ETR · Emotional Theory of Rationality
From black-box prediction to causal, traceable and adaptive intelligence. A CELL-based cognitive architecture for industrial and regulated systems, on a gated route towards general intelligence.
01De-risked to date
€2.24M
Funding secured since 2021
€700K founders · €1.4M non-dilutive · €140K F&F
€1.4M
Non-dilutive public funding
NEOTEC–CDTI · INVESTIGO · NextGenerationEU · DGA
49%
Of the €5M round committed
State-backed deep-tech investment vehicles
Step 2
EIC Accelerator
Technically validated by EU reviewers
02The problem
Rule-based systems are explicit and auditable, but become rigid when conditions were not anticipated. Statistical and neural systems adapt, but their internal representations are hard to inspect.
In critical or regulated environments, high average accuracy is not enough when operators cannot reconstruct why a decision was made, or how the system will react to a novel condition.
Auditable, but rigid
Every decision is explicit, until reality changes. No learning from experience.
Adaptive and auditable
Learns from experience. Traceability by construction in the Shared Knowledge Graph. Small data, low compute.
Neither
Operator experience and manual control: hard to scale, no systematic trail.
Adaptive, but opaque
Captures non-linear relationships, but the explanation comes after the decision.
Neuro-symbolic hybrids03Why now
EU AI Act high-risk requirements turn traceability, logging, technical documentation and human oversight into procurement issues, not optional extras.
27 Jul 2026
Reg. (EU) 2026/1744 sets the high-risk timetable.
Today
Buyers must adapt procurement and technical documentation before the deadlines.
2 Dec 2027
Annex III systems must comply.
2 Aug 2028
Annex I: medical devices, machinery, vehicles.
04The solution
A cognitive emergent architecture designed to combine adaptability, resource efficiency and native traceability.
small data → regulated, predicted, learned locally → inspectable decision trail
Small data: the current state of the system (signals, events, historian data) instead of a big-data training set.
Small adaptive units keep critical variables in range, predict short- and long-term outcomes and update from experience, under energy and time constraints.
Events, relations, decisions and consequences are written to the Shared Knowledge Graph: the trace is native, not reconstructed post-hoc.
ETR as a product
ETR complements statistical and GenAI components where decisions must remain adaptive, auditable and controllable.
05Capability-gated roadmap
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
Reactive regulation
Maintain critical variables and prioritise responses under changing inputs.
Control / monitoring
Productised · ETR 0.5
Adaptive learning
Learn non-linear action–outcome associations and update behaviour from experience.
Adaptive automation
Next gate · 2027 · ETR 0.6
Operational causality
Test action–consequence relationships, adapt continuously, remain efficient and traceable.
Industrial critical systems
2028
Context + planning
Episodic context, persistent goals, multi-step planning and anticipation.
Unlocks autonomous systems
2029
Abstraction
Transfer useful structure across tasks; cooperate and coordinate across CELL groups.
NPCs & interactive agents at consumer scale
AGI target · gated, not dated
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.
06Applications
ETR ships as a software engine integrated into third-party systems, and complements statistical and GenAI components where decisions must remain adaptive, auditable and controllable.
Available today
ETR 0.5 Engine
Fully traceable and explainable machine learning for decision-critical systems: industry, medicine, finance or justice.
Learn more
Embodied intelligence
Intelligent robotics
An operating system that lets robots assist us in tasks that, today, only humans can do.
Learn more
Multidevice intelligence
Personal AAGI assistant
A personal intelligence that follows you across devices, learning your goals and context.
Learn more
NPCs that think
Videogames & virtual worlds
Avatars and NPCs with human-like capabilities, running on the player’s device.
Learn more
Beyond remote control
Autonomous space explorers
Fully autonomous robots exploring extraterrestrial environments before human arrival.
Learn more
07Scientific foundation
Complex, effective and efficient behaviour emerges from dynamic interaction, context and regulation, not only from adding components.
302
neurons in C. elegans
Its full wiring diagram was mapped in 1986, yet predicting behaviour from that structure alone remains a scientific challenge.
86Bn
neurons in a human brain
Orders of magnitude more components…
20W
…on the power of a dim light bulb
Efficiency comes from regulation, not from count.
“Emotions are the brain’s resource-optimisation and stimulus-prioritisation mechanism: the principle behind ETR’s CELLs.”
08Newsroom
The Mindkind founder contributes to the discussion on the economic and technological limits of current AI scaling and the need to investigate new AI architectures
Read postMario Garcés says an AI pause could expose economic and technical limits, while creating room to test alternative architectures
Read postThe Mindkind has been selected for the 2026/27 cohort of Creative Destruction Lab (CDL) – San Sebastián, in Artificial Intelligence stream
Read postThe Mindkind
News, announcements and articles from The Mindkind.
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