Dominating the digital shelf with real-time competitive pricing.
Monitor your competition's pricing across thousands of sites. Our price scrapers provide the granular data needed for dynamic pricing strategies and margin optimization.
Price monitoring is easy to start and hard to keep running. The prices themselves are the simple part; the difficulty is that a competitor restructures a page, your scraper keeps running, and the figures in your dashboard quietly stop being true.
We collect prices, stock and promotions across the sites you name, check the numbers before they reach you, and fix collection when a source changes. You get a comparable series rather than a snapshot.
Track price changes as they happen, minute by minute.
Know when competitors are out of stock or running low.
Identify flash sales, coupons, and hidden discounts.
Enforce Minimum Advertised Price compliance globally.
Enterprise Standard
99.9% Extraction Reliability
The schema is yours to change — add fields, drop fields, rename them to match what your systems already read. This is the default.
| Field | Type | What it is |
|---|---|---|
product_id | string | Your identifier, so rows join to your catalogue. |
matched_sku | string | The competitor’s own code, where published. |
title | string | As listed on their site, not as listed on yours. |
price | decimal | The price actually shown to a shopper. |
currency | string | ISO 4217. Not assumed from the domain. |
list_price | decimal | Pre-discount, where shown. Null when there is none. |
in_stock | boolean | Stock state at capture time. |
seller | string | Matters on marketplaces with several sellers per listing. |
promotion | string | Flash sale, coupon or multibuy text, where present. |
rating | float | Optional, useful when price alone does not explain a gap. |
product_url | string | So anyone can verify a row by hand. |
captured_at | timestamp | UTC, ISO 8601. The series is only as good as this. |
CSV — the format most pricing teams load directly.
Values here are illustrative. On a real sample these are your products, from your target sites — see below.
{"product_id": "SKU-10044","matched_sku": "B09TKD2YRQ","title": "Kettle 1.7L Brushed Steel","price": 34.99,"currency": "GBP","list_price": 44.99,"in_stock": true,"seller": "Example Retail Ltd","promotion": "22% off until 31 Oct","rating": 4.6,"product_url": "https://example-store.com/p/kettle-1-7l","captured_at": "2026-10-15T06:00:00Z",}Repricing against a named competitor set, with stock state so you do not match an out-of-stock price.
MAP monitoring across resellers, with the listing URL as evidence.
Rate and availability tracking where the price moves several times a day.
A defined one-off project with a known scope and a delivery date.
Continuous collection, weekly or monthly. Selector fixes after a site changes are included.
A scraping engineer working inside your team, for roadmaps rather than deliverables.
All three are reasonable. Which one fits depends on how much of this you want to own.
Collecting one competitor’s prices is a weekend. Forty is a different job: a matcher that knows your SKU is their listing, stock state so you do not undercut an out-of-stock price, and maintenance every time one of the forty restructures a product page.
Repricing tools assume the data is already correct. They are good at deciding what to do with a price and not built to argue with the source about whether the price is real - the variant, the seller, the promotion that only applies over three units.
We own the collection and the matching, check figures before they reach you, and fix a source when it changes. You get a series that is comparable week to week rather than a snapshot that is right today.
Collection breaks. The question worth asking a supplier is not whether it breaks but whether you find out from them or from your own data.
The single most expensive failure here. It does not error - it returns the old price, or the strikethrough price, and you reprice against a number that is not real.
Prices are range-checked against the recent series for that product. A move that large fails validation and holds the row rather than shipping it.
You match against a marketplace reseller rather than the brand and conclude the market moved when it did not.
Seller is a field, not an assumption. You choose which seller the comparison is against.
The headline price looks like a cut that is not available to the shopper you are competing for.
Promotion text is captured alongside the price so a conditional offer is visible rather than flattened into a number.
The same six steps on every engagement, whether it is one site or ten thousand.
Hand-shake connection has been established between potential customer and Business Team.
Potential customer exchange information and Business Team consult them with expert advice.
Compliance & Technical specialist examines the identified business need of potential buyer.
Potential Buyer confirms the compliance details to allow Engineering team to start Execution.
Delivery team dispose off the Exemplary Service to ensure the customer delight.
Build a strong long term relationship with a client by exceeding their expectations at every stage.
It happens regularly, and it is part of what you are paying for. We monitor our scrapers, so in most cases we spot a break before you do. Fixing selectors after a layout change is included in retainer and dedicated developer arrangements. On fixed-price projects we agree a support window upfront.
We build collection to be well-behaved: sensible request rates, proper identification and respect for published crawl rules. If a site does not want to be collected from, that is a signal we take seriously rather than a problem to engineer around. Where a site is simply slow or rate-limited, we schedule around it.
Most projects start within a few days of the requirements being agreed. A single straightforward site is usually running within a week. Large multi-site pipelines take longer, and we will give you a schedule with the quote rather than a vague estimate.
JSON, CSV, Excel, XML, or straight into a database. We can drop files to S3, SFTP or Google Drive, push to a webhook, or expose the data through a REST API. Tell us what your systems already read and we will match it.
Three ways: a fixed price for a defined project, a weekly or monthly retainer for ongoing collection, or a dedicated scraping developer working as part of your team. Which one fits depends on how often the data changes and how much of the work is ongoing rather than one-off.
Send a target URL and the fields you want. We return 100 real extracted rows within 24 hours so you can check the structure and the quality yourself, before any money changes hands.