Organisations hold enormous amounts of information. But a large part of it isn't neatly organised in a database. It sits in contracts, reports, case files, emails, forms, Word files and scanned documents.
For people that information is usually readable enough. For systems and AI applications, much less so. So staff have to open documents, assess them and retype them by hand. That costs time, raises the chance of errors and makes it hard to analyse information at scale.
As a result, valuable data goes unused. Not because the information is missing, but because it isn't available in a form your systems can do anything with.
AI Document Intelligence changes that. From an unreadable pile to a structured dataset.
What does AI Document Intelligence cover?
Document structuring & extraction
Turn unstructured documents into usable data automatically. With document parsing, OCR and AI extraction we recognise text, fields, tables and other relevant parts. The information is captured in a consistent structure, so systems can process it automatically. Even when documents differ in shape, layout or file type.
Semantic enrichment and classification
Let AI not only recognise information, but also understand what it means. We classify documents, add relevant attributes and establish connections between topics, concepts and files. So you can organise, filter and find documents automatically on the basis of their content, instead of only a file name or a fixed folder structure.
Vector embeddings and storage
Make document information suitable for semantic search and generative AI. We convert the meaning of text into vector embeddings and store these in a vector database. That lets AI applications find information on the basis of context and meaning, not only when a search term appears literally in a document.
Data platforms for document data
Bring structured and enriched document data together in a scalable data platform. The result is one environment in which information is available in a manageable way for analysis, applications and AI. We set up the technical infrastructure so new documents can be processed automatically and existing data sources can be connected.
What does AI Document Intelligence deliver?
Information from PDFs, scans, emails and forms is processed automatically.
Unstructured documents turn into consistent datasets you can analyse.
Information is found faster, on the basis of content, meaning and context.
Document data becomes immediately usable for dashboards, process automation and AI.
Your AI applications get access to better structured and more relevant information.
Why Indicia?
Indicia is a tech, data and AI partner that helps clients stay ahead in the AI era. We think strategically with you, build what is needed and support the people who work with it. For results in weeks, not months. With privacy and security as the foundation.
We combine expertise in data, AI and software development. That means we can not only get information out of documents, but also integrate it into the systems, data platforms and processes where the data has to deliver value.
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Proven expertise
30+ years in data, technology and AI
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End-to-end
From document analysis and architecture to build, integration and management
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Dependable
ISO 27001 certificate and ISAE 3000A Type II report
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AI Document Intelligence is the automatic recognition, extraction, structuring and enrichment of information from documents. That turns files systems find hard to process into usable data.
PDFs, Word files, emails, forms and scanned documents, among others. Which technique you need depends on the file type, the quality of the document and the information you want to get out of it.
Yes. With a combination of OCR, document parsing and AI, text and data can be recognised in both digital and scanned documents. The relevant fields and sections can then be turned into structured data automatically.
Automatic data extraction is recognising and capturing specific information from documents: names, dates, amounts, attributes, tables or categories. That data is then made available to systems, analyses or processes.
OCR stands for optical character recognition. The technique recognises written or printed text in scans and images, for example, and converts it into machine-readable text. OCR is often the first step in processing scanned documents.
Document parsing is analysing the structure and content of a document. It recognises things like headings, paragraphs, tables, fields and metadata, so the information can be processed consistently.