Artos

Integrations read, write and manage documents in SharePoint, Google Drive and Box and connect to Veeva RIM, the document management system most regulatory teams submit from. Automations reach across document stores, communications apps and analysis software, so a rule can rewrite a section in a stored document and post a summary of the change to Microsoft Teams. Single sign on and governance tools connect through the same integration layer.

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Each document type gets its own AI system, built from an ensemble of models that drafts text, tables, figures and graphs. Automations chain the steps that follow a change, rewriting affected sections, updating tables and graphs, tracking each change and notifying the team. Rules can span document management systems, messaging apps and analysis software, and non AI validation logic checks each automation as it runs.

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Search runs across a company's own documents and public sources such as ClinicalTrials.gov, PubMed and Drugs@FDA, on data storage Artos built for generative AI over life sciences material. Drafts pull from raw data files, study reports, internal documents and public literature, and Source Tracer links every generated section back to the material behind it. Inconsistency Intelligence follows where the same content sits across a document or a whole dossier.

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Inconsistency Intelligence edits affected sections in Changes Marked mode, so writers see every change it proposes and accept or rework it in the document. Artos's responsible AI policy commits it to assisting life sciences work, not fully automating it. Automations, though, rewrite sections and update tables when their rules fire, with no approval step before the change is made.

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Access control is built in from the database up, and integrations with a company's user management systems let it apply the same granular access rules it already enforces, with single sign on among the supported integrations. Artos is SOC 2 compliant, keeps each company's data isolated, works with its cloud and AI providers on zero data retention, and never trains models for one company on another's data. Its subprocessor list goes to customers on request, and existing customers are notified whenever it changes.

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Every action the AI takes, whether drafting a first version or running an automation, is written to an immutable audit log alongside every manual edit. Logs can be searched in real time, compared version against version and set to trigger notifications or automations on chosen events. Source Tracer shows which sources produced each section and how Artos changed it.

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Teams upload their templates and style guides once, and Artos fills new drafts to match them. Those templates are standing instructions, and the audit log is a record of past work. Nothing the agents learn on one document is kept as memory for the next.

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Private deployments with complete data isolation keep each company's data stored apart from every other's. No hosting region or customer hosted edition is given. Large organizations can be live within seven days.

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An AI system comes ready for each type of regulatory and R&D document, covering clinical and non clinical overviews and summaries, tabulated summaries, quality overall summaries, justifications of specifications, analytical validations, clinical study reports, investigator's brochures and responses to health authority questions. Teams can also take free eCTD templates from Artos.

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Automations start on their own when source data is updated or a document changes, among other events, with no one asking. A new data table can set off an updated summary and a note to the right person in Microsoft Teams. Drafting and section regeneration start from a user in the Artos workspace.

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Behind the drafting sits an ensemble of custom models built for life sciences documents, alongside models from cloud and AI providers Artos does not name, and the subprocessor list that would name them is shared only with customers. Customers do not choose or route between models.

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Engineering teams get an API for building data transformation workflows for mappings, database storage and document management, and to extract content from complex formats. No API reference, endpoints or authentication scheme is open to read.

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Automations carry built in validation logic, using non AI techniques, that checks each automation is doing what it should every time it runs. Artos tests its products before delivery. Customers get no test sets or scoring to evaluate a drafting agent on their own documents before rolling it out.

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Work happens through document management integrations and the Artos editor. None of its agents drives a browser or a desktop application.

Sourceartosai.com/feature/generative-ai-integrationsread 2026-10-13