The Memory of
AI.
In 2026, a database isn't just a row of text. It's a semantic vector store powering your AI models. We build the high-performance data backbone for RAG, Analytics, and Gemini 3.0 integration.
Architect Your DataVector Databases
We implement Pinecone, Weaviate, and pgvector to give your AI long-term memory. Essential for semantic search and RAG applications.
Real-Time Streaming
Event-driven architectures using Apache Kafka or Redpanda. Process millions of events per second for instant analytics and fraud detection.
Modern Warehousing
Centralizing truth in Snowflake or BigQuery. We build ELT pipelines that are robust, testable, and documented.
Your Data + Gemini 3.0
Public models don't know your business. We build the pipeline that safely feeds your proprietary documents, emails, and databases into the model context window.
- 1
Ingestion & Chunking
Splitting PDFs and SQL rows into semantic chunks using LangChain.
- 2
Embedding
Converting text to vectors using OpenAI or Gemini embedding models.
- 3
Retrieval
Querying Pinecone for the exact context needed to answer the user prompt.
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)