AI Consulting for Pharma

We do not validate systems for you and we issue no certifications — validation is your quality function’s responsibility within your own framework. What we do is produce the documentation that process needs, while the system is being built rather than afterwards: a written description of intended use and boundaries, records of the datasets used with their provenance and versions, the acceptance criteria agreed before development started, held-out evaluation results that can be regenerated on demand, error analysis broken down by failure type, versioned model and code artifacts, and a description of the human review points in the workflow.

In practice we ask your quality colleagues to review the planned evidence package during scoping, so what we generate matches what they will later need — and so nobody discovers a documentation gap after development is finished.

Integration is scoped in discovery, because a result that does not reach the system your staff already use is not worth building. We build against the interfaces your landscape exposes — MES, LIMS, ERP, quality management and document management systems, historians, warehouse and serialization platforms — via APIs, database connections, message queues, or file-based exchange where that is what a legacy system supports.

To be clear about phrasing: this is a description of our capability and approach, not a claim about specific installed systems at other pharmaceutical clients. During discovery we assess your actual systems and their interfaces and agree the integration path before development begins, and your IT team receives the documentation needed to own it afterwards.

This is the normal situation in pharmaceutical manufacturing, and it changes the method rather than ruling the project out. Three approaches carry most of the weight: anomaly detection, which learns what a good unit looks like and flags deviation instead of requiring examples of every defect type; transfer learning and augmentation, which extract far more from a few hundred examples than training from scratch ever could; and deliberate defect collection, where known-defective or engineered samples are photographed under production conditions to build the classes that matter.

Evaluation has to change too: with severe imbalance, overall accuracy is a misleading number, so we agree metrics such as recall on defects and false-reject rate up front and report them separately. If the data genuinely cannot support the required reliability, we say so during assessment rather than after you have funded development.

Trained reviewers make the final call — always. AI is well suited to the mechanical layer of case processing: reading unstructured narratives, extracting entities such as products, events, dates and reporter details, classifying case type, detecting duplicates, and ordering the queue by apparent seriousness. It is not suited to deciding causality or making a reportability determination, and we would decline a scope framed that way.

Accuracy is established on your own historical cases, with metrics agreed in advance and reported per field and per class rather than as one headline number. The design principle is conservative: low-confidence extractions are flagged rather than silently accepted, nothing is auto-closed, and every model-suggested value remains visible and editable with the reviewer’s decision recorded in the audit trail.

Architecturally, not contractually alone. Your data can stay inside your environment: models can be trained and run on-premises or in your private cloud, with no dependency on external inference services. Where a hosted component is used, it is agreed explicitly and scoped to data that carries no sensitive process content. We apply GDPR-grade processing terms by default for every client worldwide, practise data minimization so a model receives only what the task requires, and we do not use your data to train public models or reuse it for other clients.

Where useful, development can begin on masked, subsetted, or synthetic data so that sensitive process parameters are not exposed during early experimentation at all.

It depends on where the data lives and how fast the answer must arrive. On-premises or edge deployment is usually right for line-side inspection: the images never leave the site, latency is controlled, and the system keeps running if the network does not. Private cloud often suits forecasting, safety-text processing, and commercial analytics, where data volumes are large, workloads are bursty, and the data can be handled under your existing governance.

Many programmes end up hybrid — inference at the site, aggregate analysis and retraining centrally. We design for portability so the choice is a deployment decision rather than an architectural lock-in, and we avoid designs that make you dependent on our continued involvement.

A proof of concept on data you already hold — historical images, batch records, safety narratives, shipment data — typically takes weeks and does not need to wait for a production campaign. That is deliberate: it makes the first decision cheap and fast.

The realistic constraint appears later. If a system must be evaluated on live line data, its schedule attaches to your campaign calendar, because a product family that runs a few times a year offers few windows to collect representative data or run a shadow trial. We plan around that explicitly: identify which campaigns matter, agree what must be captured during each, and run the system in shadow mode alongside existing checks before it influences anything. Change control and internal review cycles are scheduled as project phases too, rather than treated as a surprise at the end.

That property has to be engineered, and it is the whole point of the design. The assistant runs on a privately hosted model and answers by retrieving passages from the document set you approve — labelling, approved response documents, internal guidance — with citations shown so the user verifies rather than trusts. It is scoped to answer "this is not covered in the approved sources" instead of improvising, and out-of-scope requests are routed to a person.

Governance sits with you: your medical affairs function decides what enters the knowledge base and when it is updated, and answers change only when the approved content changes. We also recommend an evaluation set of representative enquiries with expected sourcing, so behaviour is measured before rollout and re-checked after content updates.

Yes, and the distinction is the important part. Investigations are slow largely because of retrieval and comparison: finding similar past deviations, pulling the related batch and equipment records, and reading long free-text histories. Models are genuinely good at that layer — clustering similar events, surfacing precedents and their outcomes, extracting structured facts from narratives, and highlighting where entries disagree.

Root cause determination, product impact, and any decision on disposition remain human judgments made within your quality system. We build the tool as an evidence-gathering aid with every suggestion traceable to the source record, so an investigator can verify each item rather than inherit a conclusion.

Yes. We are headquartered in the Frankfurt Rhine-Main region at Robert-Bosch-Straße 7 in Darmstadt, with a second office at Kemperplatz 1A in Berlin, and we deliver worldwide. Projects run remotely with structured communication at every stage, and for multinational pharmaceutical groups an EU-based partner applying GDPR-grade data handling by default is frequently an advantage in itself. Reach us at info@aisuperior.com or +49 6151 7076909.