I’ve built and released IFM-CostIntel v1.0.0, a production-grade C11 financial intelligence engine designed to process normalized cloud billing data and transform it into deterministic, reconciled cost intelligence.
What it does
IFM-CostIntel provides:
Cost allocation with deterministic rule resolution
4D cost aggregation across provider, account, cost center, and resource
Baseline variance analysis
Directional anomaly detection
Financial reconciliation and conservation checks
Structured NDJSON output and audit telemetry
High-throughput processing designed for large billing workloads
The engine therefore uses fixed-point integer financial arithmetic rather than floating-point calculations and enforces mathematical conservation throughout processing.
During scalability engineering, I also replaced an O(N) baseline lookup with an O(1) FNV-1a open-addressing hash index.
The measured improvement at 100,000 baseline entries:
509,525 ns → 11.51 ns per lookup
≈ 44,268× lower lookup latency
The complete pipeline benchmark reached:
1.47M records/sec
265.67 MB/sec
Verification
The v1.0.0 release has been validated through:
✅ 8/8 standard tests passing
✅ 8/8 ASan/UBSan tests passing
✅ 10,000/10,000 differential records matching the reference oracle
✅ 100,000 adversarial fuzz iterations
✅ 100% financial reconciliation conservation
What I can build with this capability
I can apply this systems-engineering stack to projects involving:
Cloud FinOps • Billing Infrastructure • Financial Data Pipelines • C/C++ Performance Engineering • Data Normalization • Cost Allocation • Reconciliation Systems • High-throughput Processing
The engine is available as an open-source reference implementation, and I’m interested in applying the same engineering principles to custom financial-data and cloud-cost infrastructure projects.