Technology Stack

The Core of
Our Intelligence

We leverage the most advanced architectural patterns and proprietary optimization techniques to deliver production-ready AI systems.

Retrieval & Search

Advanced RAG Architecture

Our RAG systems combine dense and sparse retrieval methods with semantic reranking to eliminate hallucinations.

Hybrid vector & keyword search

Multi-document semantic reranking

Automatic source attribution

Real-time knowledge sync

"Our hybrid retrieval approach achieves 35% better retrieval accuracy compared to standard vector-only methods."

orcalex_rag.py

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async def retrieve_context(query: str):
  # Cross-referenced extraction
  context = await VectorStore.search(
    query,
    k=5,
    strategy="hybrid"
  )

  # Re-rank by semantic relevance
  best_matches = Reranker.process(context)

  return best_matches

Vector DB

Context Retrieved

Agentic Check

Source Validated

Optimization

Advanced LLM Fine-Tuning

We utilize a multi-pronged approach (SFT, GRPO, GSPO, and DR-GRPO) to fine-tune models for mission-critical industrial applications.

SFT: High-quality instruction tuning

GRPO: Efficient reinforcement learning

GSPO: Sequence-level importance sampling

DR-GRPO: Dead-reward stability guards

"Our multi-method fine-tuning pipeline ensures that models remain robust and stable while achieving peak performance in specialized domains."

GRPO training

Active Session

Epoch 4/5

StartLoss GradientConvergence

Learning Rate

1.2e-5

Batch Size

256

Efficiency

Small Language Models

High-performance models optimized for low-latency inference on edge devices or standard enterprise hardware.

1-7B parameter optimizations

Edge & mobile deployment ready

90% LLM performance at 10x lower cost

Quantized model weights

"We implement advanced model distillation techniques to capture the essence of 70B models in tiny 3B footprints."

Edge Inference Active

Loading OrcaLex-3B...
Success Quantization: 4-bit
Memory usage: 1.8GB / 4.0GB

Latency24ms

Running Localy

Data Engineering

Agentic Synthetic Data

A collaborative swarm of agents that generate, validate, and refine synthetic training datasets for niche domains.

Multi-agent scenario simulation

Edge case generation

Privacy-preserving dataset creation

Automated data quality scoring

"Synthetic data allows us to bypass the cold-start problem for domains with limited real-world datasets."

Connectivity

Model Context Protocol

MCP is a universal interface that allows AI models to securely interact with external systems, tools, and real-time data streams.

Secure two-way system connections

Real-time tool & API interaction

Context preservation across tools

Standardized system integrations

Connection Speed98%

Security Compliance100%

Integration Versatility92%

Latest Model Integrations

The foundation of our intelligent systems.

Deepseek R1

SOTA reasoning model for complex logic and mathematical operations.

Reasoning Core7B-33B OptimizedLogic Focus

Qwen 2.5-VL

Advanced multimodal vision-language model for document and visual analysis.

Multimodal VQAOCR ExcellenceChart Analysis

OrcaLex RAG-3b

Our proprietary small model specialized for high-precision RAG retrieval.

RAG SpecializedUltra-Low LatencyContext Optimized

Build the Future

Connect with our engineering team to explore how these technologies can power your next generation of AI applications.

Schedule Architecture Review

The Core of Our Intelligence