Neural Studio
This is our R&D portfolio. Where we showcase custom neural networks, proprietary intelligence systems, and the research that powers them.
This page is not for everyone and that's intentional. It answers a single question: Do these people actually build intelligence from scratch?
If you're here looking to understand our technical depth, the research that guides our work, and the systems we've built without reliance on off-the-shelf models, you're in the right place.
Who This Is For
CTOs & Technical Leaders
Engineers and architects who need to understand whether a vendor has real technical depth.
Research Teams
Organizations building proprietary intelligence and needing reference points for custom model development.
Serious Founders
Teams that have invested in understanding their problem deeply and need systems that match that rigor.
What We Build
Our portfolio spans three tiers of AI system complexity. Choose any tier to explore our functional diagrams and working systems.
Note: Each system includes functional diagrams showing intelligence flow, data handling, and decision making. Click any tier to explore detailed system architectures.
Architecture Pipeline
Data Ingestion
Multiple data sources unified into structured format.
Draft Brain Curation
Knowledge organization before model training begins.
Model Training
Custom architectures learning from organized data.
Validation
Rigorous testing against real-world constraints.
Inference & Feedback
Continuous learning through production feedback loops.
Key Principle: Each stage is deliberately sequential. We do not skip problem understanding. We do not rush to LLM integration. Intelligence is designed, not defaulted.
Dataset & Training Capability
Data Organization
- •Hierarchical data structures reflecting domain logic
- •Systematic labeling with clear intent
- •Version control for all training data
- •Continuous quality audits
Training Philosophy
- •Small, focused datasets beat large unfocused ones
- •Synthetic data used strategically, never as default
- •Failure modes identified before production
- •Models are interpretable, not black boxes
Validation Approach
- •Domain expert validation is mandatory
- •Real-world constraint testing
- •Latency and resource requirements tested early
- •Feedback loops built into production systems
Draft Brain Concept
Before training begins, we organize knowledge. The "Draft Brain" is knowledge curation how data is structured, what relationships matter, what patterns we expect the model to discover.
This happens before any model touches the data. Why? Because intelligence starts with understanding. Data organization affects outcomes more than most people realize.
Intellectual Property
Clear Ownership. No Ambiguity.
Every custom system we build is your property. All models, training methodologies, and architectures developed specifically for your problem are owned entirely by you.
What You Own
- ✓Custom models and neural architectures
- ✓Training data organization and labeling
- ✓System architecture and design documentation
- ✓All inference code and deployment specifications
Our Standards
We document everything. You receive complete model weights, training procedures, validation results, and architectural decisions. Portability and independence are guaranteed.
Research Notes
Technical insights and learning captured from each system.
Why These Notes Matter: Real research documents failures, constraints, and learning. It signals we are building systems, not assembling components. It signals discipline.