Independent researcher and creator of IDDA — Intelligent Deterministic Decision Architecture.

I work on deterministic decision layers for noisy, uncertain, high-volume data environments.

The core idea:

Raw data is not the same as operational meaning.
A decision layer should admit what is meaningful, suppress what is noise, and preserve an auditable decision trail.

IDDA is an architecture for deterministic, auditable, and stable decision-making under noisy inputs.

It is focused on:

• admissibility before execution,
• deterministic decision logic,
• noise suppression,
• class-level auditability,
• profile-based interpretation,
• stable operational behavior under repeated noisy events.

IDDA is not positioned as a black-box AI model.

It is a deterministic decision architecture designed to sit before execution, storage, alerting, or downstream analytics.

Interactive public-safe demo of IDDA-style decision behavior.

https://piotrpietruszewski-research.github.io/idda-interactive-lab/

Interactive public-safe boundary / admissibility console.

https://piotrpietruszewski-research.github.io/idda-boundary-console/

Public-safe report for deterministic reduction of noisy telemetry streams into compact, auditable decision classes.

Public report:

https://piotrpietruszewski-research.github.io/idda_log_governor_report_public/

Public artifact repository:

https://github.com/piotrpietruszewski-research/idda_log_governor_report_public

Public PoC highlights:

• 50,000,000 log lines processed in a local throughput benchmark,
• approximately 62–70k lines/s observed local throughput,
• 7,500,000 live Docker industrial telemetry events,
• 90.00% suppression in the Docker live run,
• reduction into 3 decision classes,
• API profile comparison: 19 classes vs 5 classes,
• class-level audit instead of full per-line storage.

Correlation-aware admissibility and active execution governance in layered architectures.

This work revisits the classical Swiss Cheese Model from the perspective of correlation-aware admissibility, pre-escalation regulation, and deterministic execution governance.

Related concepts:

• correlation-aware admissibility,
• active regulation,
• anti-cascade governance,
• trajectory preservation,
• bounded recovery,
• telemetry-supported auditability.

My current focus is on moving IDDA from theory into public-safe PoC artifacts:

• telemetry reduction,
• log governance,
• decision-class audit,
• deterministic observability support,
• industrial telemetry interpretation,
• profile-based decision granularity.

The goal is not to replace observability platforms.

The goal is to create a deterministic decision layer that can reduce noisy streams into compact, auditable, decision-ready signals before heavier storage, alerting, or analytics.

Public repositories intentionally exclude:

• source code for protected PoC logic,
• raw logs,
• JSONL audit files,
• internal event patterns,
• class IDs,
• Docker runners,
• implementation-specific profile logic,
• production thresholds and tuning.

Public materials show the architecture, behavior, and PoC results without exposing private implementation details.

All public PoC results are local synthetic research results.

They are not production guarantees.

A real deployment would require ingestion design, buffering, source tagging, monitoring, retention rules, access control, failover, integration work, and production validation.

I am open to discussing small, bounded, offline PoC cases where IDDA-style deterministic decision reporting may be useful.

A suitable first PoC does not require production integration.

Typical starting point:

• sanitized log export,
• telemetry sample,
• CSV / JSON operational data,
• bounded offline dataset,
• clear PoC scope,
• HTML decision-quality report as the output.

The goal of a first PoC is not to promise production outcomes.

The goal is to check whether noisy operational data can be reduced into compact, auditable, decision-ready signals while preserving a clear explanation trail.

For research, PoC discussion, or collaboration:

Piotr Pietruszewski
GitHub: https://github.com/piotrpietruszewski-research
Email: piotr@piotrpietruszewski.com