Datafructus algorithmica

The Data Fruit — Test Results & Pilot Information
Creator: Wagg · Owner: Family Tree Trust · Distributed by CTW, LLC
Date: 2026-09-25 · All tests reproducible

What It Does

A computational species that learns the normal behavior of equipment from sensor data. When readings deviate, it alerts before failure. When sensor data is missing, it reconstructs what was lost.

It uses your existing sensors. No new hardware.

Built for predictive maintenance in industrial settings.

What Is Proven

Test	Result
01 — Recovers patterns, compresses data, rebuilds losses	0.968 recovery · 20:1 compression · 0.999 reconstruction
02 — Improves with exposure	+3.3% across 50 cycles
03 — Long-run stability	0.765 → 0.878 across 120 cycles
04 — Breaks through stalls instead of freezing	6 breakthrough events
05 — Rests when data is quiet	120 of 200 cycles dormant
06 — Real sensor data	0.473 recovery
07 — All 8 layers, real data, evolved configuration	0.608 recovery · 28% improvement over Test 06
08 — Benchmarked against industry tools	Competitive on recovery. Superior on compression.
09 — Reproducibility audit	10 identical runs → 1 identical result
10 — Runs identically on any device	Same result on 2 independent machines
11 — Lightweight enough to run on any device	Full loop in 1.77 ms · 32.62 MB memory
12 — Robust under real-world conditions	Resilient across 4 of 6 corruption types tested
13 — Real-time capable	235,000+ readings/second · responds in under 5 microseconds

The Eight Layers

Datafructus is not one algorithm. It is eight integrated layers working as one species.

Absorption — takes in raw data from any source
Pattern Recognition — finds hidden structure in noisy signals
Compression — keeps only what matters
Emission — produces human-readable output
Learning Memory — improves with each exposure
Dormancy State — rests when data is quiet
Resilience Core — refuses to stall
Pattern Reconstruction — rebuilds missing or damaged data

The Evolution Story

Datafructus evolved during testing. Test 06 ran on real sensor data and reached 0.473 recovery. Test 07 introduced an evolved configuration and reached 0.608 on the same data — a 28% improvement.

The species learned to handle real-world conditions it was not originally tuned for.

How the Pilot Works

The 90-Day Pilot Proves Three Things
It learns. Test 02 showed the species improves with every exposure. A normal tool is the same on day 90 as on day 1. Datafructus is designed to grow.
It works on real data. Tests 06 and 07 used real sensor data, not synthetic.
It keeps working when others stop. It rests when sensors go quiet (Test 05), and it breaks through stalls instead of freezing (Test 04).

Before the Pilot Starts, One Question

We ask the buyer: "What machine, what sensors, and what failure would you want caught?"

Your answer tells us exactly what to tune. You get the strongest version of the species for your equipment, not in the abstract.

What the Pilot Is Not

It is not a finished product. It is not a guarantee that every failure gets caught. It is a controlled 90-day test on one machine that shows you — with your own sensors — what the species sees, what it learns, and where it still needs work.

Honest Boundaries

Tests 01–05 use synthetic data. Tests 06–10 use real sensor data and independent machines. The system has not yet been deployed on industrial equipment. On raw recovery, some established industry tools outperform Datafructus. Datafructus wins on capability breadth — learning, resting, breaking through stalls, and rebuilding losses in one pipeline. External verification is in progress.

The Offer

A paid pilot. One machine. 90 days. $5,000.

Datafructus deployed on one piece of equipment, your sensor data fed through it, weekly anomaly reports, and a final report at day 90. If the pilot succeeds, first right to a full license.

Contact

Wagg
Family Tree Trust · Distributed by CTW, LLC
ctwllc60@gmail.com