Pricing & Product Data Scraping Services

Collecting publicly available pricing information is generally lawful in the US, UK and EU, and price transparency is well established in competition law. The risks sit elsewhere: bypassing authentication, ignoring rate limits, breaching contractual terms, or collecting personal data without a lawful basis.

Actowiz operates only on publicly accessible pages, respects robots directives and crawl-rate limits, and never circumvents logins or paywalls. Every engagement includes a written collection methodology and source list that your legal team can review before signature. We are a data engineering provider, not a law firm — final assessment for your jurisdiction and use case should come from your own counsel, and we support that review with full documentation.

Matching runs in three layers. First, identifier matching on GTIN, EAN, UPC, ASIN or MPN wherever retailers expose them — this handles the majority of branded goods cleanly. Second, attribute matching on brand, model, variant axes and specification values for listings without shared identifiers. Third, manual review by our QA analysts for pairs the model scores as low confidence.

Every delivered row carries a match_confidence score between 0 and 1, so your repricing rules can require, for example, 0.9 or above before acting. You can also supply your own mapping file and we will extract against it directly.

It depends on category velocity, and paying for hourly across a whole catalogue is usually waste. A practical tiering that works for most clients:

• Hourly: consumer electronics, marketplace Buy Box competition, flash-sale categories, your top 5% revenue SKUs.
• Daily: grocery, fashion, home goods, the bulk of a typical catalogue.
• Weekly: long-tail SKUs, assortment and attribute refresh, category structure.

We set frequency per source and per SKU tier rather than globally, so you spend refresh budget where price actually moves.

Yes, wherever the retailer exposes it. Grocery and home-improvement chains in particular vary price and stock by location, and a national average hides the variation that matters. We extract by store ID, postcode or delivery zone as the site permits, and deliver location as a first-class dimension in the schema.

Tell us your target postcode or store list during scoping; geographic granularity affects run volume, so it is priced in from the start rather than bolted on.

Our monitoring flags it, usually within one run, through fill-rate anomaly detection rather than a customer complaint. A named engineer rebuilds the extractor, most breaks are resolved within 24 hours, and complex redesigns are communicated with an ETA. Your feed is backfilled for the gap where the source permits.

This is the core economic argument for a managed feed. Site changes are inevitable; the question is whether they consume your engineering sprint or ours.

On three variables: number of records per run, refresh frequency, and source complexity. A daily feed of 50,000 SKU-retailer rows from mainstream retailers sits at the entry end. Hourly monitoring of millions of rows including marketplace Buy Box rotation and store-level stock sits at the enterprise end.

Engagements are quoted as a fixed monthly figure after scoping rather than a variable per-request charge, so your finance team can budget it. The pilot sample is free and carries no obligation.

Partially, and we are precise about the limits. Where public archives, cached pages or our own prior collection cover your targets, we can backfill history — in some categories going back several years. Where they don't, no honest provider can invent it.

During scoping we tell you exactly which sources and date ranges are backfillable and which start from day one. If deep history is essential to your model, that assessment should happen before you commit.

Yes. Direct warehouse loading is the most common enterprise pattern — typically Parquet partitioned by date and retailer, landed in S3, GCS or Azure Blob and loaded via your existing ingestion, or written to the warehouse directly where you grant access. Snowflake, BigQuery, Databricks and Redshift are all in production with clients today.

Webhooks fire on batch completion so downstream jobs trigger on real availability rather than a cron guess.

We quote every pricing and product data engagement individually, because a real number depends on scope: source count, record volume, refresh frequency and delivery method. Anyone quoting you a price before understanding those four things is guessing.

Most repricing programmes sit in the mid range: a defined competitor set, daily or hourly refresh, delivered to a warehouse. Scope drives the number far more than record count does.

The process is short: one scoping call, a free pilot on your own sources within 48 hours, then a fixed monthly quote. No per-request metering, no overage billing, and field or source additions are handled inside the retainer rather than re-quoted. Request a quote.