See the Platform in Action
Watch how AI-powered sterile monitoring automates smoke study analysis and delivers up to 96% faster incident response in pharmaceutical cleanrooms.
The Challenge
Traditional smoke study validation relies on manual video review, making the process time-intensive, subjective, and difficult to scale.
- Extensive manual video review and high operational effort
- Inconsistent interpretation of airflow behavior
- Delayed validation reporting and compliance readiness
- Fragmented evidence collection and audit challenges
- Difficulty in detecting short-duration airflow disruptions
- Scalability limitations across multiple facilities
Business Objectives
Improve Validation Accuracy
Accelerate Validation Cycle
Strengthen Audit Readiness
Centralize Evidence
Enable Scalable Multi-Facility Deployment
Our AI-Powered Solution
An intelligent platform that automates the entire smoke study validation lifecycle, from secure video ingestion to AI-driven airflow analysis and audit-ready evidence.
- Smoke Study Video
- AI Video Processing
- Airflow Analysis
- Anomaly Detection
- Dashboards & Alerts
- Audit-Ready Evidence
AI-driven accuracy
Advanced models analyze every frame for precise, repeatable results.
Real-time alerts
Anomalies surface instantly with timestamped visual evidence.
Audit-ready
Structured records and evidence for complete traceability.
Enterprise-grade
Secure, scalable, and built for regulated environments.
AI Processing Workflow
1Upload Video
2Video Preprocessing
3Smoke Segmentation
4Optical Flow Analysis
5Airflow Validation
6AI Anomaly Detection
7Timestamped Alerts
8Dashboard Visualization
9Audit Repository
Solution Architecture
A cloud-native architecture on AWS, engineered for secure video processing, scalable AI inference, and end-to-end observability.
Experience Layer
- Next.js Frontend
- Web Application
- Interactive Dashboards
API & Security
- API Gateway
- NestJS Backend
- Microsoft Entra ID Authentication
Processing Layer
- AWS Lambda
- Amazon ECS
- Event-Driven Pipelines
AI / ML Layer
- AWS SageMaker
- Computer Vision Models
- Optical Flow Engines
Data & Messaging
- Amazon S3 Storage
- PostgreSQL Database
- Amazon SNS 路 EventBridge
Monitoring & Delivery
- Amazon CloudWatch
- AWS CodePipeline 路 CodeBuild
- GitHub CI/CD
Technology Stack
Next.js
NestJS
AWS SageMaker
Amazon S3
PostgreSQL
AWS Lambda
Amazon ECS
Amazon SNS
Amazon EventBridge
Microsoft Entra ID
AWS IAM 路 KMS 路 TLS Encryption
Amazon CloudWatch
AWS CodePipeline
AWS CodeBuild
GitHubAmazon SNSAmazon EventBridgeMicrosoft Entra IDAWS IAM 路 KMS 路 TLS EncryptionAmazon CloudWatchAWS CodePipelineAWS CodeBuildGitHubNext.jsNestJSAWS SageMakerAmazon S3PostgreSQLAWS LambdaAmazon ECSCore Features
AI Smoke Detection
Airflow Behavior Analysis
Optical Flow Tracking
Anomaly Detection & Classification
Real-Time Alerting
Timestamped Evidence
Image-to-Alert Mapping
Interactive Dashboard
Audit Logging & Traceability
Secure User Access (SSO)
Business Benefits
Up to 70% reduction in manual review effort
Faster validation and reporting
Improved accuracy and consistency
Complete audit readiness
Centralized visibility across facilities and teams
Scalable and future-ready AI platform
Security & Compliance
- End-to-End Encryption (TLS)
- Role-Based Access Control
- Microsoft Entra ID SSO
- Audit Logs & Traceability
- Secure Cloud: AWS Security Best Practices
Future-Ready Platform
- Predictive Analytics
- Multi-Facility Monitoring
- Integration with Digital Systems
Frequently Asked Questions
Computer vision models analyze every frame of smoke study video (segmentation, optical flow, and anomaly detection) so teams avoid exhaustive manual review. The platform can cut manual review effort by up to 70% and accelerate validation reporting for regulated cleanrooms.
Yes. Frame-level optical flow and anomaly classification catch brief airflow deviations that human reviewers often miss, then surface timestamped visual evidence and real-time alerts for faster incident response.
Structured records, image-to-alert mapping, and a centralized audit repository preserve traceability from video ingestion through airflow findings, supporting compliance readiness across multi-facility deployments.
A cloud-native AWS stack processes video through Lambda and ECS pipelines, runs SageMaker computer-vision inference, stores evidence in S3 and PostgreSQL, and secures access with Microsoft Entra ID SSO plus IAM, KMS, and TLS controls.
Manual review is slow, subjective, and hard to scale across sites. AI-driven airflow analysis improves consistency, centralizes visibility, and helps pharmaceutical manufacturers respond to incidents up to 96% faster while staying audit-ready.
Ready to transform your validation workflows?
Let's build an intelligent, scalable, and AI-powered solution tailored to your needs.