Platform
One dataset, from shelf observation to forecast.
Price, availability and scarcity for the components that set the cost of computing. Built on continuous observation of what is actually listed and actually on shelves, not on surveys or list prices.
Modules
Four parts, one record underneath.
Each module is a view of the same observations. Take one, or all of them; the identifiers and the clock are the same throughout.
Price intelligence
AvailableEvery listing matched to a canonical product, priced across every retailer and marketplace seller we observe.
- Canonical products with structured specifications
- Per-unit normalization: $/TB, $/GB
- Full price history by product and seller
- Drops and changes as timestamped events
Availability and scarcity
Scarcity index in early accessShelf-level stock at physical stores across national retail chains, not just "available online".
- In-stock status by store, retailer and channel
- Restock and sell-out events as they happen
- Availability depth by product and segment
- Scarcity index by product, segment and metro
Forecasting
In developmentModels trained on the full observation history to project where prices and supply are heading.
- Price paths by product and segment
- Restock timing and sell-out risk
- Early warning on constrained parts
- Confidence ranges on every projection
Data delivery
By arrangementThe dataset in the shape your team works with, from a single feed to your warehouse.
- API access and event webhooks
- Scheduled bulk exports (CSV, Parquet)
- Delivery to your warehouse or cloud bucket
- Custom segments, retailers and markets
How it fits together
From a price on a shelf to a number in your system.
- 01
Observation
Prices and availability for 21,791 products, online and at 977 physical stores, each observation timestamped in UTC.
- 02
Record
One canonical product per part, one schema across segments and sellers, the full history kept.
- 03
Signal
Events and indicators derived from the record: drops, restocks, sell-outs, availability depth, scarcity.
- 04
Delivery
The slice you need, where you need it: API, events, files, or your warehouse.
Quality
From raw listing to trusted signal.
Retail listings are messy: the same part under ten names, accessories next to products, placeholder prices. The record is what is left when that is resolved.
- 01
Observe
Prices and availability are observed continuously, online and at store level. Every observation carries a UTC timestamp.
- 02
Resolve
Each listing is matched to one canonical product with structured specifications, so the same part is the same record everywhere.
- 03
Normalize
Prices are normalized per unit and per channel, and the same offer seen through different sellers is recorded once.
- 04
Validate
Implausible values are screened out, and a change is checked against history before it counts as a signal.
Put hardware market data to work.
Tell us which segments, products and markets matter to you. We'll show you what the data can answer.