RAG Systems & LLM Pipelines

We build Retrieval Augmented Generation(RAG) systems that connect large language models to your proprietary data. Delivering accurate, grounded AI responses instead of hallucinations.

Build Your RAG System

rag_pipeline.py

1from langchain.vectorstores import Pinecone

2from langchain.embeddings import OpenAI

4async def rag_query(question: str):

5 embedding = embedder.embed(question)

6 docs = vectorstore.similarity_search(

7 embedding, k=5

8 )

9 context = '\n'.join([d.text for d in docs])

10 return await llm.agenerate(

11 prompt=TEMPLATE.format(ctx=context)

12 )

What We Build

AI That Knows Your Data

Custom RAG Architecture

We design retrieval pipelines tailored to your data. PDFs, databases, web pages, or proprietary APIs, using the right chunking and embedding strategy.

Vector Store Integration

Pinecone, Weaviate, pgvector, or Chroma. We select and configure the vector database that fits your latency, scale, and cost requirements.

LLM Pipeline Engineering

LangChain, LlamaIndex, and custom chains. Prompt engineering, few-shot templates, output parsing, and hallucination mitigation strategies.

Multi-Source Knowledge Bases

Ingest, clean, and index data from multiple sources, structured databases, unstructured documents, real-time feeds into a unified knowledge base.

Evaluation & Guardrails

RAGAS evaluation frameworks, faithfulness scoring, answer relevance metrics, and content filtering to ensure safe, accurate AI responses.

Production Deployment

FastAPI or LangServe endpoints, streaming responses, caching, rate limiting, and observability dashboards for production AI systems.

Tech Stack

Tools We Master

LangChain

LlamaIndex

OpenAI

Anthropic

Pinecone

Weaviate

pgvector

FastAPI

Python

RAGAS

Chroma

Ollama

Ready to Build Your AI Knowledge Base?

Book a RAG architecture consultation. We will design the system for you.

Book your Consultation