Unlocking Value from the Forgotten 80%: Why Intelligent Data Extraction Matters Now

The Unstructured Data Bottleneck

In industries like automotive, legal, and energy, a common bottleneck persists: critical business data is trapped in PDFs, scans, static portals, and legacy content management systems. This unstructured data - covering everything from pricing plans and legal clauses to field reports - is essential for operations, compliance, and analytics, yet largely invisible to enterprise systems.

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Studies estimate that over 80% of enterprise data is unstructured. Manual methods of extraction are not only time-consuming and error-prone, but they also pose compliance risks in the face of tightening regulations like GDPR, the EU AI Act, and sector-specific audit requirements.

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Moreover, most tools still struggle with the variability and context of unstructured data - particularly in regulated industries, where archived documents like old contracts, compliance filings, and outdated brochures are still critical for day-to-day operations and audits. These archives are often scanned, inconsistently classified/ tagged/ summarised, and locked in static systems.

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This is where intelligent document extraction comes in.

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At Merit Data & Technology, we have developed a scalable framework that combines GenAI, OCR, and NLP to extract not just content, but context - from images, brochures, bulletins, scanned contracts, and static portals. This is where most platforms fall short, and where Merit stands apart.

Why Unstructured Data Is So Challenging Today

Traditional data systems are optimised for structured databases - neatly organised rows and columns. Unstructured data, by contrast, is messy. It includes images, handwritten notes, untagged PDFs, and scanned documents - each with unique formats, layouts, and hidden metadata.

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While OCR and NLP technologies can help extract visible text, they often miss the bigger picture - such as the layout-driven meaning, clause-level context, or implied metadata. This is where GenAI combined with vision language models adds critical value:

• Identifying document types and variants even when formatting is inconsistent
• Detecting implied relationships or regulatory conditions not explicitly mentioned
• Handling poor quality scans or multi-language content without rule-heavy pre-processing
• Classifying sensitive content dynamically (e.g., pricing, legal references, compliance tags)

Regulations like GDPR now require organisations to know what data they store, where it resides, and how it is used. Without intelligent extraction, unstructured archives become both a missed opportunity and a compliance risk.

Where existing tools fall short

Many players have entered the intelligent data extraction space — but their capabilities are often limited to:

• Reading simple forms or ID cards(via big tech APIs)
• Performing text extraction with open-source OCR engines Summarising documents with GenAI but lacking structure and explainability

These tools struggle with:

• Complex document layouts (e.g., product brochures vs legal contracts)
• Extracting structured data from tables embedded in PDFs or HTML exports
• Ensuring compliance tagging or audit readiness
• Connecting extracted data to downstream analytics or planning workflows
• Balancing accuracy, scale, and cost - high-quality extraction at scale often requires significant compute, which can be difficult to justify or operationalise without an optimised architecture

Merit’s Approach to Intelligent Extraction at Scale

Merit’s extraction framework is purpose-built for complex, compliance-heavy environments where data formats and business rules vary significantly.

What Sets Merit Apart

Merit’s approach blends foundational techniques with advanced GenAI capabilities — enabling not just extraction, but interpretation, validation, and contextualisation of data at scale.

• ‍GenAI for Contextual Understanding
  Uses large language models fine-tuned on domain-specific data to extract not just content, but meaning - including clause types, pricing logic, and implied conditions. GenAI also powers summarisation, cross-referencing, and classification across large document repositories, helping users surface what matters most.
• Iterative Accuracy and Edge-Case Handling
  Merit continuously improves output quality through prompt tuning, human-in-the-loop feedback, and dynamic rule application - enabling higher accuracy, even across unusual formats or ambiguous language.‍
• Advanced OCR + Vision-Based Preprocessing
  Uses enhanced OCR (Tesseract + OpenCV)for image-based inputs, with techniques like de-skewing, noise removal, and binarisation to improve scan accuracy - especially for archived and poor-quality documents.‍
• Semantic Parsing for PDFs and HTML
  Combines structural AI models with rule-based logic to parse tables, detect semantic blocks (headers, footers, clauses), and retain document context in output data.‍
• Legacy and Static System Compatibility
  Extracts from outdated CMS systems, static portals, and non-standard exports using custom connectors and ethical scraping techniques.
• Search, Simplification, and Recommendation Layers
  Enables contextual search across large archives, simplifies legal or technical language on demand, and recommends related clauses or data points - transforming how users interact with extracted data.‍
• Compliance-Ready Tagging
  NLP and GenAI models classify fields for GDPR, PII, or regulatory relevance and output audit-ready metadata to meet enterprise compliance standards.

Where It’s Working: Real-World Use Cases

• ‍Automotive: Extracting lease documentation, feature specs, and pricing logic from marketing brochures and OEM PDFs. Outcome: reduced manual QA cycles, enabling faster pricing intelligence.‍
• Legal: Digitising contract repositories and legal filings. Merit's clause-level delta detection and entity tagging reduce review cycles and ensure clause-level traceability.

Platform Highlights

• ‍Modular Architecture: Easily add new file types or compliance tagging rules‍
• Flexible Output Formats: Structured output via SQL, JSON, CSV, or secure SFTP‍
• Deployment Options: Cloud-native or on-premise for data-sensitive environments‍
• Integration-Ready: Connects to BI dashboards, analytics platforms, or GenAI pipelines

The Takeaway: From Archive to Actionable Asset

Unstructured data doesn’t need to remain an operational blind spot. With the right framework, it becomes a valuable, compliant, and analysable asset.

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Merit’s intelligent data extraction engine delivers structured insight from complex, unstructured formats - enabling automation, compliance, and decision intelligence across the enterprise.

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If your business is sitting on thousands of PDFs, scanned records, legal docs, or legacy portals, now is the time to explore intelligent extraction.

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Let’s talk about how a pilot or custom framework can help you unlock that forgotten 80%.