Learn how Merit’s hybrid extraction stack unlocks valuable intelligence from static legal and construction portals - turning inaccessible data into actionable insights.
Across the legal and construction sectors, valuable information is hiding in plain sight - buried in public planning portals, court websites, procurement boards, and regulatory bulletins. These sites publish critical updates daily - from new tenders to legal rulings - yet most are not presented in a structured, machine friendly formats. There are no APIs, no structured exports, and often, no alerts. They're designed for human eyes, not automation.
The impact is significant. UK public sector portals post over 30,000 procurement notices a year, many directly relevant to construction firms. In the legal sector, unstructured data makes up nearly 80% of all information held bylaw firms, much of it buried in non-textual, scanned PDF filings, and fragmented systems. Tracking and extracting insights from these static sources is slow, error-prone, and hard to scale - creating a blind spot that costs organisations time, money, and competitive advantage.
Solving this blind spot requires more than a crawler or script - it takes a system that can adapt to unstructured formats, interpret scanned documents, extract data from deeply embedded visual layouts, and ensure outputs are clean, compliant, and usable. That’s what Merit Data and Technology delivers through its hybrid extraction stack: combining Python-based web-scraping, OCR for image-based inputs, and processing all extracted data through NLP-driven validation. Designed with modular pipelines, this approach scales across hundreds of static portals - turning unreadable data into structured intelligence.
While it may seem simple to “scrape a website,” static portals present a range of hidden technical barriers that make automation far more complex than it appears. Unlike modern data platforms that offer structured APIs or standardised data feeds, these sources are fragmented, inconsistent, and often actively resistant to automation.
No APIs, No Exports, No Structure: Many static portals do not offer machine-readable formats like JSON, XML, or even CSV. Data is often embedded in dynamic JavaScript or AJAX based HTML tables and paginated lists, or nested behind filter-based search interfaces - all designed for human interaction, not machine extraction.
Session Handling and Authentication Roadblocks: Some portals generate session-based tokens or dynamic page content that breaks when accessed outside a browser. Others require step-by-step form interactions or store in-browser JavaScript state - making them brittle to crawl and difficult to scale reliably.
Scanned and Semi-Structured Documents: In construction and legal workflows, vital information is often embedded within scanned PDFs, image-based notices, or poorly formatted text documents. Traditional scrapers can fetch the file, but they can’t interpret or extract structured fields from within it - like bid deadlines, clause references, or case numbers.
Anti-Bot Measures: CAPTCHA, Rate Limits, and More: Even when the page is readable, automation is often blocked by security features - CAPTCHA, rate limiting, IP throttling, or browser fingerprinting. Bypassing these while staying compliant with ethical data practices requires careful engineering and oversight.
Most traditional web scraping scripts are rule-based: they follow static selectors, pull data from predefined page elements, and fail when the layout or structure changes. These approaches struggle with:
To extract meaningful intelligence - not just raw text - from these portals, amore adaptive and modular approach is needed. That’s why off-the-shelf tools or one-off scrapers rarely succeed at scale.
Solving the static portal problem isn’t about building one clever scraper - it’s about engineering a system that can handle dozens of variations, document types, and edge cases without breaking.
That’s where Merit’s hybrid extraction stack comes in. Designed for flexibility, scale, and compliance, it combines multiple technologies to extract structured, usable intelligence from even the most difficult sources.
Python-Based Scraping for Semi-Structured Portals: Many public portals - like local government planning sites or procurement boards - have HTML-based listings, but lack APIs. Merit uses robust, Python-driven scraping frame works that can handle pagination, dynamic filters, and inconsistent markup. These scrapers are modular and can be adapted quickly as site structures evolve.
OCR for Scanned and Image-Based Documents: When information is embedded in scanned PDFs - common with tender documents, regulatory notices, or court filings - traditional scrapers hit a wall. Merit integrates optical character recognition(OCR) engines to convert images into text, enabling field-level extraction even from poor-quality scans.
NLP and Rule-Based Validation: Extracting raw text isn’t enough. Merit layers natural language processing (NLP) and business rule engines to identify and validate key fields - such as project values, deadlines, case references, or regulatory clauses - from noisy or unstructured content. This ensures outputs are contextually relevant and ready for downstream use.
Modular Pipelines Built for Compliance: Every client has different requirements - whether it's audit trails, data residency, or security parameters. Merit’s pipelines are designed to plug into these environments seamlessly. With built-in controls for exception handling, logging, and traceability, they ensure that automation doesn’t come at the cost of compliance or oversight.
A UK-based construction intelligence provider relied on outdated PERL-based scripts to extract infrastructure tender data from static and semi-structured public portals. These legacy systems were prone to failure, difficult to scale, and costly to maintain - especially as the volume and variety of tenders increased.
They sought a more scalable, reliable, and modular data harvesting solution—capable of automating extraction from tender portals and enabling downstream use cases like domain-specific knowledge graphs. Improving system uptime, reducing manual intervention, and future-proofing their data infrastructure were key goals.
Merit replaced the brittle legacy scripts with a Python-based, cloud-native scraping framework. The new system featured:
This upgrade not only improved day-to-day extraction performance but also laid the groundwork for building domain-specific knowledge graphs - capturing detailed metadata on permits, contractor entities, project timelines, and compliance parameters across thousands of tenders.
Legal teams face the challenge of monitoring hundreds of fragmented portals - from court websites to tribunal notifications to regulatory bulletins. Much of this data is published as scanned PDFs, inconsistent tables, or poorly structured web pages.
We enable:
This streamlines compliance tracking, reduces reliance on manual review, and allows firms to maintain an auditable, searchable legal intelligence repository - especially in jurisdictions without APIs.
Whether you're tracking infrastructure projects or court rulings, the data is already out there - but it’s locked in sources not designed for automation.
Merit’s portal extraction stack helps you unlock that data and turn it into usable intelligence, fast - with full compliance and context.
If you’d like to explore how we can help your team automate unstructured data from high-value portals, get in touch.
