Pharmaceuticals AI Solutions

ZechionAI offers AI strategies, AI development, research analytics, pharmaceutical data engineering, document AI, automated regulatory documents, RAG and knowledge systems, AI agents, integrations, governance, and managed AI services.

Yes. ZechionAI can create workflows to organize, normalize, analyze, visualize and report approved research datasets. The analytical results need to be interpreted and validated accordingly by qualified experts.

Yes. AI can help you with ingesting documents, classify documents, extract metadata, extract information, compare documents, summarise, search and route documents. The right process is dependent on the kind of document and intended use.

Yes. A document-classification workflow can classify documents according to rules, models and approved training or reference data, and exceptions from the workflow are taken for human review.

Yes. Document AI supports OCR for digital PDFs and as needed, scanned PDFs. The quality of the extraction will be influenced by the structure of the documents, the quality of the scan, the language of the documents, formatting, and the model configuration.

Yes. ZechionAI is capable of creating comparison workflows to detect differences between versions and show them for subsequent review.

Yes. AI can generate summaries to assist users in reading long documents. Summaries are not intended to be regulatory interpretations, but instead should serve as a review tool.

Yes. ZechionAI can develop controlled knowledge assistants that are linked to approved regulatory documents, internal policies, SOPs, correspondence, technical information or other approved repositories.

RAG is an acronym for Retrieval-Augmented Generation. Fetches relevant data from the configured sources and delivers it to an AI model to use for generating a response.

Yes. A well-designed RAG system can pull data from documented SOP repository, honoring access and document versions.

Yes, if a suitable technical integration or approved data-access method is offered by the repository. Additionally, the solution needs to adhere to organizational permissions and data-use requirements.

On theoretical grounds it is possible where it is technically designed and authorised. The integration can be achieved through APIs, approved connectors, databases, middleware or other interfaces that may be available in the environment of the LIMS.

Yes. Once the API, credentials, permissions and the architecture are in place, they can be used to integrate AI applications with the existing enterprise system.

Yes. AI can support areas such as document classification, literature screening, information extraction, case intake support, routing, and duplicate-identification support. Qualified safety professionals are still responsible for making "judicious" assessments and decisions.

Yes. AI can help in literature discovery, classification, screening, literature extraction and prioritization. There may still be a need for human review, especially if the screening has safety, scientific, or regulatory importance.

AI can help find and pull out relevant safety data from sources, which are configured. Identification does not result in a final safety assessment or case determination.

No. ZechionAI offers technology for the support of regulatory professionals. The qualified staff members are still accountable for the interpretation, decisions, submission and approval of regulations.

No. ZechionAI's solutions should be created with the purpose of helping to make decisions within a set of well-defined workflows, not making decisions on their own.

A typical workflow for the AI would be for it to do a defined task, present the output to an authorised individual, have that person review or modify what it has produced and record the action taken as appropriate.

Security can mean data segregation, secure APIs, monitoring, controlled access, RBAC, audit logging, environment separation, and encryption. The selection of specific controls should be based on the requirements of the organisation.

It is possible, depending on the architecture, infrastructure, models, integrations and security requirements selected. During solution design, deployment options should be considered.

Yes, as long as the chosen technology architecture enables hybrid deployment. Each component can run in an approved cloud, private or on-premises environment, depending on technical and security requirements.

AI governance may encompass use case approval, access to data, model documentation, evaluation, permissions, monitoring, auditability, change management, human oversight and lifecycle controls.

The decision to evaluate should be based on the use case. This can be the efficiency of classification, the accuracy of the extracted data, the quality of the retrieved data, the quality of the response, the number of exceptions, results of the human review, and other specified acceptance criteria depending on the workflow.

Yes. If the underlying architecture of RAG and document-retrieval systems allows it, source documents, passages, citations, or references can be displayed.

A system can be engineered to relate content to versions of documents and metadata. The implementation should take into consideration the organization's document-management and governance needs.

