Transform Your Business with
Custom AI Models
We specialize in LLM fine-tuning, model optimization, and lightning-fast task routing. Get production-ready AI tailored to your exact requirements.
Our Services
From custom model training to enterprise-scale deployment, we deliver AI solutions that work.
Case Study: Real Results
See how our optimization technology delivers measurable improvements
We fine-tuned the Qwen3 0.6B model to solve first and second-order ordinary differential equations. Using our proprietary neural pruning methodology, we isolated neurons specifically responsible for mathematical reasoning while eliminating noisy connections that degraded performance.
Our methodology selectively preserves neurons essential for the target task while pruning redundant connections. This noise reduction dramatically improves accuracy while also reducing model size and inference time.
LLM Modification Packages
Choose the package that fits your needs. All packages include API testing before payment.
Perfect for focused tasks with smaller models.
- โ Up to 10B parameters
- โ 3 custom tasks
- โ Soft accuracy tuning
- โ API testing included
- โ GGUF export
- โ Priority support
Ideal for production workloads with custom requirements.
- โ Up to 30B parameters
- โ 7 custom tasks
- โ Hard accuracy tuning
- โ API testing included
- โ Multiple export formats
- โ Priority support
Full-scale enterprise solutions with unlimited customization.
- โ Unlimited parameters
- โ Unlimited tasks
- โ Custom training pipeline
- โ Dedicated engineer
- โ SLA guarantee
- โ On-premise deployment
| Feature | Simple | Medium | Advanced |
|---|---|---|---|
| Model Parameters | โค 10B | โค 30B | Unlimited |
| Custom Tasks | 3 | 7 | Unlimited |
| Accuracy Mode | Soft | Hard | Custom |
| Extra Task Price | $50 | $40 | Included |
| Delivery Time | 5-7 days | 3-5 days | Negotiable |
| Revisions | 1 | 3 | Unlimited |
| Support | Priority | Dedicated |
High-Speed Routing Technology
Revolutionary task routing with 98%+ accuracy at 157x the speed of traditional LLM inference.
Full technology integration with source code, comprehensive training, and 1-year premium support.
โ Traditional Approach
Like a clinic with a large administrative staff. Each request goes through a reception desk where employees manually determine the routing. As the queue grows, more staff must be hired. Each request carries the full cost of human decision-making and infrastructure overhead.
- High per-request cost (infrastructure overhead)
- Linear scaling (more traffic = more resources)
- Latency increases with load
โ Our Approach
Intelligent routing happens instantly at the entry point. No queue, no administrative overhead. The system determines the optimal path in microseconds, directing each request to the appropriate specialized model with near-zero marginal cost.
- Fixed minimal overhead (0.08% - 2.4%)
- Sublinear scaling (costs stay flat)
- Constant latency regardless of load
The Business Case: Enterprise clients implementing this technology typically achieve 50-100x reduction in inference costs while maintaining identical end-user pricing. The $500K investment pays for itself within weeks at scale.
Our Research & Technology
Proprietary technology developed by Oleg Kirichenko, solving the fundamental challenge of catastrophic forgetting in neural networks.
Dynamic Task-Graph Masked Attention โ architectural approach to continual learning using task-specific attention masks with negative-infinity masking.
- โ 98.9% accuracy on Split MNIST
- โ 0% catastrophic forgetting
- โ Hard isolation via attention masking
- โ Proven zero gradient flow theorem
Frozen Core Decomposition โ Tucker-style tensor factorization with core freezing for hard task isolation and sublinear memory growth.
- โ 96.1% accuracy with 0.2% forgetting
- โ 99%+ memory savings vs baselines
- โ Works with any LLM architecture
- โ Graceful degradation when T > k
Our technology enables continuous model improvement without losing previous capabilities.
- โ Near-100% task accuracy
- โ Continual learning capability
- โ Inference acceleration
- โ Production-ready stability
| Application Number | Filing Date | Title of Invention |
|---|---|---|
| USA 19/452,464 | Jan 19, 2026 | SYSTEM AND METHOD FOR DYNAMIC TASK-GUIDED NEURAL NETWORK COMPRESSION WITH CATASTROPHIC FORGETTING PREVENTION |
| USA 19/452,440 | Jan 19, 2026 | SYSTEM AND METHOD FOR UNSUPERVISED MULTI-TASK ROUTING VIA SIGNAL RECONSTRUCTION RESONANCE |
Developer of unique architectures for solving catastrophic forgetting in neural networks. Published research on DTG-MA and FCD methods demonstrates state-of-the-art results in continual learning with zero forgetting guarantees.
Start Your Project
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