AI Document Automation for Pharma & Life Sciences

Speed, accuracy and compliance: teams typically save around 60-80% of the time spent on document-based processes, remove manual re-typing errors, and get structured, validated data that is ready for analytics and GenAI initiatives. The same data foundation accelerates regulatory filings and batch releases.

General-purpose LLMs are hard to validate for GxP use: outputs vary run to run, accuracy on complex tables and layouts is inconsistent, and there is no built-in review workflow. Acodis trains machine learning models per document type, which is why they reach 99%+ accuracy on long, complex scientific documents; in Acodis benchmarking on pharmaceutical batch records, general-purpose models made 21x more errors, largely on tables, checkboxes and signatures. The models are deterministic (same input, same output), run at a fraction of the compute (around 6x cheaper per page), and combine with human-in-the-loop validation for auditable results.

No. Each client's models run in a dedicated instance, and any further training from your documents improves only your model, in your instance. No model weights or data are shared across clients. You also control training and deployment of your own models, including when new versions go live: older versions are never force-deprecated, so you decide when, or if, to move to a new one.

Documents come in as PDFs or scans, a machine learning model tailored to the document type extracts the relevant data, and the platform validates every value against your parameters. Low-confidence values are routed to a human reviewer. The result is document processing automation from intake to export: structured, validated data delivered straight into your systems.

Yes. Acodis works as document workflow automation software for regulated document processes: documents come in, data is extracted and validated, low-confidence values route to a human reviewer, and structured results flow into your downstream systems. Each use case, from CoA data capture to batch record review, runs this workflow independently on the same platform.

A typical example is Certificate of Analysis processing: incoming CoAs are read automatically, test results are extracted and validated, and product release gets faster. Batch record review, stability report processing and clinical study data extraction follow the same pattern.

Yes. R&D teams use Acodis to structure clinical study reports and historic research documents so the data can be reused for new analyses and studies instead of staying locked in PDFs. Quality and Regulatory teams then work on the same validated platform.

Acodis extracts, chunks and contextualizes documents into structured, validated data. That AI-ready data feeds RAG pipelines and generative AI applications reliably, instead of feeding raw PDFs to a model and inheriting their errors.

Implementations are phased: start with one document type and use case, prove the ROI, then scale. A first use case is typically live within weeks, not months, with limited setup effort on your side.