Reanimating a hand-crafted C++ model for the cloud era
Reverse engineered a decade-old predictive system into a secure, autoscaling cloud deployment with modern DevOps guardrails.
Clients served
1,500+
Daily requests
2.8M
SLA adherence
99.99%
Overview
An industrial analytics firm depended on a custom C++ model written more than a decade ago. Tribal knowledge and handwritten math notebooks were the only references, making enhancements impossible and outages risky.
We partnered with the customer to reverse engineer the model, modernize the architecture, and deliver a resilient cloud-native deployment that could keep pace with growing demand.
Challenges
- No surviving documentation or tests existed for the original model, creating uncertainty in every change.
- The legacy binaries were tied to end-of-life hardware with no clear migration path to the cloud.
- Security, networking, and observability patterns were nonexistent, risking compliance breaches for sensitive telemetry.
Approach
Hands-on model introspection
Disassembled the C++ codebase, reconstructed mathematical workflows, and paired with domain experts to capture a definitive knowledge base for the algorithm.
Cloud-native platform buildout
Built container images, hardened VPC networking, IAM policies, and secret management while wiring autoscaling compute instances and serverless lambdas for burst workloads.
End-to-end automation and testing
Established CI/CD pipelines with compilation gates, code and data unit tests, synthetic load testing, and observability dashboards to guarantee safe, repeatable releases.
Impact delivered
- The platform now handles an average of 2.8 million monetizable requests per day with sub-second response times.
- Containerized deployments and automated rollouts reduced release cycles from quarterly to weekly while meeting a 99.99% SLA.
- Advanced caching and workload orchestration cut cloud compute costs by 35% while preserving accuracy guarantees.
Key lessons
- Even undocumented legacy models can thrive when reverse engineering is paired with modern automation.
- Autoscaling and serverless patterns complement high-throughput inference when tuned with real workload profiles.
- Comprehensive testing across code, data, and infrastructure is essential when reviving critical legacy assets.
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