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About us
Pricing and promotions

Clean market signals for your pricing engine.

Automated pricing needs inputs it can trust without a human checking each run. We deliver competitor prices, availability and demand proxies as a stable, versioned feed built for repricing engines and pricing models.

How it works

Four steps from sources to signal.

STEP 01

Define the feed contract

We agree a fixed schema with your pricing or data science team: fields, types, keys, null handling and freshness targets. Changes are versioned and announced in advance, so your engine does not receive unexpected columns.

STEP 02

Collect signals at engine cadence

Collectors run on the schedule your repricer needs, typically every 15–60 minutes for priority SKUs and less often for the long tail.

STEP 03

Apply eligibility rules

Each offer is tagged for condition, bundle status, seller type and stock, so your rules can filter to comparable offers without extra logic.

STEP 04

Stream with freshness monitoring

Data is pushed by API, webhook or warehouse sync with a capture timestamp and a freshness flag. Missed runs raise an alert on our side before your engine notices.

Data points

What we capture.

Every field is validated, normalised and documented in a data dictionary you can share with your analysts.

Lowest comparable offer

The cheapest new, in-stock, non-bundled offer per SKU and market.

Offer count

The number of active competing offers, as a proxy for price pressure.

Competitor stock flag

Available, low stock or unavailable, per competitor and SKU.

Condition and bundle tags

New, refurbished or used, and whether the listing is a bundle.

Rank or position signal

Search or category rank where available, as a demand proxy.

Freshness indicator

Minutes since capture, so stale inputs can be down-weighted.

Deliverables

What you receive.

  • Versioned JSON or Parquet feed matching your engine's schema
  • Webhook push when a priority competitor changes price
  • Freshness and completeness report per run
  • Eligibility-tagged offer table for rule-based repricing
  • Historical feed archive for model training and back-testing
Built for
Pricing science and analytics
Revenue management
Data engineering
Marketplace operations
Sample output

An illustrative extract.

Repricing feed extract, priority SKUs, 3 March 2026
SKUMarketLowest comparableCurrencyOffersCompetitor stockAge (min)
HB-2201 Hair dryer 2200WUAE199.00AED7In stock12
HB-2201 Hair dryer 2200WKuwait16.500KWD3Low stock18
KT-900 Stand mixer 5LAustralia329.00AUD5In stock25
KT-900 Stand mixer 5LCanada279.99CAD4Unavailable31
VC-45 Cordless vacuumMexico5,499.00MXN9In stock9

Illustrative rows. Sources, markets and fields are agreed with you during scoping.

FAQ

Questions about dynamic pricing inputs

Can’t find your answer? Ask an engineer

Can the feed match our repricer's input format?

Yes. We build to your schema rather than asking you to adapt. Field names, types and file layout are agreed up front and versioned, and we give notice before any change.

What happens if a source fails during a run?

Each record carries a capture time and freshness flag, so your engine can ignore stale values. We monitor every run and alert on missed or partial collections, usually before the gap affects your prices.

Do you provide pricing recommendations?

No. We supply the market inputs; your engine or team applies the pricing logic. This keeps commercial decisions with you and lets us focus on data quality and reliability.

Start with proof

See your own data before you commit.

Name the sources and fields you need. Within 24–48 hours you receive a real sample from your target sites, in your format, free of charge.

Request a free sample Talk to a data engineer Sample in 24–48 hours · NDA on request · Any format, any schedule