Deep experience in the sectors that run on web data.
Twenty-one industries, each with its own sources, questions and data models. From e-commerce catalogues to government tenders, if it is public and structured differently everywhere, we have likely built for it.
Ecommerce and retail
Are we priced correctly against the retailers our customers actually compare us with?
Quick commerce
What does a shopper in each neighbourhood see when they open a competitor's app right now?
Grocery and FMCG
How do our unit prices and promotions compare with competing brands at each grocer?
Fashion and apparel
When and how deeply are competitors marking down, and which sizes are selling out first?
Electronics and appliances
Which resellers are advertising our models below agreed price, and where?
Food delivery and restaurants
How do our menu prices and delivery fees compare with nearby competitors on each app?
Travel and hospitality
Is our rate competitive against our comp set for each stay date, and are channels in parity?
Real estate and proptech
What is the real asking price per square metre and supply level in each district this month?
Automotive and EV
What are comparable vehicles actually listed for, by trim, age and mileage, in each market?
Logistics and freight
What are competing carriers charging for comparable lanes and parcel profiles right now?
Education and edtech
How do our programme fees, formats and intakes compare with competing providers?
Transport and mobility
What does a given journey cost, and how is it served, at different times of day?
Finance and investment
Can this dataset be trusted in a backtest, and how exactly was it collected?
Insurance
How do our premiums and cover features compare with competitors for the same risk profile?
Healthcare and pharma
Where is our product priced, stocked and approved, and what is changing in the pipeline around it?
Energy and commodities
How are local fuel and energy prices moving against published benchmarks, and who is tendering for what?
Jobs and recruitment
What are employers offering for this role in this city, and how fast is demand changing?
Government and public data
Which new tenders and official notices are relevant to us, across every portal we should be watching?
Legal and compliance
What changed today in the rules, lists and registers that apply to us?
Manufacturing
How are our products priced and stocked across distributors, and what are our inputs costing?
Media and entertainment
What are competitors offering, at what price, and how much attention is it getting in each market?
Quick commerce, measured neighbourhood by neighbourhood.
In quick commerce the shelf is a dark store serving a few square kilometres, and prices, stock and delivery fees change by the hour. We capture app data by delivery zone so you can see what shoppers in each neighbourhood are offered, not a national average.
Explore quick commerceItem price by zone
Selling price per product for each delivery zone or pin location.
Availability by dark store
Whether each item can be ordered from the store serving that zone.
Delivery fee and minimum order
Fees, small-basket charges and free-delivery thresholds by zone and time.
Quoted delivery time
The ETA shown to the shopper at the moment of capture.
The data types each industry uses most.
A starting point. Every project combines data types to fit the question you need to answer.