Guides
Regulatory Intelligence and Pharma Compliance Automation
Regulatory Intelligence and Pharma Compliance Automation
This pillar addresses the automation of pharmaceutical compliance and quality workflows, helping companies stay adherent to Good Manufacturing Practice (GMP) while reducing manual overhead. Sub-guides cover 'How to automate pharma compliance with AI,' 'Building AI platforms for GMP adherence,' and 'Implementing real-time quality control in biomanufacturing' targeting regulatory bottlenecks in biotech.
How to Architect an AI-Powered GMP Compliance Platform
This guide covers the end-to-end system design for a platform that automates Good Manufacturing Practice (GMP) adherence. You will learn how to integrate data sources like LIMS and MES, implement real-time monitoring agents, and design audit trails that satisfy FDA 21 CFR Part 11 requirements. The architecture ensures continuous compliance by automating document control, deviation management, and corrective actions.
Setting Up an AI-Driven Regulatory Intelligence Pipeline
This guide explains how to build a system that autonomously monitors, parses, and analyzes regulatory updates from agencies like the FDA, EMA, and ICH. You will implement web scraping agents, natural language processing with models like Llama 3, and a knowledge graph to map changes to internal SOPs. The pipeline provides actionable alerts and impact assessments to keep your quality system current.
How to Implement an AI-Based Deviation Management System
This guide details the construction of an autonomous system for detecting, classifying, and initiating investigations for GMP deviations. You will integrate with manufacturing execution systems (MES), use anomaly detection algorithms to flag outliers, and implement a multi-agent workflow to route incidents, perform root cause analysis, and trigger CAPAs. The system reduces mean time to closure and improves data integrity.
Launching an Automated Regulatory Change Management Platform
This guide provides a blueprint for automating the entire change control lifecycle, from impact assessment to implementation verification. You will architect a system that uses AI to evaluate proposed changes against regulatory constraints, auto-generate required documentation, and track execution through integrated workflows. This ensures changes are managed consistently and audit-ready, linking directly to your Quality Management System (QMS).
How to Design an AI System for Real-Time Environmental Monitoring
This guide covers the integration of IoT sensors, computer vision, and predictive analytics for continuous monitoring of cleanrooms and utilities. You will learn to process streaming data for parameters like particulate counts, temperature, and humidity, using time-series models to predict excursions before they occur. The system automates alerts and generates compliance reports for areas critical to aseptic processing.
Setting Up a Predictive Compliance Risk Engine
This guide explains how to build a machine learning engine that scores and forecasts compliance risks across manufacturing sites and suppliers. You will aggregate data from audits, deviations, and process performance, then train models to identify high-risk patterns. The engine provides a dashboard for quality leaders to prioritize interventions, enabling a proactive, risk-based approach to GMP adherence.
How to Build an AI Platform for Continuous GMP Adherence
This guide outlines the development of a unified platform that orchestrates multiple compliance agents for end-to-end GMP coverage. It integrates modules for document management, training compliance, audit readiness, and batch record review into a cohesive system. You will learn to design agentic workflows that ensure cross-functional data flows and maintain a state of perpetual inspection readiness.
Launching an AI-Powered Batch Record Review System
This guide details the implementation of an AI agent that automates the review of electronic batch records (EBRs) against master formulas and in-process controls. Using computer vision for handwritten entries and NLP for text fields, the system flags discrepancies, missing signatures, and out-of-spec results. It integrates with MES to provide real-time release decisions, drastically reducing manual review time.
How to Architect a Self-Auditing Quality Management System (QMS)
This guide covers the design of an autonomous QMS that performs continuous internal audits against GMP regulations. You will implement agents that scan documentation, training records, and process data to identify non-conformances and control gaps. The system schedules follow-up actions, tracks closure, and generates audit reports, creating a closed-loop system for quality assurance. This relates to broader principles of autonomous workflow design.
Setting Up an AI-Driven Supplier Quality Assurance System
This guide explains how to automate supplier qualification, performance monitoring, and audit scheduling using AI. You will build a platform that ingests supplier data, certificates of analysis, and audit reports, using predictive models to score supplier risk. The system can auto-schedule audits based on risk scores and monitor for trends that indicate quality drift, ensuring robust supply chain integrity.
How to Implement AI for Proactive CAPA Management
This guide provides a technical framework for transforming corrective and preventive action (CAPA) from a reactive process to a predictive one. You will learn to connect the CAPA system to data sources like deviations, complaints, and audit findings. Using causal inference and pattern recognition, the AI identifies systemic root causes and recommends preventive actions before issues recur, ensuring effective closure.
Launching an AI-Powered Stability Study Monitoring System
This guide details the setup of an automated system for managing drug product stability studies per ICH guidelines. You will integrate with stability chambers, LIMS data, and use statistical forecasting models to predict shelf-life and detect out-of-trend results early. The system auto-generates stability protocols and reports, ensuring compliance and accelerating time-to-market for new products.
How to Design an AI System for Automated Documentation Compliance
This guide covers the automation of GMP document lifecycle management, including creation, review, approval, and archival. You will implement AI agents that check documents for regulatory keyword compliance, version control errors, and required metadata. The system enforces workflows, manages electronic signatures per 21 CFR Part 11, and ensures documents are always audit-ready. This is a core component of a GMP compliance platform.
Setting Up a Smart Alert System for GMP Non-Conformances
This guide explains how to build a real-time alerting engine that triages potential GMP breaches from across manufacturing and quality systems. You will define rules and machine learning models to prioritize alerts based on severity and regulatory impact. The system routes alerts to the appropriate personnel or autonomous agents for immediate action, integrating with notification platforms like Slack or Microsoft Teams.
How to Build an AI Framework for Automated Inspection Readiness
This guide provides a comprehensive approach to using AI to prepare for regulatory inspections. You will architect a system that continuously scans data and documentation, simulates inspector queries, and identifies potential findings. It auto-generates response packages and ensures all required evidence is pre-assembled and easily retrievable, transforming a reactive scramble into a managed, confident process.
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