Document Chunking for Regulatory and Quality Teams in Life Science

Document chunking is the process of splitting a document into smaller passages, called chunks, that still make sense on their own. Quality and regulatory teams often call the same step content structuring or componentization. With Acodis each chunk keeps the context it needs, such as its heading path, its table structure, its units and a reference to the source document, so it can be retrieved, reused or handed to an LLM without losing meaning. For clinical studies, SOPs and quality documents this is what makes reuse safe: a chunk without its context is just text, and text without a source cannot be checked.

Acodis starts from the structure the document already has, so sections, subsections, tables and footnotes stay together, and every table is kept as one unit instead of being cut across chunk boundaries. Around 15 specialised models run per document, each trained on a specific element such as titles, tables or footnotes, and business rules are applied for the specific document type. Where it helps, LLM post-processing can be added on top.

Detect the table as a table before chunking, and keep it as one chunk together with its caption, header row, units and footnotes. Acodis runs specialised models per document element, so a table is recognised, reconstructed with its rows and columns and then chunked as a whole. Values keep the unit and the column they belong to, which is what stops a retrieval pipeline from returning a number that means something else.

Every chunk carries the identifier of its source document, the version and the page or section it came from. That reference stays with the chunk when it is exported, so whatever your downstream system generates can be linked back to the exact place in the original file. Reviewers check the evidence instead of re-reading the document, and audits get the trail they expect.

Chunks are delivered as XML, including DITA, or as JSON, and where a standard applies we map to it, for example FHIR in health data contexts. Delivery runs through an API or a drop-in folder into your data lake, search index, RAG pipeline or authoring platform. Input is equally flexible: PDFs, Word files and other formats from the sources you already use.

No. Acodis does the chunking and hands the chunks over to the systems you already run, whether that is your data lake, your RAG stack, your LLM or your authoring platform. We are not a drafting tool. Faster drafting is the outcome of clean, traceable content, not a product we put in front of your teams.

In a couple of hours we can set up a dedicated instance and run our standard models on your studies. Around 15 specialised models run per document, each trained on a specific element such as titles, tables or footnotes, so you get usable chunks before any tuning has happened. Within a few weeks the models and rules are adapted to the patterns of your own documents.

Yes. Acodis is built for GxP-regulated environments: deterministic models, full audit trails, version control and traceability back to the source, with human-in-the-loop review where judgement is needed. Every client runs on a dedicated instance. Your models, your data.

Data extraction pulls defined fields out of a document, for example the batch number or a result value on a certificate of analysis, and hands you a record. Document chunking keeps the document as content: it splits the whole file into passages that stay meaningful on their own, with their tables, section context and source reference attached. Extraction answers "what is the value?", chunking answers "which content can we reuse, and where did it come from?". Most teams end up using both, see our data extraction use case.

Yes. Each chunk carries structured metadata such as document type, section, product, version and source page, which is what a knowledge graph needs to create entities and relations. Pharma teams use this to connect product and CMC information across approved documents and make it searchable, with every fact traceable to its source. Acodis delivers the chunks, the graph runs in your own stack.