In pharma, a document is part of the product. Every batch, every study and every report leaves a trail that has to be described, classified, linked and kept for years. That is why the biggest time sink in pharma is not the decision itself but the manual work around documents - retyping data, hunting for the right version, checking that nothing has dropped out of the compliance chain.
In our experience, AI done right can genuinely cut that work, but only when it is treated as a tool under human control, not a black box that makes the calls. Our AI, Data and LLM skills in the RITS Group are real - they are built by our own AI, LLM and data teams, and we have delivered GenAI, RAG and LLM projects, including a knowledge assistant. We do not yet have a deployment inside a pharma plant, so we say it plainly: we provide the skills and the people who build this, not a ready-made case from the industry.
Where does pharma lose the most time on manual document work?
Before we automate anything, we map the places where people do a machine’s job:
- retyping data from certificates of analysis, protocols and forms into systems,
- classifying and labelling documents that arrive in dozens of formats,
- searching for the right version of a procedure, report or dossier,
- stitching together information scattered across emails, scans and PDFs.
These tasks are repetitive, error-prone and well described by rules - exactly where document automation in pharma pays back fastest.
How does AI classify documents and extract data (OCR and IDP)?
Here we start with the layer that feeds everything else. Our engineers build a pipeline where OCR and intelligent document processing (IDP) turn a scan into usable data:
- OCR reads text from scans, photos and PDFs,
- classification recognises the document type and routes it onward,
- data extraction pulls specific fields (batch number, date, result, signature),
- validation checks consistency and flags cases for a human to review.
The key point is that the model does not “guess quietly”: every result carries a confidence score, and anything below the threshold goes to review instead of slipping into the system unchecked.
How does AI support scientific and regulatory literature review?
Literature review is the second area where AI saves specialists hours. It does not replace the expert - it prepares the ground:
- searches publications, reports and databases against set criteria,
- summarises long texts down to the key findings,
- surfaces links and signals that are easy to miss by hand,
- points to the source of each passage, so it can be verified.
We design this so AI always leads back to the source rather than substituting its own version. The substantive judgment stays with the human, because they own the conclusion.
How do you automate pharma back-office processes (workflow and RPA)?
Documents are one thing, but a lot of repetitive clerical work circles around them. Here we tie workflow and RPA together with the AI layer:
- flow automation for documents, approvals and notifications,
- software robots (RPA) moving data between systems that do not integrate,
- business rules that watch deadlines and completeness,
- exceptions escalated to a person when something deviates from the pattern.
This setup lifts the routine off the teams and leaves them the work that needs judgment. We automate the process so that the audit trail is generated on its own - because in pharma it matters not only what the result is, but how it was reached.
What is an AI assistant for regulatory documentation?
This is the solution we know best, because it builds on RAG - the approach with which we already built a knowledge assistant. An assistant for regulatory documentation:
- answers questions from the company’s own procedures and dossiers, not from “general knowledge”,
- cites the specific document and passage the answer comes from,
- helps draft sections, summaries and responses to authorities’ questions,
- finds gaps and inconsistencies between versions.
Thanks to RAG, the model answers from a closed, controlled set of documents, and every answer is anchored in a source. That trait is exactly what sets a regulatory AI assistant apart from an ordinary chatbot.
Why does a human in the loop decide trust and compliance?
In a regulated environment the point is not for AI to decide, but to speed up a decision that a human makes and signs off. So we design the process so that:
- a specialist approves results before they enter the compliance system,
- every AI suggestion is explainable and tied to its source,
- a full trail is created of who accepted what and when,
- confidence thresholds route hard cases to review, not to an automaton.
In practice the model prepares, suggests and tidies up, while the final word and the responsibility stay with the team. This is not decoration - it is the condition for passing the audit and for trusting the solution.
Which AI and Data skills do we provide for a pharma project?
We shape the team around the specific stage of work, not “just in case”. On GenAI/RAG projects we staff roles such as:
- an AI / ML engineer building models, extraction and pipelines,
- a RAG / LLM engineer connecting the model to company knowledge,
- a data engineer sorting out sources, quality and access,
- an architect and DevOps/MLOps owning deployment, security and maintenance.
We develop these skills in the RITS Group, within our AI, LLM and data teams, and bring them into the client’s team as team augmentation or a dedicated team.
Which AI pitfalls should you avoid in a regulated environment?
Experience from LLM projects shows where it is easiest to trip up:
- hallucinations - the model “fills in” what is not in the source (answer: RAG and citation),
- sensitive data - risk of leaking into external models (answer: control of data and environment),
- no trail - a decision without history will not pass an audit (answer: logging and approvals),
- automation at any cost - wiring up a process no one had tidied first.
So we sort out the process and the data first, and only then plug AI into it. AI will not fix a mess - it will only speed it up.
How do we do it at RITS?
We do it as a nearshore partner from Poland, with real AI/Data competence in the Group:
- we develop AI, LLM and data through the RITS Group, within our AI/data teams,
- we have delivered GenAI, RAG and LLM projects, including a knowledge assistant,
- we start from the process and the data, and keep the human in the decision loop,
- we verify skills before the start and shape the team around the project stage.
We are honest about what we do not have: a ready AI deployment inside a pharma plant. What we do have are people who build such solutions in other industries, and we bring them into your team.
Where to start automating pharma documents with AI?
Start from the single most painful document or process - the one that eats the most hours and repeats most often. On that one you will see whether AI really shortens the work before you extend it to the rest. We will help assess which process fits the first step and which engagement model suits it - we covered the differences in our article on IT engagement models.
Want to automate pharma documents and processes with AI, with a human in the decision loop? Let’s talk - we’ll match the skills and the approach to your process.