon.in/dp/B0FQFNQ5LX
ask“Get the product name, current price in ₹, the MRP, rating, stock status, and the delivery promise.”
{
"asin": "B0FQFNQ5LX",
"title": "Apple iPhone 17 Pro Max 256 GB — Cosmic Orange",
"price_inr": 143990,
"mrp_inr": 149900,
"discount_pct": 4,
"rating": 4.6,
"rating_count": 545,
"in_stock": true,
"delivery": "Free delivery"
}
Then choose how often that request runs. Cadence is scoped per source — try it:
A single snapshot — a dataset, not a feed.
freshness is the biggest single cost leverANY SHAPE
One extraction. Whatever shape your pipeline reads.
Every panel below is the same real record, captured from one Blinkit product page. You are not choosing between our formats: you are describing yours, down to field names and units.
Grouped the way an API consumer expects it — product, pricing and availability as separate objects. Note the rating: it is in the payload, not on the rendered page.
{
"source": "blinkit",
"captured_at": "2026-08-21T06:45:00Z",
"product": {
"id": "570279",
"name": "Adidas Regular Fit Men’s T-shirt (Navy Blue)",
"brand": "Adidas",
"size": "Small",
"category": "Men’s Sports Wear"
},
"pricing": {
"currency": "INR",
"price": 789,
"mrp": 999,
"discount_pct": 21
},
"availability": {
"in_stock": true,
"inventory": 3,
"merchant_type": "express"
},
"rating": 3.98
}
The same record mapped to warehouse column names. You send the schema; we conform to it — units, casing and all.
{
"vendor_code": "BLINKIT",
"item_id": "570279",
"item_name": "Adidas Regular Fit Men’s T-shirt (Navy Blue)",
"variant": "Small",
"selling_price": 789,
"list_price": 999,
"currency": "INR",
"stock_flag": "Y",
"units_on_hand": 3,
"fulfilment_tier": "express",
"ingested_at": "2026-08-21T06:45:00Z"
}
One line per size variant. Price differs by size because different merchants fulfil them, and XL/XXL were out of stock at capture.
{"item_id":"570279","variant":"Small","price":789,"inventory":3,"merchant":"express"}
{"item_id":"570281","variant":"Medium","price":809,"inventory":1,"merchant":"longtail"}
{"item_id":"570283","variant":"Large","price":799,"inventory":1,"merchant":"longtail"}
{"item_id":"570284","variant":"XL","price":null,"inventory":0,"merchant":"longtail"}
{"item_id":"570286","variant":"XXL","price":null,"inventory":0,"merchant":"longtail"}
Flattened with a header row, for spreadsheets and COPY-style loads. Five real rows from the same capture.
item_id,name,variant,price,mrp,inventory,merchant
570279,Adidas Regular Fit Mens T-shirt (Navy Blue),Small,789,999,3,express
570283,Adidas Regular Fit Mens T-shirt (Navy Blue),Large,799,999,1,longtail
570274,Adidas Regular Fit Mens T-shirt (White),Small,769,999,1,express
569469,Decathlon Domyos Regular Fit T-Shirt (Black),Small,399,499,2,express
569461,Decathlon Domyos Regular Fit T-Shirt (Black),Large,389,499,2,longtail
Idempotent upserts keyed the way your table is keyed — replayable without duplicating a row.
INSERT INTO catalog_prices (item_id, name, variant, price, mrp, inventory, merchant, ingested_at)
VALUES ('570279', 'Adidas Regular Fit Mens T-shirt (Navy Blue)', 'Small', 789, 999, 3, 'express', '2026-08-21T06:45:00Z')
ON CONFLICT (item_id) DO UPDATE SET
price = EXCLUDED.price,
inventory = EXCLUDED.inventory,
ingested_at = EXCLUDED.ingested_at;
Prose-shaped, pre-chunked, with provenance retained — for retrieval corpora rather than tables.
## Adidas Regular Fit Men’s T-shirt (Navy Blue) — Small
Price: ₹789 (MRP ₹999, 21% off). In stock, 3 units, express fulfilment.
Rated 3.98. Captured 21 Aug 2026 from blinkit.
Larger sizes are fulfilled by a different merchant and priced differently;
XL and XXL were unavailable at capture time.
<!-- chunk_id: blinkit-570279#0 | tokens: 68 | source_url retained -->
For systems that still speak XML, and they are more of them than anyone admits.
<record source="blinkit" captured_at="2026-08-21T06:45:00Z">
<item_id>570279</item_id>
<name>Adidas Regular Fit Mens T-shirt (Navy Blue)</name>
<variant>Small</variant>
<price currency="INR" mrp="999">789.00</price>
<availability in_stock="true" inventory="3" merchant="express"/>
<rating>3.98</rating>
</record>
Parquet is binary, so here is the schema we would write — typed, nullable where your data is, partitioned per run.
item_id BYTE_ARRAY (UTF8) required
name BYTE_ARRAY (UTF8) required
variant BYTE_ARRAY (UTF8) required
price DOUBLE optional -- null when unavailable
mrp DOUBLE required
inventory INT32 required
merchant BYTE_ARRAY (UTF8) required
rating DOUBLE optional
ingested_at INT64 (MILLIS) required
— partitioned by ingested_at, one file per run
WHY AURACRAWL
A specialist team for the targets others give up on
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Natural-language extraction
You describe what you want in a sentence and we find it. Nothing to rewrite when a site changes its layout — your request stays the same.
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Real anti-bot engineering
Not just switching proxies and hoping. We have spent years working out how Akamai, DataDome and PerimeterX actually decide to block you — and we apply that to your sites.
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Engineers, end to end
You talk to the engineers who build and look after your data feed. No ticket queue, no account manager passing messages along.
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An API is coming
We are building a self-serve API, and we will say plainly that it is not ready yet. For now, every project starts with an email and a scoped plan.
SELF-SERVE API
Coming soon.
We are building an API so smaller jobs will not need a call with us first. It is not ready yet, and we would rather say that here than after you sign up. For now, every project starts with an email.
Describe your data targets. We'll scope your engagement.
Tell us which sites, which data, and how often. An engineer replies with a scoped plan and a sample — before you commit to anything.