Introducing, N-Code.

A state of the art encoding protocol for tabular file formats

Step One:

Review the official “What is N-Code?” summary to understand

how it differs from traditional binary encoding.

Step Two:

Test out the interactive demo on Google Colab for the N-Code decoder.

Choose among 64 encoded files in the official Basel Lake and perform the decode script. Output statistics are provided in the script and aggregated over your session.

Step Three:

Visit the official Decoder-Only repository and inspect both the README and code for consistency.

Super-Linear Gains against the Industry Standard:

deeper compression &‍ ‍faster retrieval

Natively resides in the Parquet ecosystem

Minimal adoption friction and low vendor lock-in risk.

Opportunistic selection: is only applied

when it clearly beats standard encoding

Encoded files are invertible with standard Parquet —

Fatal errors gracefully fail-over to standard encoding

But, don’t take our word for it.

Independently Verifying the Official Benchmarks

Step Four:

Download the official N-Code technical specification binder.

Review the sections on key failure modes, design assumptions, and intended design patterns for data developers and architects.

Security engineers should review the component-wise audit and exposure analysis.

Step Five:

The specification binder contains a code that waives the fee to the Client Sandbox.

Register an account and run up to 10 individual encodes, or a total of 1.3 GB data processed by the encoder.

N-Code

Description & Product Scope

    • Petabyte-scale tabular data stored in parquet-compatible systems

    • Enterprise data lakes, lakehouses, warehouses, and archival layers

    • Structured datasets with stable schemas and repeated analytical access

    • High-volume fact tables, event tables, telemetry aggregates, and operational records

    • Applications where storage cost, transfer cost, and scan efficiency are valued

    • Designed for structured tabular data, not arbitrary unstructured blobs

    • Encoding may require offline analysis

    • Requires basic schema identification for each dataset

      • Best results are achieved when dimensional or categorical fields are explicitly defined

    • Compute resources require a minimal GPU for bulk operations

      • Requirements are modest; Entry-level GPUs achieve parallelism at scale.

    • Compression for strict structured/tabular datasets

    • Parquet-focused compression and reconstruction workflows

    • Residual-based encoding compatible with commodity compression layers

    • Dataset profiling, benchmark reports, and compression feasibility analysis

    • Model-plus-residual storage patterns designed for enterprise data infrastructure

    • Evaluation against baseline codecs such as Snappy and zstd

    • Integration planning for data lakes, archival stores, and analytical pipelines

    • Reconstruction validation, error analysis, and decode performance measurement

    • Image, video, audio, PDF, document, or general object compression

    • JSON document stores, MongoDB-style semi-structured records, and arbitrary nested blobs

    • Real-time transactional database replacement

    • End-user BI dashboards or analytics applications

    • Data cleaning, ETL consulting, governance remediation, or warehouse migration
      Replacing cloud storage providers, compute engines, or query platforms

    • Consumer-facing compression tools

    • Lossless Reconstruction to some defined tolerance for decimal precision

    • Deterministic Output

    • Compression artifacts are versioned and reproducible across controlled benchmark runs

    • Failure modes are documented rather than hidden behind aggregate averages

    • Customer data remains under agreed deployment, access, and retention controls

  • Our product suite is deployed selectively in enterprise environments - prioritizing those with major storage capacity and demands.

    Engagement typically begins with a limited technical evaluation focused on pilot datasets.

    Custom licensing can then be drafted once customer fit is verified. Pricing will attempt to capture a percentage of projected annual savings, with credits issued if projected value is over-estimated.

  • Technical Specifications and Descriptions can be found in three categories.

    • N-Code Specifications Sheet

      • This is the