CARNOS ™
Privacy-preserving natural language intelligence for offline environments.
A zero-infrastructure approach to human-computer interaction that eliminates cloud dependency and reduces environmental impact.
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Privacy-preserving natural language intelligence for offline environments.
A zero-infrastructure approach to human-computer interaction that eliminates cloud dependency and reduces environmental impact.
Read More
Most AI systems assume cloud access, data collection, and centralized computing.
Current embedded AI solutions still rely on infrastructure and training data
| • Requires cloud updates | • Requires training data |
| • Opaque non-deterministic behavior | • Not suitable for children or regulated systems |
| • Non-auditable learning provenance | • Requires access to massive data centers to operate |
A fully offline natural language system designed for constrained environments
| What it is | What it is not |
|---|---|
| ✓ On-device natural language understanding | ✗ Not a chatbot |
| ✓ No cloud, no massive training datasets, no large language model | ✗ Not speech recognition |
| ✓ Deterministic and explainable | ✗ Not generative AI |
Eliminating infrastructure simplifies systems and reduces energy use
Converts output from the core engine into human readable sentences
| What it does | How it operates |
|---|---|
| ✓ Transforms grammatically incorrect sentences into a standard form | ✓ Uses a curated set of linguistic relationships derived from examples |
| ✓ Preserves original meaning and intent | ✓ Applies deterministic transformations rather than probabilistic predictions |
| ✓ No open-ended text generation | ✓ Same input always produces the same output |
The core engine produces an intermediate representation that is deterministically converted into human-readable grammar
| What it does | How it differs from generative AI |
|---|---|
| ✓ Interprets input sentences by analyzing context, meaning, and relationships | ✗ No next-token prediction outside bounded contexts |
| ✓ Resolves meaning from structure and context rather than purely predicted text sequences | ✗ No probabilistic sampling |
| ✓ Selects transformations based on understood relationships | ✗ No generative completion |
Environmental benefits are a direct result of architectural efficiency
Designed for environments where cloud AI cannot operate
The need for privacy-first, efficient technology is growing
Core Engine proof-of-concept is complete with end-to-end system functional and integrated
| ✓ Authentication & user management | ✓ Semantic graph database |
| ✓ Language processing & understanding | ✓ Core operational logic |
| ✓ Mobile app with speech recognition and text-to-speech | ✓ Desktop application |
| ✓ Messaging infrastructure and synchronization |
The core platform proof-of-concept is complete. Current work focuses on refining language quality to maximize accuracy and usability.
Core Engine Development
Proof-of-concept core engine and offline natural language processing.
Grammar Correction Development & Dataset Preparation
Offnet, grammar correction model design. Refactor existing language processing layer to support model integration. Define performance targets. Curate and simplify task-specific dataset.
Model Development
Train a lightweight, task-specific grammar correction model. Optimize for memory efficiency and generate performance benchmarks.
Model Integration into Core Engine
Augment existing language processing component with on-device grammar correction.
Performance, Memory, and Stress Testing
Conduct sustained-use testing to measure memory growth over time.
This project is seeking grant funding to continue and strengthen this work in an accelerated timeframe. Please contact me at sales@qubitcode.com for details and next steps.
Thank you for considering support for this work.
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