Case study
How we consolidated five acquired businesses into one cloud-native platform and enabled 84% of screenings to be completed in less than 24 hours.
Metrics
acquired businesses consolidated into one platform
of screenings completed in less than 24 hours
Avg. Order turnaround times ranging from a few hours to 2.6 days
Project Description
We helped a leading U.S. background screening provider rebuild its core software platform from the ground up. Through a comprehensive cloud-native application development initiative, we replaced several fragmented systems with a unified platform built on Microsoft Azure. The new solution supports end-to-end background screening operations, automates complex workflows, connects with external providers through secure APIs, and gives clients, candidates, and internal teams greater visibility into every order.
The resulting cloud-native architecture provides the scalability, automation, observability, and operational flexibility the client needs to introduce new products, onboard customers, integrate future acquisitions, and continue its digital transformation.
Client
The company serves employers, educational institutions, volunteer organizations, contractors, and individual consumers. Its services help organizations conduct compliant background checks and make informed decisions about hiring, enrollment, and engagement.
The client operates in an integration-heavy, highly regulated environment. Its platform must communicate with external data providers, screening vendors, CRM systems, financial platforms, notification services, and compliance-related systems while protecting sensitive personal information.
Problem
Different business units relied on different applications, workflows, integrations, and user experiences, making operations increasingly difficult to scale and maintain.
The client needed a unified platform designed around its business processes, compliance obligations, integration ecosystem, and long-term growth strategy.
Key challenges included:
Solution
The cloud-native architecture used loosely coupled microservices, Docker containers, Kubernetes, APIs, and asynchronous messaging. This allowed individual services to be developed, deployed, and scaled independently.
The platform supported:
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If you submit a request today, your MVP will be ready
as early as April 19, 2027
Key Features
Azure Application Insights and the Elastic Stack provide centralized logging, tracing, performance monitoring, and incident-management capabilities. This observability helps teams identify bottlenecks, investigate failures, and understand platform behavior across complex workflows and integrations.
Candidate-facing workflows are optimized for mobile devices and provide clearer instructions, greater transparency, and the ability to reuse previously submitted information.
Clients receive real-time visibility into background check progress, order status, and required actions. Configurable account settings allow organizations to tailor the platform to their operational needs.
A single ecosystem now supports multiple acquired businesses, business units, screening products, and operational teams.
The platform automates order creation, fulfillment tasks, approvals, communications, and status transitions, reducing manual work and improving consistency.
Camunda provides visual workflow modeling for complex screening and compliance processes, making business logic more transparent and manageable.
Fulfillment teams can manage tasks, monitor key performance indicators, investigate issues, and optimize processing efficiency from one interface.
Isolated integration components support external vendors, screening providers, CRM platforms, financial systems, and notification services.
Granular permissions help ensure that users can access only the information and actions appropriate to their roles.
Automated notifications keep clients, candidates, vendors, and internal teams informed as orders move through each workflow.
Selected platform capabilities were implemented using Azure Functions, enabling serverless execution for event-driven workloads while reducing operational overhead and improving resource efficiency.
To accelerate delivery without compromising quality, our engineering team incorporated AI-assisted workflows into its daily development practices. AI was used as an engineering productivity tool—not as an autonomous replacement for developers. Architecture, security, implementation, and production decisions remained under full human ownership.
Developers used AI agents to conduct preliminary code reviews before submitting pull requests. This helped identify potential issues earlier, improve code consistency, and reduce the amount of routine review work required from senior engineers.
The use of AI-assisted engineering helped the team shorten development cycles, improve code-review efficiency, and maintain quality through human-led validation.
The outcome
From a technology perspective, the platform transformed difficult-to-maintain business logic into a modular, observable, and configurable system.
From a business perspective, it gave the client the flexibility to grow without continuing to add disconnected applications, manual workarounds, and costly technical debt.
5
acquired businesses consolidated into one platform. The project replaced fragmented systems inherited through acquisitions with a unified platform supporting multiple business units.
84%
of screenings completed in less than 24 hours. Workflow automation, optimized processing, asynchronous messaging, and scalable cloud infrastructure helped the platform complete most screenings within one day.
2.6 days
Average turnaround times ranging from a few hours to 2.6 days. Turnaround times vary by screening type and complexity, with average processing ranging from several hours to 2.6 days.
Additional outcomes include:
Increased workflow automation
Reduced manual operational effort
Improved order turnaround times
Greater visibility into every background check
Better experiences for clients, candidates, and internal teams
Faster onboarding of new clients and screening of products
Easier integration with external providers
Improved fault tolerance and platform availability
Automated scaling during demand increases
Faster and safer software releases
Improved portability across cloud environments
Stronger security and compliance controls
Greater observability across workflows and services
Reduced dependence on monolithic applications
Readiness for future acquisitions and business expansion
Technologies
Our AI consulting and development team works across the leading AI frameworks, cloud platforms, and data science tools, selecting the right stack for your specific requirements rather than defaulting to a single ecosystem.
Frontend
React, NX Monorepo, Redux, React Query, React Hook Form, Microsoft Authentication Library
Backend
ASP.NET, Entity Framework Core, MediatR, Camunda BPMN Engine, Background Workers
Cloud and Infrastructure
Microsoft Azure, Azure Service Bus, Azure Blob Storage, Azure Key Vault, Azure Application Insights, Elastic Stack, Redis
Engineering and Quality
Azure DevOps, CI/CD pipelines, Automated testing, SonarQube, Feature flags, OWASP security scanning
If you submit a request today, your MVP will be ready
as early as April 19, 2027
