Transforming raw data into strategic business advantages.
Data is useless without insight. We combine massive scale extraction with analytics to provide trends, patterns, and predictive signals for your industry.
Collected data answers "what is on the page". Intelligence answers "what changed, and does it matter". That means the same fields over time, from enough sources that one outlier does not move the number.
We handle collection and the layer above it: normalising entities across sources, tracking a metric through time, and flagging movement worth looking at rather than shipping a spreadsheet nobody opens.
Identifying emerging market patterns before your competitors.
Finding where you can outperform in the market.
Analyzing how users interact with products across the web.
Forecasting market movements based on web activity.
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 |
|---|---|---|
entity_id | string | The thing being tracked, resolved across sources. |
entity_name | string | Canonical name after matching. |
metric | string | What is measured - price, headcount, listing count. |
value | decimal | This period’s figure. |
previous_value | decimal | Last period, so change needs no join. |
change_pct | float | Computed, so every consumer agrees on it. |
period | string | The window the figure covers. |
source_count | integer | How many sources support it. One is a rumour. |
confidence | float | Lower when sources disagree. |
CSV or a database table — whichever your BI tool reads.
Values here are illustrative. On a real sample these are your products, from your target sites — see below.
{"entity_id": "ent_3391","entity_name": "Example Competitor Ltd","metric": "active_listings","value": 12840,"previous_value": 11960,"change_pct": 7.36,"period": "2026-W41","source_count": 4,"confidence": 0.91,}Competitor movement tracked on the same basis each week.
Signals across portfolios and pipelines, from public sources.
Supply and rate movement across portals, normalised into one view.
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.
The collection is the smaller problem. The work is resolving that "Example Ltd", "Example Limited" and "example.com" are one entity, keeping the same measurement basis for a year so a trend means something, and not shipping a 7% move that is one source changing how it counts.
BI tools visualise what you give them. They do not tell you that this week’s figure came from three sources and last week’s from four, which is the difference between a trend and an artefact.
Entities resolved across sources, a metric tracked on a constant basis, and a confidence figure that drops when sources disagree - so a number you would act on is distinguishable from one you would check first.
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.
A step change in the series that looks like the market moving and is a definition changing.
source_count and confidence ship with every figure. A move supported by one source out of four is visible as such before anyone acts on it.
You undercount a competitor by two thirds and conclude they are smaller than they are.
Entity resolution runs across sources with variants retained, so a merge can be inspected and reversed.
The metric drops, nobody knows whether the market moved or the collection did.
A missing source is a gap, not a zero. The series shows the gap rather than averaging over it.
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.
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.
You do. The datasets we produce for you are yours. We do not resell them and we do not reuse them for other clients.
Yes, routinely, and we are happy to work under your paperwork rather than ours. We can also sign a data processing agreement where your engagement involves us handling data on your behalf.
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.
Both. We have built one-off datasets for solo founders and continuous pipelines running millions of pages a day for larger teams. The approach is the same, the scale is not.
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.