Deep relationship discovery across the massive expanse of the web.
We mine deep relationships between entities, uncovering hidden links in company structures, social networks, and research data across the entire internet.
Mining starts without a list. Rather than "collect these 400 URLs", the question is "find every company in this sector and tell me what connects them". Discovery first, structure second.
We crawl broadly, resolve the same entity appearing under different names across sources, and deliver a linked set rather than a pile of pages - with every attribute carrying the source it came from.
Connecting disparate data points into a unified knowledge graph.
Discovery-based mining that finds new targets automatically.
Mapping relationships and influence across social platforms.
Systematic mining of academic and research repositories.
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 | Resolved across every source it appeared in. |
name | string | Canonical, with variants kept. |
entity_type | enum | company | person | place | product. |
attributes | object | Whatever was found, each with its own source. |
linked_entities | array | The relationships, which are usually the point. |
sources | array | Every URL the record draws on. |
first_seen | timestamp | When it entered the set. |
last_seen | timestamp | Last confirmed. Stale records are visible. |
JSON — or a graph export where the relationships matter most.
Values here are illustrative. On a real sample these are your products, from your target sites — see below.
{"entity_id": "org_55120","name": "Example Plumbing Ltd","entity_type": "company","first_seen": "2026-03-02T11:00:00Z","last_seen": "2026-10-14T23:50:00Z",}Building a sector list from scratch, with the source behind every field.
Mapping suppliers and their relationships across public sources.
Sizing a market where no published list of participants exists.
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.
Starting from a list is collection. Starting from a question - "every supplier in this sector" - is discovery, which means crawling broadly, deciding what counts, and resolving duplicates with no reference list to check against. Most in-house attempts stall at deduplication.
Tools take URLs. Mining has no URLs at the start; producing them is the work. A tool can collect what you found, but it cannot tell you what you have not found yet.
We run the discovery and the entity resolution, and deliver a linked set where every attribute carries the source it came from - so a disputed field can be checked rather than argued about.
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.
Your sector list has three entries for one company, and every count built on it is wrong.
Entities are resolved across sources with every variant and source kept, so a merge can be audited and undone.
The set grows, looks impressive, and stops being the thing you asked for.
Inclusion criteria are agreed before the crawl and applied as a filter, with borderline entities surfaced rather than silently kept or dropped.
A set that only ever grows slowly fills with companies that no longer trade.
last_seen is on every record. A set that is not refreshed shows its own age instead of looking current.
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.
Collecting information that is published publicly is generally lawful, but it depends on the site, the data and where you operate. We only collect publicly available data, we don't gather personal or identity information, and we won't take on work that needs us to get around a site's access controls or break its terms of use. If a request falls the wrong side of that line, we'll tell you before we start rather than after.
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.
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.
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.