Modernizing Compliance Workflows through AI/ML
VerifiNow is a US-based background screening and compliance management company. Their platform empowers HR teams and administrators to manage background checks, document submissions, and compliance workflows with accuracy and speed across healthcare, education, and corporate sectors.
- AI/ML-driven background verification
- Automated fraud and compliance checks
- Real-time analytics and reporting
Business Challenge
As the platform scaled, manual verification slowed onboarding, unstructured documents required lengthy review, and automation for fraud/compliance was limited. Handling thousands of concurrent requests strained scalability, and AI-driven insights were needed to improve accuracy and speed.
Manual Verifications
Slow, manual processes delayed onboarding.
Unstructured Documents
Time-consuming reviews for high document volumes.
Limited Automation
Gaps in fraud detection, compliance checks, validations.
Scalability Constraints
Thousands of concurrent requests taxed the system.
Need for AI Insights
Improve accuracy and speed with ML-driven insights.
Engagement Objectives
Build an AI/ML-driven verification system, automate document workflows and compliance checks, improve time-to-hire, ensure secure compliant handling, and enable real-time analytics.
Build an AI/ML-driven background verification system on AWS.
Automate document classification, fraud detection, and compliance checks.
Improve time-to-hire and operational efficiency via intelligent automation.
Ensure secure, compliant data handling (HIPAA, GDPR aligned).
Enable real-time analytics and reporting for admins and clients.
Solution Design
GenClouds modernized VerifiNow’s platform with AI/ML Development Services on AWS. The solution included:
Automated Data Pipelines
AWS Glue cleanses and prepares candidate records at scale.
Custom ML Models
TensorFlow/PyTorch for classification, identity validation, fraud detection.
Amazon SageMaker
Train, fine-tune, and deploy models with MLOps at scale.
NLP with Comprehend
Extract and analyze information from unstructured documents.
Real-time Decisioning
AWS Lambda + API Gateway accelerate verification workflows.
Secure Storage & Access
Amazon S3 with KMS encryption and IAM RBAC; end-to-end monitoring.
Solution Architecture
An AWS-native, secure, and scalable architecture spanning data engineering, ML/NLP, compute, storage, security, and governance.
Platform Architecture
Key components across data ingestion, ML/NLP, compute, storage, security, and monitoring.
- Data Engineering: AWS Glue, Kinesis for ingestion and preparation
- AI/ML: SageMaker, TensorFlow, PyTorch, Scikit-learn for model development
- NLP: Amazon Comprehend to extract and analyze compliance data
- Compute & Orchestration: AWS Lambda, ECS for scalable inference
- Storage: Amazon S3 (encrypted) for documents; Amazon RDS for transactional data
- Security: IAM roles, KMS encryption, VPC isolation (HIPAA/GDPR)
- Monitoring & Governance: CloudWatch, CloudTrail, model retraining workflows
Results & Outcomes
VerifiNow realized significant gains in speed, accuracy, scale, compliance, and operational efficiency.
40% Faster Verifications: Automation reduced onboarding times.
Accuracy & Reliability: ML models improved precision and reduced errors.
Scalability: AWS-native pipelines handled thousands of requests seamlessly.
Compliance & Security: HIPAA/GDPR readiness with encryption and governance.
Operational Efficiency: AI-driven workflows removed manual bottlenecks.
Conclusion
By leveraging AI/ML on AWS, VerifiNow transformed verification workflows into a scalable, secure, AI-powered platform. The solution accelerated hiring, improved compliance readiness, and positioned VerifiNow as a leader in next-gen background screening with a foundation for predictive analytics and innovation.