Unlocking structured intelligence from unstructured digital noise.
We go beyond simple crawling. Our extraction engines use deep DOM analysis to find the signal in the noise, delivering high-precision datasets from even the most chaotic websites.
Not every source is a tidy product page. Invoices as PDFs, tables buried in HTML, XML feeds with inconsistent schemas, pages that assemble themselves in the browser. Extraction is the work of getting one clean structure out of several messy ones.
You tell us the fields you need. We decide how to get them from each source and normalise everything into a single schema, so a record from a PDF looks the same as one from a web page.
Intelligent identification of product names, specs, and prices.
Extract data from HTML, PDF, XML, and complex JavaScript objects.
Converting mixed formats into a single, unified structure.
Automatically tagging extracted data with SEO and category context.
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 |
|---|---|---|
record_id | string | Stable across runs, so updates can be matched. |
source_url | string | Where it came from, always included. |
source_type | enum | html | pdf | xml | json | docx. |
title | string | The record’s own heading, where one exists. |
attributes | object | Your fields, normalised across every source type. |
tables | array | Extracted tables, kept as rows rather than flattened text. |
confidence | float | Low values flag records worth a human look. |
extracted_at | timestamp | UTC, ISO 8601. |
JSON — flattened to CSV or Excel on request.
Values here are illustrative. On a real sample these are your products, from your target sites — see below.
{"record_id": "rec_88213","source_url": "https://example.org/reports/2026/q3.pdf","source_type": "pdf","title": "Quarterly operating report, Q3 2026","confidence": 0.97,"extracted_at": "2026-10-15T08:02:19Z",}Clauses and dates out of filings and contracts held as PDFs.
Rates and schedules from carrier documents that arrive in five different shapes.
Structured fields out of published literature and public registries.
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.
One source is an afternoon. Five formats is a project: a PDF parser that copes with scanned pages, an HTML path that survives a redesign, an XML reader for a schema that varies by supplier, and a normalisation layer so all three produce the same record.
Extraction tools handle the format they were built for. The difficulty is rarely one format - it is making a record from a PDF indistinguishable from one from a web page, which no single tool does for you.
You define the fields once. We work out how to get them from each source and normalise everything into that one schema, with a confidence score so the records worth a human look are visible.
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.
Text extraction returns nothing, and a pipeline that assumed text silently produces empty records.
Source type is detected per document rather than assumed. A scan routes to a different path and is flagged if the confidence drops.
Half the rows arrive, the other half are read as a new table with the wrong headers - the worst outcome, because it looks like data.
Tables are reconstructed across page boundaries and checked against the header, not read page by page.
A null that means "not published" is indistinguishable from one that means "we failed to read it".
Those are different values in the output. Absent and unreadable are not the same record.
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.
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.
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.
You do. The datasets we produce for you are yours. We do not resell them and we do not reuse them for other clients.
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.