Many AI initiatives start with general internet data. The real value for organisations often sits in their own sources: files, registers, drawings, reports, correspondence, heritage collections and historical documentation. 2dA makes those sources reliably digital and suitable for responsible further use.
Image quality, order, document boundaries and metadata determine whether OCR, HTR, chunking and embeddings work reliably later.
Rights, privacy, provenance, context and purpose must be clear first. Otherwise data may be digital, but not responsibly usable.
This route is for organisations that hold substantial proprietary information and want to use it better for search, knowledge management, document AI, analysis or research.
General internet information is widely available to AI systems. Organisations stand out through their own sources: specialist files, local history, technical documentation, policy archives, registers and collections that do not exist elsewhere.
We start with material, capture process, quality, OCR/HTR, metadata and delivery. Only then do AI, embeddings, RAG or chat applications become meaningful.
AI agents running locally within a company or public organisation can use digitised archives as a knowledge source. Think of files, decisions, reports, correspondence, policy documents, contracts, meeting minutes and historical documents.
When such an agent performs a task, it can retrieve relevant information from these archives and use that context in analysis, preparation or action. The agent then works not only with general AI knowledge, but also with the organisation's own institutional memory.
Well digitised archives can become a local knowledge layer for AI agents. The agent can search documents, identify connections, recognise earlier decisions, retrieve context and support actions with stronger evidence.
This turns the archive from a passive storage location into an active source for analysis, decision support and automation. The condition is that the digital foundation is sound: image quality, OCR/HTR, metadata, rights, context and document structure all matter.
Not every collection needs to be processed fully at once. A small exploration is often enough to assess quality, recognition, metadata, rights and usability for AI or retrieval.