// AI Systems & Agents
Intelligent AI
Systems
We build AI infrastructure that goes beyond chatbots — production-ready agents, RAG pipelines, and LLM integrations that create real business value.
rag_pipeline.py
from langchain import RAGChain
from pinecone import Pinecone
// Initialize knowledge base
vectorstore = Pinecone(
index_name="kb",
embedding="text-embedding-3-large"
)
chain = RAGChain(vectorstore)
// Returns grounded answers
// Capabilities
What We Build
GPT-4o powered conversational AI assistants
RAG (Retrieval-Augmented Generation) systems
Vector database architecture (Pinecone, ChromaDB)
Voice AI agents (inbound & outbound)
Multi-agent orchestration systems
AI copilots for internal team workflows
Knowledge base AI with document ingestion
LLM fine-tuning and prompt engineering
LangChain / LlamaIndex pipeline development
OpenAI, Claude, and Gemini integrations
Streaming AI response infrastructure
AI evaluation and observability
// Architecture
How RAG Systems Work
We build production RAG pipelines that give AI models accurate, real-time access to your organization's knowledge.
01
Document Ingestion
PDFs, Notion, Confluence, websites → chunked and embedded
02
Vector Storage
Embeddings stored in Pinecone or ChromaDB with metadata
03
Semantic Retrieval
User query → vector similarity search → top-k chunks retrieved
04
LLM Generation
Retrieved context + query → LLM generates accurate response
// Models
AI Models We Work With
🧠
GPT-4oOpenAI
General reasoning, code, multimodal
🧠
Claude 3.5Anthropic
Long context, analysis, writing
🧠
Gemini ProGoogle
Multimodal, search augmented
🧠
Llama 3Meta (OSS)
Private deployment, fine-tuning