Education & EdTech Data Scraping Services

Yes, and this is essential rather than optional in this category. A large share of institutions publish fee schedules only as PDFs, frequently as scanned tables. We run a document-processing pipeline with OCR for scanned material, then normalise the extracted values into our schema.

Every fee carries an extraction-method flag, so you know which values came from structured markup and which came from OCR. OCR-derived values are held to a higher review threshold, and low-confidence extractions go to human verification before delivery.

We resolve the pricing unit explicitly and store it in a dedicated tuition_unit field with values like per_year, per_term, per_credit or full_programme. Where the institution states it, we use their statement; where it must be inferred from duration and credit structure, we infer it and flag the inference.

Where the unit genuinely cannot be determined with confidence, we mark it unresolved rather than guessing. A wrong unit is worse than a missing one, because it produces comparisons that look valid and aren't — a per-year fee compared against a full-programme fee understates cost by a factor of two or three.

Yes. We extract in-language and optionally supply translated programme titles and descriptions alongside the originals. Coverage is strongest in Western European languages, and we handle Hindi, Arabic and East Asian scripts with reasonable reliability.

Extraction quality is generally lower on non-English sites, mainly because page structures vary more and fee presentation conventions differ. We give you honest per-region coverage expectations during scoping rather than a uniform promise followed by a file full of nulls.

On different cycles depending on the field. Tuition and fees change annually, usually announced several months before the academic year. Programme catalogues change termly as courses are added and retired. Entry requirements change annually. EdTech platform pricing changes weekly or faster, with frequent promotional discounting.

We refresh institutional data termly and EdTech pricing weekly, which matches how these actually move. Refreshing university fees weekly would generate identical records at real cost; refreshing EdTech pricing termly would miss most of the pricing behaviour.

Only what institutions and platforms publish publicly, which varies enormously. Some jurisdictions mandate publication of completion rates, employment outcomes and salary data; others publish nothing. EdTech platforms often display enrolment counts and ratings openly.

We extract what is published and clearly mark what is absent. We do not model or estimate enrolment figures — where a number does not exist publicly, we supply no number. Vendors who fill those gaps with estimates rarely disclose the method, and the resulting figures find their way into market sizing where they cause real damage.

Yes, and clients do exactly that — but with a caveat worth stating. Fee accuracy carries reputational stakes in student-facing products: a quoted figure that turns out to be wrong, or the wrong student category, damages trust immediately and is hard to recover.

For this reason we recommend surfacing our fee_source_ref field in your product so users can click through to the authoritative institutional page. That way your product provides the comparison and the institution provides the confirmation, which is the right division of responsibility.

We extract scholarship and bursary listings where institutions publish them, including eligibility criteria, award value and application deadlines where stated. Coverage is inconsistent because publication practice is inconsistent — some institutions maintain structured scholarship databases and others mention funding in prose on a single page.

Scholarship data decays quickly since deadlines pass and awards are withdrawn. We capture the stated deadline and our capture date so your product can suppress expired listings rather than showing a student an opportunity that closed months ago.

We quote every education data engagement individually, because a real number depends on scope: source count, record volume, refresh frequency and delivery method. Anyone quoting you a price before understanding those four things is guessing.

Institution count and how much of the fee data sits in PDFs are the main drivers, since document extraction is more labour-intensive than page collection.

The process is short: one scoping call, a free pilot on your own sources within 48 hours, then a fixed monthly quote. No per-request metering, no overage billing, and field or source additions are handled inside the retainer rather than re-quoted. Request a quote.

Yes, and it is one of the strongest use cases here because no comparable public dataset exists. For a defined segment — level, field, geography, delivery mode — we assemble the complete provision universe with fees, institution profiles and mode, then deduplicate across institutional sites, aggregators and recruitment portals.

We will also tell you honestly where the universe is incomplete. In some markets institutional publication is patchy enough that a genuinely complete list is not achievable, and knowing that before you build a model on it is more valuable than a confident number that isn't.