BUKYA NARESH
Systems Architect | Performance & Precision Engineering
New to Contra
BUKYA is building their profile!
⚡ IFM-CostIntel — Deterministic FinOps & Cloud Cost Intelligence Engine
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
Why it matters
Financial pipelines cannot afford silent rounding errors, inconsistent allocation, or unexplained reconciliation differences.
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.
🔗 GitHub:
IFM-CostIntel Repository (https://github.com/CloudOps-Financial-Platform/ifm-costintel?utm_source=chatgpt.com)
🔖 v1.0.0 Release:
IFM-CostIntel v1.0.0 (https://github.com/CloudOps-Financial-Platform/ifm-costintel/releases/tag/v1.0.0?utm_source=chatgpt.com)
Need a high-performance financial or cloud-cost data pipeline? Let's talk.
1
8
Billing Data Gateway v1.0.0 — Zero-Copy C11 Cloud Billing Engine
1
1
Building reliable software means designing for imperfect real-world data—not perfect demos.
This week, I upgraded our open-source cloud billing ingestion engine to handle unpredictable CSV data drift automatically through dynamic header resolution and defensive validation.
Instead of assuming fixed column layouts, the parser dynamically adapts to changing vendor schemas while protecting downstream financial pipelines from malformed or incomplete records.
The result? Software that is more maintainable, resilient, and built for production environments where data rarely arrives in a perfect format.
The same engineering principles apply to client systems involving:
• Automated Data Pipelines & ETL
• Custom Business & Financial Tools
• Low-Latency Backend Infrastructure
• System Resiliency & Input Validation
I enjoy solving complex problems where software needs to be dependable, scalable, and engineered for long-term maintainability.
Sovereign Code Repository:
https://github.com/CloudOps-Financial-Platform/billing-data-gateway
#CustomSoftwareDevelopment #DataEngineering #Automation #BackendDevelopment #OpenSource
1
134
Over the last few weeks I've been building Forge-Core, a systems programming project focused on AI-agent infrastructure.
The project started after noticing a recurring pattern in agent frameworks:
Many execution failures don't immediately crash.
The process stays alive.
Logs continue flowing.
Resources continue being consumed.
Yet the system is no longer progressing toward the intended outcome.
To explore this problem, I built a high-performance telemetry ingestion engine using:
• Native C11
• SIMD processing
• Memory-mapped I/O
• Multi-threaded pipelines
Current benchmark:
274M+ trace rows/sec
The engineering challenge has been fascinating, but the bigger question is whether infrastructure teams need better ways to identify costly execution anomalies before they become operational incidents.
Still researching.
Still testing assumptions.
Still building.
#SystemsProgramming #AIInfrastructure #OpenSource #Engineering
1
79