Cutting-Edge AI Technologies
Discover the innovative technologies powering our AI solutions
Advanced RAG Implementation
Our Retrieval-Augmented Generation (RAG) systems combine the power of large language models with your enterprise data to deliver accurate, context-aware responses. This approach ensures AI outputs are grounded in your specific knowledge base and business context.
Our RAG implementation uses a hybrid retrieval approach that combines dense vector embeddings with sparse representations, achieving 35% better retrieval accuracy compared to standard vector search methods.
RAG Architecture
GRPO Fine-Tuning Benefits
- Requires 70% fewer training examples
- 30% faster convergence during training
- 25% improvement in task-specific performance
- Reduced computational requirements
GRPO-Based Fine-Tuning
Our proprietary Gradient-Regularized Policy Optimization (GRPO) approach enables fine-tuning language models with significantly fewer examples while achieving superior results. This innovative method makes advanced AI customization accessible even with limited training data.
Our GRPO approach combines policy gradient methods with regularization techniques that prevent catastrophic forgetting, allowing models to specialize in specific tasks while maintaining their general capabilities.
Small Language Models (SLMs)
Our Small Language Models (SLMs) provide efficient, specialized AI capabilities with significantly lower computational requirements. These models are optimized for specific tasks and can run on edge devices or resource-constrained environments.
We have implemented the latest advancements in model distillation and quantization, allowing our SLMs to achieve 90% of the performance of models 10x their size while running on standard hardware.
SLM Comparison
| Model Type | Parameters | Performance | Hardware |
|---|---|---|---|
| Large LLM | 70B+ | 100% | GPU Cluster |
| Medium LLM | 13-70B | 95% | Multiple GPUs |
| OrcaLex SLM | 1-7B | 90% | Single GPU/CPU |
| OrcaLex Edge SLM | 0.1-1B | 75% | Edge Device |
Synthetic Data Applications
Training Data Augmentation
Generate additional training examples to improve model performance with limited real data.
Edge Case Simulation
Create rare but important scenarios to test system robustness and safety.
Privacy-Preserving Data
Generate synthetic data that maintains statistical properties without exposing sensitive information.
Balanced Datasets
Create balanced training data to reduce bias and improve model fairness.
Agentic Synthetic Data
Our agentic synthetic data generation system uses collaborative AI agents to create high-quality, diverse datasets that simulate real-world scenarios and edge cases. This approach enables training robust AI models even with limited initial data.
Our agentic synthetic data system uses a CrewAI architecture where specialized agents collaborate to generate, validate, and refine synthetic data points, ensuring both diversity and realism.
Model Context Protocol
The Model Context Protocol (MCP) is an open standard that enables secure, two-way connections between AI models and external data sources, tools, and systems. Developed by Anthropic and now embraced by major AI companies including OpenAI, MCP serves as a universal interface for AI applications to access and interact with the digital world beyond their training data.
Unlike the traditional approach of building custom integrations for each data source, MCP provides a unified protocol that simplifies how AI systems connect to external resources, making truly connected AI systems easier to scale.
Context Protocol Benefits
Extended Context Windows
95%Process documents up to 200K tokens in length without performance degradation.
Enhanced AI Capabilities
85%Gives AI models access to up-to-date information beyond their training data
Actionable Intelligence
90%Enables AI to not just provide insights but take concrete actions.
Standardized Integration
80%Replaces fragmented custom integrations with a universal protocol.
Latest Model Implementations
Deepseek R1
A powerful reasoning-focused model with enhanced mathematical and logical capabilities.
Key Features:
- Advanced reasoning capabilities
- Superior mathematical problem-solving
- Optimized for complex logic tasks
- Available in 7B and 33B parameter versions
Qwen 2.5-VL-7b
Qwen 2.5-VL-7B is a multimodal model that can process both text and images.
Key Features:
- Image captioning
- Visual question answering (VQA)
- Document and chart understanding
- OCR (optical character recognition)
OrcaLex-RAG-3b
Our specialized small model optimized specifically for RAG applications.
Key Features:
- Purpose-built for RAG implementations
- Compact 3B parameter size
- Optimized context processing
- Deployable on standard hardware
Ready to Implement These Technologies?
Contact our team to discuss how our cutting-edge AI technologies can be applied to your specific business challenges.
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