Engineering Focused

Generative AI Development

Fine-tuning, RAG Pipelines, and Private Model Deployment. We architect the AI intelligence layer that powers your business applications using state-of-the-art models like DeepSeek, Claude 3.5, and Llama 3.

How We Build Custom Models

From raw data to a fine-tuned model serving your users.

Model Selection

We select the optimal model for the job. DeepSeek for coding, Claude for reasoning, or Gemini for huge context.

DeepSeek V3 Claude 3.5 Sonnet Llama 3

Vector Search Infrastructure

We deploy self-hosted Vector Databases for maximum speed and zero monthly fees. We prefer Qdrant or pgvector.

Qdrant pgvector FAISS

Deployment & API

We containerize the model (Docker) and deploy it on GPU instances (AWS/RunPod/Azure) with an API endpoint.

Docker vLLM FastAPI

# Example: Initializing Multi-Model Client
                            
                                from langchain.chat_models import ChatAnthropic, ChatDeepSeek
                            
                                from langchain.vectorstores import Qdrant
                            
                                # Using Claude 3.5 for Reasoning
                            
                                llm = ChatAnthropic(model="claude-3-5-sonnet")
                            
                                # Using DeepSeek for Code Generation (Cheaper)
                            
                                coder_llm = ChatDeepSeek(model="deepseek-coder")
                            
"System Ready: Multi-Agent Router Active..."

Model Agnostic. We don't lock you into OpenAI.

Generative AI Development

How We Build Custom Models

Model Selection

Vector Search Infrastructure

Deployment & API

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