Medical Research & Pharma
AI Workflows

AI drafts comprehensive grant proposals for NIH, NSF, ERC, and private foundations — including specific aims, research strategy, significance and innovation sections, budget justifications, biosketches, and facility descriptions. The system learns from successful proposals in your institution's history, adapts to funder-specific formatting requirements, and ensures compliance with submission guidelines. Supports collaborative editing with PI and co-investigator review cycles, all tracked in Odoo.

AI assists the entire publication workflow: generating structured manuscript drafts from research data, formatting for target journal requirements, creating figures and table descriptions, writing cover letters, suggesting reviewers, managing co-author contributions and approvals, and tracking submission status across multiple journals. Supports ICMJE guidelines and journal-specific formatting.

AI generates ICH-GCP compliant clinical trial protocols including study design rationale, eligibility criteria, treatment arms, endpoint definitions, statistical analysis plans, safety monitoring procedures, and data collection schedules. Pulls from published literature and regulatory precedents to ensure protocols meet FDA, EMA, and local regulatory standards. Supports Phase I through Phase IV trials across therapeutic areas.

AI creates targeted patient recruitment campaigns for clinical trials: generating IRB-compliant recruitment materials, designing multi-channel outreach strategies (digital, community, physician referral), creating screening questionnaires, building trial-specific landing pages, and tracking recruitment funnel metrics. Adapts messaging for different demographics and health literacy levels while maintaining regulatory compliance.

AI conducts systematic literature reviews: searching databases (PubMed, Scopus, Web of Science), screening abstracts against inclusion/exclusion criteria, extracting data from selected papers, performing quality assessment using PRISMA guidelines, generating forest plots and summary tables, and writing the narrative synthesis. Supports both systematic reviews and rapid evidence assessments for grant applications and regulatory submissions.

AI optimizes laboratory operations: equipment scheduling and maintenance prediction, reagent inventory management with automatic reorder points, safety compliance documentation, lab notebook organization, protocol management with version control, and personnel scheduling. Generates lab meeting agendas from ongoing project status, tracks experiment timelines, and creates equipment usage reports for grant budget justification.

AI generates detailed research budgets and justifications: personnel effort calculations with fringe benefit rates, equipment quotes and depreciation schedules, supplies and reagent cost projections, travel budgets, subcontract budgets, F&A cost calculations, and multi-year escalation factors. Formats budgets for specific funder requirements (NIH modular vs. detailed, NSF formats) and generates the budget justification narrative that reviewers scrutinize.

AI compiles and formats regulatory submissions for FDA (IND, NDA, BLA, 510(k)), EMA (MAA, CTA), and other regulatory bodies. Automates the assembly of Common Technical Document (CTD) modules, generates summaries of clinical and non-clinical data, creates regulatory correspondence, and ensures cross-referencing integrity across thousands of pages. Tracks submission timelines and agency interactions.

AI generates informed consent documents at appropriate health literacy levels: translating complex study procedures into plain language, creating visual consent aids, generating multilingual versions, building electronic consent (eConsent) workflows with comprehension checks, and ensuring compliance with 21 CFR 50 and ICH-GCP requirements. Tracks consent versions and re-consent requirements when protocols are amended.

AI monitors and reports adverse drug events: processing individual case safety reports (ICSRs), generating MedDRA-coded narratives, detecting safety signals from accumulated data, creating periodic safety update reports (PSURs/PBRERs), and ensuring compliance with FDA MedWatch, EMA EudraVigilance, and ICH E2B reporting timelines. Automates the transition from individual case processing to aggregate safety analysis.