Case Studies
Industrial IoT automation platform for robotic drilling
Key Results
- ~20% fewer migration-related defects
- Improved CAD-to-drilling alignment
- ~30% faster drawing-file processing
About the project
About the project
A global construction equipment manufacturer engaged PerformaCode to support the evolution of an industrial IoT platform used in semi-automated robotic drilling workflows. The platform coordinated engineering drawing processing, cloud-connected field operations, and operator-facing applications across multiple product generations.
The system combined cloud services, CAD-processing pipelines, frontend applications, and industrial device workflows used to prepare and execute drilling plans in construction environments. As the platform evolved, the client needed to modernize aging infrastructure, stabilize production behavior, and maintain compatibility with existing operational processes.
PerformaCode joined the client’s engineering organization as a dedicated technical team. While product priorities and task ownership remained client-led, PerformaCode managed team continuity, senior oversight, and execution quality across backend, frontend, cloud, migration, debugging, and DevOps workstreams.
PerformaCode engineers contributed to backend, frontend, cloud, and DevOps workstreams, with a focus on migration, integration, processing reliability, and deployment automation. The engagement included legacy Java service modernization, IBM Cloud to AWS migration, CAD conversion optimization, and stabilization after major architectural changes.
Over the engagement, the team supported continuous updates across two platform generations while helping preserve production continuity during infrastructure and technology transitions.
2
engineers
4+
years
Outstaff
delivery model
Client challenges
The internal team needed additional expertise in .NET migration, AWS-based infrastructure, GitLab CI/CD automation, and troubleshooting legacy CAD-processing components that combined Java services with C++ conversion logic.
The system mixed legacy Java services, Angular frontend code, C++ conversion components, cloud services, and database layers. Small changes could affect drawing import, coordinate handling, deployment behavior, or downstream robotic workflows.
Migration work was performed around active production processes. IBM Cloud to AWS, Java to .NET, Bamboo to GitLab, and frontend modernization all evolved in parallel with ongoing platform releases.
The CAD-processing pipeline was particularly sensitive. Large engineering drawing files, overloaded geometry data, and coordinate-calculation errors could affect drilling-plan alignment and create visible issues during field operations.
Post-migration stabilization required practical debugging rather than clean architectural isolation. Engineers often worked directly from logs, runtime behavior, and production symptoms across multiple interconnected services.
Tasks performed
- Migrated legacy Java services to a modern .NET-based backend while preserving existing industrial workflow behavior.
- Refactored legacy service logic to separate business rules from framework-specific Java components before migration.
- Modernized frontend components in Angular to support the second-generation platform interface and ongoing maintenance.
- Supported IBM Cloud to AWS migration by integrating AWS Lambda, DynamoDB, CloudWatch, and Secrets Manager.
- Hardened environment configuration using AWS Secrets Manager and deployment-specific service settings.
- Implemented structured logging and monitoring for migration diagnostics, production debugging, and cloud-service observability.
- Optimized CAD-to-SVG conversion logic used to prepare drawing data for robotic drilling workflows.
- Optimized message-query execution for large job-site drawing files to improve processing throughput and platform responsiveness.
- Updated database access patterns for job-site files, conversion metadata, and processing-status tracking.
- Resolved coordinate-calculation defects that affected drilling-plan alignment and downstream robotic execution.
- Improved error handling in CAD-processing workflows for failed conversions, malformed files, and overloaded geometry data.
- Performed post-migration debugging through log analysis, defect isolation, and production issue troubleshooting.
- Built CI/CD deployment workflows for automated build, validation, and publishing across environments.
- Migrated DevOps pipelines from Bamboo to GitLab to improve release consistency and deployment control.
- Validated migrated workflows against existing field-operation scenarios to reduce behavior drift after modernization.
- Maintained platform updates across two generations while working inside the client-managed engineering roadmap.
Project results
~20% fewer post-migration defects
Reduced production defects after migration by stabilizing backend services, improving logging visibility, and resolving runtime inconsistencies across cloud and deployment workflows.
Reduced drilling misalignment
Improved drilling-plan accuracy by fixing coordinate-calculation defects and eliminating geometry-alignment inconsistencies in CAD-processing workflows.
~30% faster CAD file processing
Increased processing throughput for large engineering drawing files by optimizing CAD-to-SVG conversion logic, database access patterns, and message-query execution.
Days-to-hours deployment cycles
Reduced deployment preparation and release time by replacing manual Bamboo workflows with automated GitLab CI/CD pipelines.
Lower release instability after modernization
Improved platform reliability during ongoing migration by separating legacy business logic from framework-dependent Java components and stabilizing cross-service integrations.
Standardized AWS-based deployment workflows
Improved environment consistency and operational maintainability by migrating infrastructure workflows from IBM Cloud to AWS services and centralized configuration management.
Continuous releases across 2 platform generations
Maintained uninterrupted platform updates and production support during simultaneous cloud migration, frontend modernization, backend refactoring, and DevOps transition work.
Value we bring
Migration discipline for systems that cannot pause
We modernize industrial platforms by controlling behavior change before changing too much architecture at once. Legacy logic, cloud services, deployment paths, and user-facing workflows are separated, validated, and migrated in stages so teams can keep releasing while reducing dependence on outdated frameworks. This lowers migration risk without forcing a full rewrite that the business cannot operationally absorb.
Cross-layer debugging across industrial IoT stacks
We diagnose production defects across application code, cloud infrastructure, databases, file-processing pipelines, and deployment tooling instead of treating each layer as a separate queue. By following failures through logs, runtime behavior, data flow, and environment configuration, we shorten the path from symptom to root cause.
Processing reliability for engineering data workflows
We build and stabilize workflows where engineering data has to survive conversion, validation, querying, and downstream operational use. This requires attention to file size, geometry handling, coordinate logic, status tracking, and error recovery, not only API behavior.
Technologies
- C/C++
- .NET Core
- Angular
- Java Spring Boot
- AWS
- IBM Cloud
- AWS Lambda
- DynamoDB
- CloudWatch
- MongoDB
- GitLab CI/CD
- Bamboo
- Docker
- REST API
- Python
- SonarQube
- Nexus
- MQTT
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