Data. Insights. Impact.
Extraction from sites that fight back, pipelines that deliver clean records into the tools you already use, and agents that tell you what changed and why it matters.
We work with startups, enterprise data teams, and GTM engineers — people who need the data itself, not another dashboard to check.
What we build
Search engines, job boards, marketplaces, social platforms. Sources with rate limits, bot detection, and markup that changes without notice. We build the collectors, and we keep them working when the target shifts underneath them.
In production today against Google, LinkedIn, Glassdoor, Indeed, TikTok, YouTube, and Similarweb.
Raw pages become typed records — deduplicated, validated against a schema, enriched with contact and firmographic detail, and delivered where the work actually happens rather than into another export folder.
Every actor we publish ships a documented dataset schema. There is a worked example below.
Agents that research a subject on demand, decide what is genuinely new since the last run, and summarise why it matters — with every finding linked back to its source.
The asset is the history. "What changed" only exists because prior runs were kept and compared.
Who we work with
Public work
We publish and demo scrapers on the Apify Store — 25 of them, listed in full below. They are a fraction of what we build, but they are the part you can inspect, run, and judge without a sales call.
Each title links to its Apify listing, where the input schema, pricing, and run history are public.
What a deliverable looks like
Taking Google Maps Lead Generation as the worked example — a small JSON input, one typed record per result, and a field reference that tells you exactly what you are getting. Everything below is the actor’s documented schema, unedited. Custom work is scoped and documented the same way.
{
"businessType": "Dentists",
"location": "Manhattan, NY",
"maxResults": 5
}{
"name": "Example Dental Care",
"category": "Dentist",
"rating": 4.8,
"totalReviews": 320,
"address": "123 Example Ave, New York, NY 10001",
"phone": "(212) 555-0123",
"website": "https://example-dental.com",
"businessStatus": "Open",
"googleMapsUrl": "https://www.google.com/maps/place/...",
"emails": ["info@example-dental.com"],
"socialLinks": {
"facebook": ["https://facebook.com/exampledental"],
"instagram": ["https://instagram.com/exampledental"]
}
}| Field | Type | Description |
|---|---|---|
| name | string | Business name. |
| address | string | Full business address. |
| phone | string | Phone number, from Google Maps or the website. |
| website | string | Business website URL. |
| emails | array | Email addresses found on the website. |
| socialLinks | object | Social profile links grouped by platform. |
| rating | number | Google Maps rating. |
| totalReviews | number | Total number of reviews. |
| category | string | Business category. |
| businessStatus | string | Open or closed status. |
| hours | object | Weekly opening hours. |
| googleMapsUrl | string | Google Maps listing URL. |
How it goes
You describe the sources and the records you need out the other end. We come back with what is feasible, what will be fragile, and what we would not attempt — before anyone signs anything.
You get the schema before we write the collector, so there is no argument later about what a field means. Then the collectors, the pipeline, and delivery into wherever the data has to land.
Sources change their markup and their defences, usually without notice. Keeping the collectors working is the ongoing engagement, and it is the part most teams underestimate when they build this in-house.
What we turn down
Said up front, not discovered mid-engagement.
Start a conversation
The useful first message describes the decision the data has to support. We will tell you what is collectable, what it will take to keep it collectable, and whether you need us at all — some of this you can already run yourself on the Apify Store.
Worth including