Gemini-powered extraction for complex, adaptive web targets.
Traditional scrapers break when a site changes. Our AI scrapers learn the site's structure and adapt, using LLMs to understand and categorize data with human-like accuracy.
Training and grounding a model needs volume, but it needs provenance more. Where each document came from, when it was fetched, whether it is a duplicate of something already in the set, and whether it was publicly published in the first place.
We collect at scale and deliver with that lineage attached, deduplicated, and in a shape that loads into a pipeline rather than needing a week of cleaning first.
Scrapers that automatically adapt to minor HTML/CSS changes.
Using AI to understand text context and categorize it correctly.
Extracting data from images and non-selectable UI elements.
Quickly deploy scrapers for new sites without manual mapping.
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 |
|---|---|---|
doc_id | string | Stable identifier for the document. |
url | string | Source, always retained. |
title | string | As published. |
text | string | Main content, boilerplate stripped. |
token_estimate | integer | For budgeting a training run before you load it. |
language | string | Detected, ISO 639-1. |
dedup_hash | string | Near-duplicate detection across the whole set. |
robots_allowed | boolean | What the source’s crawl rules said at fetch time. |
fetched_at | timestamp | UTC, ISO 8601. |
JSONL — one document per line, the format most training pipelines expect.
Values here are illustrative. On a real sample these are your products, from your target sites — see below.
{"doc_id": "doc_7c21a9","url": "https://example.org/guides/intro","title": "An introduction to the subject","token_estimate": 1840,"language": "en","dedup_hash": "sha256:9f2c...","robots_allowed": true,"fetched_at": "2026-10-15T07:41:03Z",}Domain corpora with provenance, so a dataset can be audited later.
Fresh grounding data on a schedule, deduplicated against what you already hold.
Reproducible collections where the fetch date is part of the result.
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.
Volume is the easy half. The hard half is provenance: knowing where each document came from, when, whether it is a near-duplicate of something already in the set, and whether the source permitted collection - all of which you need before anyone asks how the model was trained.
Generic crawlers give you pages. A training set needs boilerplate stripped, near-duplicates collapsed across the whole corpus rather than per-source, and a record of the crawl rules at fetch time, which a page-level tool does not keep.
A corpus delivered with its lineage attached - source, fetch time, crawl permission, dedup hash - in JSONL, so it loads into a pipeline rather than needing a week of cleaning 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.
It is forty documents by volume and one by information. Train on that and you have weighted one opinion forty times.
Near-duplicate detection runs across the whole corpus, not within a source. Duplicates are collapsed with the variants recorded.
Months later nobody can say whether a document was permitted at the time it was fetched.
robots_allowed is captured at fetch time and stored on the record. The answer does not depend on re-checking today.
Every document carries the same hundred words of chrome, which a model learns as signal.
Boilerplate is stripped per template rather than by a global heuristic, and token counts make an inflated document obvious.
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.
It happens regularly, and it is part of what you are paying for. We monitor our scrapers, so in most cases we spot a break before you do. Fixing selectors after a layout change is included in retainer and dedicated developer arrangements. On fixed-price projects we agree a support window upfront.
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.
Yes. Send us a target URL and the fields you want, and we will return a free sample so you can check the structure and quality before any money changes hands.
We build collection to be well-behaved: sensible request rates, proper identification and respect for published crawl rules. If a site does not want to be collected from, that is a signal we take seriously rather than a problem to engineer around. Where a site is simply slow or rate-limited, we schedule around it.
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.
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.