ETR 0.5 · FLY-level engine
Fully traceable, explainable ML / RL / MoE
ETR 0.5 productises WORM (reactive regulation) and FLY (adaptive learning) with native traceability across both. It brings explainable machine learning into decision-critical systems: industry, medicine, finance or justice.
01The challenge
Opaque models can’t enter critical systems
In deep neural networks, decisions are made through internal processes that are difficult to interpret: it is hard to understand how and why a model reaches a conclusion.
ETR 0.5 combines the strengths of two complementary families, neural networks and decision trees/random forests, and learns from each interaction, accumulating knowledge that can be applied across applications.
SaaS
Hosted engine, integrated through APIs.
On-Premise
Inside your infrastructure, for regulated data.
Edge
Bounded compute on embedded devices.
02How it compares
The best of both families, without their limits
vs
Neural networks
Detect non-linear combined effects, but decisions are neither traceable nor explainable.
- Traceability. Continuous information on which factors influence the final decision, and how.
- Adaptability. No retraining when a new feature becomes available; a neural network would have to start from scratch.
- Transfer learning. Knowledge moves between systems, and features can be removed without retraining.
vs
Decision trees & random forests
Traceable, but blind to combined features unless each has a strong individual effect.
- Crossed-variable interactions. Captures hidden relationships with high impact in non-linear systems.
- Flexibility. Greater control over decision-making, with tunable parameters per problem.
- Scalability and fusion. New features without retraining; knowledge from two separately trained ETR systems can be merged.
- Continuous processing and RL. Adapts to dynamic environments where inputs change over time.
03System parameters
Built for real-world constraints
The parameters that define how ETR 0.5 adapts to each application.
Input variables
A large, dynamic and expanding input space without retraining, for environments where features evolve or new variables are discovered.
Computational resources
Resource-efficient in training and operation, with modular updates: suited to embedded systems, edge devices and strict energy budgets.
Maximum response time
Fast, deterministic responses once trained, with a traceable logic path for real-time decisions.
Degrees of freedom
High-dimensional outputs and multi-variable decisions: it sets objectives, evaluates strategies and generates alternatives.
04Capability-gated roadmap
Evolution-inspired capability levels
Evolution-inspired, not biologically simulated: each level is a measurable capability, a benchmark and a commercial use case. The upside is capability-gated, not a binary bet.
Productised · ETR 0.5
WORM
Reactive regulation
Maintain critical variables and prioritise responses under changing inputs.
Control / monitoringProductised · ETR 0.5
FLY
Adaptive learning
Learn non-linear action–outcome associations and update behaviour from experience.
Adaptive automationNext gate · 2027 · ETR 0.6
BEE
Operational causality
Test action–consequence relationships, adapt continuously, remain efficient and traceable.
Industrial critical systems2028
MOUSE
Context + planning
Episodic context, persistent goals, multi-step planning and anticipation.
Unlocks autonomous systems2029
PRIMATE
Abstraction
Transfer useful structure across tasks; cooperate and coordinate across CELL groups.
NPCs & interactive agents at consumer scaleAGI target · gated, not dated
HUMAN
General cognition
Open-ended generalisation, language, metacognition and cross-domain reasoning.
General cognition
Current platform: ETR 0.5 covers WORM and FLY in productised form, with native traceability across both. Next gate: BEE, operational causality (ETR 0.6, 2027). AGI remains the destination; each gate is the investable milestone.
The Mindkind
AGI is the destination.
Traceable adaptive intelligence is the business on the way.
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