Yes. OCR is used to transform documents that are captured by the camera into machine-readable text before they are processed by downstream classifiers and extractors. If the scan is not done properly, it could affect the quality of the extraction.

Yes. Tables can be detected and retrieved as part of a document-processing systems design. For complex layouts, a special process and manual verification may be necessary.

A configured rule and context can be used to identify potentially missing fields, sections, metadata, references, or information that can be flagged by AI. Items that are flagged should be looked at, not assumed to be confirmed deficiencies.

Yes. AI and traditional workflow automation can gather approved content, create a structured summary, fill in reports and send out content for review.

Yes. SOP search, document classification, quality-document processing, CAPA documentation support, deviation-document support, audit preparation and quality reporting are just a few examples of the potential applications.

Yes. Controlled knowledge systems can be used in approved information retrieval, inquiry classification, content search, reporting, and for other well-defined workflows in medical affairs teams.

Yes. Depending on the client's needs and the access of available data, CROs may use AI to process study documents, organize research information, extract data, report results, perform data-quality workflows, and retrieve knowledge.

Yes. AI can be leveraged by smaller, emerging biotechnology companies for research data management, retrieval, document processing, analytical capabilities, regulatory workflows, and knowledge management.

Yes. A little smaller company can start with a narrower workflow instead of a wide enterprise platform. The scope will be specific to the data available, systems, resources and business priorities.

The length of a project can be quite variable. A proof of concept could be a smaller proof of concept that lasts less time than a production system of multiple data sources, integrations, validation activities, security controls, and governance requirements.

Yes. ZechionAI can design an engagement along a clear, specific pilot with a workflow, data set, user group, evaluation criteria, and success measure.

Requirements are user case specific. Data can be documents, structured data, metadata, system records, APIs, approved repositories or other approved sources.

Yes, if it is technically possible. The goal might be to embed into existing infrastructure instead of needless replacement of established infrastructure.

Yes. Custom applications can integrate AI models, data pipelines, document processing, enterprise integration, workflow automation, search, dashboards and user interfaces.

Yes. ZechionAI can be configured with task-specific agents having set permissions, tools, workflows, data sources, review controls, and escalation rules.

The system can be programmed to reject results with low confidence or unclear answers, ask for more detail, send it to a human for review, or avoid giving a definite answer.

Exception handling can be used to send out cases when they are uncertain, incomplete, failed or unusual to specific users or queues for review, rather than automatically completing the workflow.

Elements of monitoring may be model performance, extraction quality, retrieval performance, exception rates, system availability, user feedback, workflow performance, changes of source data, etc.

Yes. Roles, groups, repositories, document classification, permissions, and security policies can be used to create access.

Yes. Role based and permission aware architectures can limit access per department/organization if desired.

AI can be used to organize documents, find relevant records, classify documents, identify requested materials, and create document review packages. The responses to and representations made in final audits remain the responsibility of the organisation and its authorised persons.

Can help to automate repetitive tasks in suitable workflows when it comes to classification, extraction, comparison, summarization, routing, search, and data entry. The reduction should be based on their organisation's baseline.

There are agreed KPI's that can be used to measure value including processing time, manual effort, workflow cycle time, exception rate, search time, reporting time, adoption, and cost per document processed.

Outsourcing can give a company specialized engineering capacity, data expertise, document-AI, integration support, and flexible project resources. The best way will vary depending on the internal capabilities of the organization and the needs of the project.

Yes. Managed services may involve managed monitoring, managed maintenance, managed optimization, managed knowledge base updates, integration support, technical assistance, and governance support.

Yes, if technically possible and authorisation granted. There can be a number of different integrations, ranging from APIs, middleware, data pipelines, enterprise applications, repositories, and other methods.

Yes. One can take a phased approach starting with a single validated workflow and then branch out into more use cases once the organization assesses performance, adoption, risk and value.

ZechionAI highlights the control of access to the data, overseeing it, its evaluation, traceability, governance, monitoring, documentation, security, and appropriate validation. AI-generated output is not intended to be a replacement for regulatory, medical, scientific, safety or quality determinations.