Design Philosophy:
Thesis: Our current data infrastructure, relying on standard text formats, is penalizing our growth. We are paying to store and transfer "virtual air."
Metric |
Current Cost (100k Records) |
Business Problem |
File Size |
30.9 Megabytes (MB) |
Higher cloud storage costs and increased egress fees. |
Processing Time |
2.5 - 5.0 seconds |
Delays in decision-making and suboptimal user experience. |
Security |
External Encryption (Add-on) |
Complicates key management and adds performance overhead. |
Quote: "We need a solution that treats our large datasets as assets, not as costly liabilities."
We have developed and tested a Proprietary Ultra-Efficiency Format that eliminates data redundancy and radically optimizes resource consumption at the infrastructure level.
Our tests have demonstrated consistent and superior size reduction efficiency, even with complex, nested data structures.
Number of Records |
Initial Size (MB) |
Final Size (MB) |
Total Reduction |
100 |
0.03 MB |
0.004 MB |
84.24% |
1,000 |
0.31 MB |
0.04 MB |
88.05% |
10,000 |
3.08 MB |
0.36 MB |
88.37% |
100,000 |
30.94 MB |
3.58 MB |
88.43% |
The solution guarantees a consistent average reduction of 88.4% in data transfer volume. This directly translates to:
The second pillar of value is speed. By executing the compression logic with low-level programming, we eliminate the sluggishness of high-level scripting languages.
Metric |
Current Baseline (High-Level Language) |
Target (Proprietary Format) |
Added Value |
Process Time |
Seconds (2.5s - 5.0s) |
Milliseconds (< 0.5s) |
Real-time processing, enabling new features and low-latency microservices. |
Server Utilization |
High RAM/CPU Consumption |
Low Consumption and Stable Peaks |
Better resource utilization and lower operational cost per instance. |
(Once the results from the low-level processing speed test are available, this slide will be updated with actual numbers to complete the argument).
The Proprietary Ultra-Efficiency Format is a strategic investment that delivers:
"The cost reduction results are conclusive. The next step is to integrate this low-level solution to capture the speed and infrastructure optimization gains."
We read structured data such as JSON with up to 4 levels of object nesting, process, convert, validate, encrypt, and compress it using our unique technology for intelligent serialization.
PON is the future of structured and secure-by-design data transfer and storage!
Hardware used for testing:
Intel(R) Xeon(R) Gold 6126 CPU @ 2.60GHz x 4
Memory: 24GB
Environment and language used:
Interpreted scripting language in CLI mode
Public Dataset – 100,000 and 1000000 JSON MIN
100k rows test JSON min:

100k rows test JSON Pretty Print:

1M rows test JSON min:
| Input | Original Size | PON4 Encrypted | Reduction |
|---|---|---|---|
| Minified JSON | 29.51 MB | 3.39 MB | 88.78 % |
Pretty-Print JSON (99 % Production) |
87.88 MB | 3.39 MB | 96.14 % |
Features no one else offers simultaneously:
| Format | Final Size | Reduction | Native Encryption | Total Time (incl. Encryption) |
|---|---|---|---|---|
| PON4-SC_ULTRA | 3.39 MB | 96.14 % | ✅ | < 3.8 s → < 1.5 s C |
| Parquet + zstd-19 | ~26 MB | ~70 % | ❌ | ~6–8 s |
| Protobuf + zstd-19 | ~28 MB | ~68 % | ❌ | ~5–7 s |
| Avro + Snappy | ~32 MB | ~64 % | ❌ | ~7–9 s |
| Everything Else | > 30 MB | < 66 % | ❌ | > 5 s |
A single binary. A single command.
96 % less volume, military-grade encryption, and indestructible data... forever.