-
Client
CAIRN Energy
-
Service
Enterprise Knowledge Client
-
Industry
AI & Document Intelligence
-
Company Size
1,000+
The client
A knowledge-intensive enterprise managing a vast library of complex documents spanning contracts, technical reports, compliance records, and multi-format archives had reached the operational limits of manual document review. With legal, compliance, and commercial teams spending hours daily extracting insights from documents that existing tools could neither reliably read nor intelligently summarise, the organisation faced a compounding productivity problem directly constraining its ability to operate efficiently and scale. Worxwide designed and built an AI-native document intelligence platform to transform how the business interacted with its information assets.
The problem
Document complexity multi-format files, scanned records, handwritten notes, tables, and images embedded within lengthy reports had made reliable, scalable document processing an unsolved operational problem. Manual review was slow, inconsistent, and expensive, and the absence of intelligent search and extraction capabilities meant critical insights were effectively buried in archives that no one had the bandwidth to mine systematically.
- Complex Multi-Format Documents Resisting Reliable Reading — Contracts, scanned documents, handwritten annotations, images, and mixed-format files defeated existing document tools, producing incomplete extractions and unreliable summaries. The diversity of document formats meant no single traditional tool could reliably process the full corpus accurately.
- Use-Case-Specific Insight Extraction Impractical Manually — Lengthy documents containing large volumes of irrelevant content made it impractical to extract the specific clauses, data points, or insights needed for any given business context. Legal teams, compliance officers, and commercial managers each needed different information from the same documents a requirement that manual search could not efficiently satisfy.
- No Semantic Query or Cross-Version Document Discovery — There was no mechanism to query the document library semantically to ask a natural-language question and receive an accurate, sourced answer drawn from across multiple documents. Critical clauses were difficult to locate, version comparisons were manual, and discoverability across the full archive was effectively zero.
OUR SOLUTION
Worxwide conducted a detailed use-case analysis to understand the specific document workflows, search needs, and insight requirements across business functions. An AI pipeline was built incorporating OCR and layout-aware parsing for complex multi-format documents, with NLP and large language models enabling summarisation, entity extraction, and contextual insight generation. Semantic search and natural-language Q&A capabilities with source citations for every answer — were implemented to give every user the ability to query the document library conversationally and receive accurate, attributed responses.
- AI Pipeline with OCR and Layout-Aware Parsing — A custom AI document processing pipeline was built to reliably handle the full diversity of document formats including scanned files, handwritten documents, complex tables, and image-embedded PDFs with layout-aware parsing preserving structural context through the extraction process.
- LLM-Powered Summarisation and Entity Extraction — Large language models were applied to generate contextual summaries, extract key entities and clauses, and produce use-case-specific insights tailored to the needs of different business functions from legal clause extraction to compliance data points and commercial intelligence.
- Semantic Search and Natural-Language Q&A with Citations — A semantic search and Q&A layer gave users the ability to query the entire document archive through natural language asking specific questions and receiving accurate answers with source citations, transforming the archive from a passive repository into an active, queryable knowledge base.
Our work in action
The Impact
60% Reduction in Manual Document Review Effort
AI-powered extraction, summarisation, and intelligent search reduced the manual effort required to review and extract insights by nearly 60%, recovering significant time across legal, compliance, and commercial teams and enabling higher-value analytical work.
Faster and More Reliable Access to Critical Insights
Intelligent parsing, LLM summarisation, and semantic search dramatically reduced the time from document arrival to actionable insight — improving decision-making speed and the reliability of information underpinning commercial and compliance judgements.
Scalable Document Intelligence Across Growing Archives
The AI platform established a scalable foundation for document intelligence that could grow with the organisation’s archive, processing new document types and answering new classes of questions without requiring additional manual processing capacity.
