Build intelligent knowledge systems that combine your proprietary data with LLM capabilities. Accurate, citable, and secure AI assistants for enterprise use cases.
RAG (Retrieval-Augmented Generation) enhances LLMs by retrieving relevant documents from your knowledge base and including them in the prompt context. This grounds responses in your actual data, enables source citations, keeps knowledge current without retraining, and maintains data privacy. RAG is essential for enterprise AI because it combines the reasoning capabilities of LLMs with accurate, up-to-date, organization-specific knowledge.
Answer questions using product documentation, FAQs, and support history. Reduce ticket volume and improve response quality.
Help employees find information across wikis, policies, and documentation. Surface institutional knowledge.
Query research papers, reports, and datasets. Extract insights and synthesize findings with citations.
Search contracts, regulations, and legal documents. Draft responses with accurate references.
Medical literature search, clinical guidelines, and research synthesis with proper citations.
Policy lookup, regulatory compliance Q&A, and internal knowledge management.
We build modular, API-first RAG systems designed for production. Every component is replaceable as better tools emerge.
Design document ingestion, chunking strategies, and embedding pipelines tailored to your content types.
Configure vector databases, hybrid search, and reranking for high-precision retrieval.
Implement grounded generation with citations, hallucination detection, and confidence scoring.
Deep-dive articles on building production RAG systems, from choosing vector databases to reducing hallucinations.
Understand the key differences and learn when to use RAG, fine-tuning, or both for your AI application.
Read articleCompare Pinecone, Weaviate, Qdrant, pgvector, and Chroma to find the right vector database for your needs.
Read articleLearn effective chunking approaches including fixed-size, semantic, recursive, and sentence-window techniques.
Read articleImplement enterprise-grade RAG with access control, encryption, PII handling, and compliant deployment.
Read articleTechniques to minimize LLM hallucinations including better retrieval, verification, and UX design.
Read articleMeasure RAG quality with retrieval metrics, generation evaluation, and end-to-end assessment.
Read articleA production RAG pipeline has two main components: indexing and query processing.
Choosing the right vector database depends on your scale, features, and deployment preferences.
| Database | Best For | Scale | Deployment |
|---|---|---|---|
| Pinecone | Managed simplicity, fast setup | 1M - 1B vectors | Managed only |
| Weaviate | Hybrid search, modularity | 10M - 100M vectors | Managed + Self-hosted |
| Qdrant | Filtering, efficiency | 1M - 100M vectors | Managed + Self-hosted |
| pgvector | Existing Postgres, simplicity | <1M vectors | Self-hosted |
| Chroma | Prototyping, embedded | <100K vectors | Embedded |
RAG costs include: embedding generation ($0.0001-0.001 per 1K tokens), vector database ($20-500/month for managed), and LLM inference ($0.01-0.10 per query for GPT-4 class models). A typical enterprise system processing 10K queries/day costs $500-2000/month.
A basic RAG proof-of-concept can be built in 1-2 weeks. Production-ready systems with proper chunking, evaluation, and monitoring take 1-3 months. Enterprise deployments with access control, security requirements, and integration take 3-6 months.
For general English text: OpenAI text-embedding-3-large or Cohere embed-v3. For cost-sensitive applications: text-embedding-3-small or open-source models (BGE, E5). For multilingual: Cohere multilingual or multilingual-e5. Benchmark options on your actual queries.
Based in Bangalore, we help enterprises across India and globally build RAG systems that deliver accurate, citable answers—not hallucinated guesses.
We design document pipelines, chunking strategies, and embedding approaches tailored to your specific content types and query patterns.
Our RAG systems include hallucination detection, confidence scoring, source citations, and proper error handling from day one.
We implement access control, PII handling, audit logging, and compliant deployment for sensitive enterprise data.
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Boolean and Beyond
825/90, 13th Cross, 3rd Main
Mahalaxmi Layout, Bengaluru - 560086
590, Diwan Bahadur Rd
Near Savitha Hall, R.S. Puram
Coimbatore, Tamil Nadu 641002