We build the data pipelines, vector stores, and real-time streaming infrastructure that powers modern AI applications and business intelligence.
Discuss Your DataDesign and deploy production-grade vector stores using Pinecone, Weaviate, or ChromaDB for semantic search, recommendation engines, and RAG pipelines.
Event-driven architectures using Kafka, Redis Streams, and WebSockets for real-time data processing, notifications, and live dashboards.
Modern data warehouse solutions with Snowflake or BigQuery, orchestrated with dbt for reliable, tested, and documented data transformations.
Retrieval-Augmented Generation is the most impactful AI architecture pattern today. We build production-grade RAG systems that connect your proprietary data to LLMs.
Ingestion & Chunking
Documents, PDFs, and databases are parsed, chunked, and cleaned with metadata tagging for optimal retrieval.
Embedding & Indexing
Text chunks are converted to vector embeddings using OpenAI or open-source models and stored in high-performance vector databases.
Retrieval & Generation
Semantic search retrieves the most relevant context, which is injected into LLM prompts for accurate, grounded responses.
import pinecone
from langchain.vectorstores import Pinecone
# Semantic Search
query = "Q3 Revenue analysis"
docs = index.similarity_search(
query,
k=5, # Top 5 matches
filter={ "department": "finance" }
)
# Pass to LLM
llm.predict(prompt, context=docs)