Service

Knowledge Base & RAG

We build production-ready RAG (Retrieval-Augmented Generation) systems that enable LLMs to answer questions using your private data. We implement vector databases (Pinecone, Weaviate, Qdrant, pgvector), embedding models (OpenAI, Cohere, Sentence Transformers), chunking strategies, and hybrid search to deliver accurate, context-aware responses.

How can we help you?

Vector Database Setup & Optimization

We set up and optimize vector databases (Pinecone, Weaviate, Qdrant) for your scale. We configure indexes, choose appropriate dimensions, implement metadata filtering, and optimize query performance. For AWS deployments, we use pgvector on RDS or managed services.

Embedding Pipeline & Data Ingestion

We build pipelines to process your documents (PDFs, markdown, databases, APIs) into embeddings. We implement smart chunking strategies (semantic, recursive, fixed-size), handle metadata extraction, and set up automated ingestion workflows with monitoring and error handling.

RAG Architecture & Retrieval

We design RAG systems with hybrid search (vector + BM25), query rewriting, re-ranking (Cross-Encoders, Cohere Rerank), and context compression. We implement retrieval strategies like parent-child chunking, multi-query retrieval, and self-RAG for better accuracy.

Production Deployment & Monitoring

We deploy RAG systems to production with proper observability (tracing, latency monitoring, token usage), caching strategies, and cost optimization. We set up evaluation pipelines to track retrieval quality, answer accuracy, and system performance over time.

What do you gain with us?

01

Accurate, Context-Aware Answers

RAG systems provide answers grounded in your actual data, reducing hallucinations. We implement advanced retrieval techniques (hybrid search, re-ranking, query expansion) to ensure the most relevant context reaches the LLM.

02

Scalable Vector Search

Vector databases handle millions of documents with sub-second query times. We optimize indexes, implement proper sharding, and use managed services (Pinecone, AWS OpenSearch) or self-hosted solutions (Qdrant, Weaviate) based on your needs.

03

Cost-Effective LLM Usage

By retrieving only relevant context, we reduce token usage and costs. We implement caching, compression techniques, and smart chunking to minimize API calls while maintaining answer quality.

How we work

  1. Step 1

    Data analysis & requirements (document types, query patterns, scale)

  2. Step 2

    Vector DB setup & embedding pipeline (chunking, indexing, metadata)

  3. Step 3

    RAG architecture (retrieval, re-ranking, prompt engineering, context management)

  4. Step 4

    Deployment & evaluation (monitoring, quality metrics, optimization)

FAQ

Which vector databases do you recommend?

For managed services: Pinecone (easiest), Weaviate Cloud, or AWS OpenSearch. For self-hosted: Qdrant (fast, Rust-based), Weaviate (feature-rich), or pgvector (PostgreSQL extension). We choose based on your scale, budget, and infrastructure preferences.

How do you handle different document types?

We use specialized loaders: PyPDF2/Unstructured for PDFs, LangChain document loaders for various formats, database connectors for SQL sources, and API integrations. We extract metadata (titles, dates, authors) and implement preprocessing (cleaning, normalization) before chunking.

What embedding models do you use?

We use OpenAI ada-002 or text-embedding-3 for best quality, Cohere for multilingual, and Sentence Transformers (all-MiniLM, all-mpnet) for cost-effective open-source options. We test different models to find the best fit for your domain and language.

How do you improve retrieval accuracy?

We implement hybrid search (vector + BM25), query rewriting and expansion, re-ranking with Cross-Encoders or Cohere Rerank, and advanced chunking (parent-child, semantic). We also use metadata filtering and query-time retrieval strategies to narrow down results.

How long does it take to build a RAG system?

Simple RAG with a single data source: 2-3 weeks. Complex systems with multiple sources, hybrid search, and production deployment: 4-6 weeks. Timeline depends on data volume, complexity of documents, and integration requirements.