Blueprint delivered a Databricks-native fraud detection and application anomaly solution for a large enterprise customer in two weeks, using custom GitHub Copilot review skills to accelerate secure feature delivery by 100x.
Client snapshot
Who:
A large enterprise customer responsible for certifying individuals across multiple classifications.
Use Case:
DevOps Modernization
Applicable Industries:
- Public Sector / Government
- Financial Services
- Healthcare & Insurance
- Telecommunications
- Retail & eCommerce
- Media & Entertainment
Work summary
What we did:
- Designed and deployed a Databricks-native fraud detection and anomaly identification platform to process large volumes of structured and unstructured application data.
- Built capabilities for large-scale data ingestion, normalization, and cross-document correlation to detect inconsistencies and fraudulent patterns across certification workflows.
- Enabled continuous evolution of fraud detection logic through rapid deployment of new validation rules and scalable data processing architecture.
- Implemented CI/CD pipelines and modern software lifecycle practices to support ongoing platform enhancements and reliability.
- Developed custom GitHub Copilot review skills to automate pull request evaluations across security, compliance, infrastructure, and engineering best practices.
- Established an automated multi-persona code review system, eliminating manual bottlenecks while maintaining enterprise-grade governance and quality controls.
Client background
The client was in the process of strategically aligning their technology stack with the Microsoft ecosystem, including Azure, Power Platform, and Databricks. However, their existing RPA estate, built on legacy PowerShell scripts, and SQL Server, was a major source of friction. The previous RPA platform was expensive, difficult to integrate with core systems, and highly unstable. Many of the active bots, including a critical “megabot” with dozens of sub-processes, were broken and required constant human intervention, undermining the very purpose of automation. At the same time, accumulated tech debt across their homegrown applications was creating additional operational inefficiencies and cost pressures, limiting the ability to scale.
The problem
Legacy workflows and siloed data created blind spots in fraud detection and increased operational risk.
A large enterprise customer responsible for certifying individuals across multiple classifications was struggling to process extremely large volumes of structured and unstructured application data, supporting evidence, and cross-system records.
The customer’s legacy review processes relied on fragmented workflows across disparate data sources and inconsistent document formats, making it difficult to identify submission anomalies, evidence inconsistencies, and fraudulent patterns during the certification process.
This created several operational risks:
- Delayed processing timelines for legitimate applicants
- Limited ability to detect fraud and deception during intake
- Increased approvals of fraudulent submissions
- Higher investigation costs after downstream discovery
- Internal compliance violations caused by incomplete review controls
- Elevated audit and mission risk for the customer
Because fraud patterns continuously evolved, the customer also needed a solution that could be rapidly enhanced as new anomaly patterns emerged.
The solution
A Databricks-native solution with integrated AI-assisted code review for scalable, secure fraud detection.
Blueprint designed and deployed a Databricks-native intelligent fraud detection solution that leveraged multiple core platform services to ingest, normalize, correlate, and continuously evaluate high-volume application data and supporting evidence.
The architecture was designed to process both structured and unstructured records across disparate sources, enabling:
- Delayed processing timelines for legitimate applicants
- Limited ability to detect fraud and deception during intake
- Increased approvals of fraudulent submissions
- Higher investigation costs after downstream discovery
- Internal compliance violations caused by incomplete review controls
- Elevated audit and mission risk for the agency
To ensure the small engineering team could maintain rapid release velocity, Blueprint also implemented custom GitHub Copilot skills for automated pull request reviews.
These Copilot skills introduced multi-persona automated code review, evaluating every PR simultaneously through the lenses of:
- Security controls
- Compliance requirements
- Infrastructure standards
- Software engineering best practices
This created an automated quality gate that reduced manual PR review bottlenecks while preserving enterprise-grade code governance.
Impact
Stronger fraud prevention, reduced risk, and 100x faster software delivery.
The combined Databricks and GitHub Copilot solution transformed both fraud operations and engineering velocity.
Mission impact
- Improved anomaly detection across large-scale certification workflows
- Increased identification of fraudulent and deceptive submissions
- Reduced risk of invalid certifications
- Strengthened internal compliance posture
- Improved ability to adapt detection logic as fraud tactics evolved
Engineering impact
- Reduced PR review cycles from 1–2 hours to minutes
- Eliminated code review bottlenecks for an 8-person engineering team
- Accelerated feature integration and release cycles by 100x
- Enabled simultaneous security, compliance, and infrastructure review
- Allowed engineers to focus on detection enhancements instead of manual review overhead
The result was a continuously improving fraud detection platform capable of evolving at the speed of mission needs.
