Retrieval pipeline transforming a user query into a grounded response

RAG Development

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AI That Knows
Your Business

Build AI systems that answer questions using your actual data—not just generic training. We develop RAG (Retrieval-Augmented Generation) systems that connect LLMs to your documents, databases, and knowledge bases with proper citations and access control.

Retrieval path

Ingest · Retrieve · Cite

Market Growth

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RAG in 2026

The foundation for enterprise AI that's accurate and trustworthy.

Market by 2030$10B

RAG market growing at 38% CAGR

Use GPT Models63%

of enterprise RAG implementations

Standard Frameworks80%

use FAISS or Elasticsearch retrieval

Enterprise Use Case#1

Knowledge search and synthesis

Capabilities

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RAG System Components

End-to-end retrieval-augmented generation development.

01 · RAG Development

Document Ingestion & Processing

Ingest PDFs, Word docs, wikis, Notion, Confluence, and databases. Intelligent chunking and metadata extraction for optimal retrieval.

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02 · RAG Development

Vector Database Setup

Deploy and optimize vector stores—Pinecone, Weaviate, Qdrant, or pgvector. Hybrid search combining semantic and keyword matching.

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03 · RAG Development

Embedding & Retrieval Pipeline

Custom embedding models tuned to your domain. Reranking for relevance. Sub-second retrieval at enterprise scale.

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04 · RAG Development

Access Control & Citations

Role-based document access. Every AI answer includes source citations. Audit trails for compliance.

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05 · RAG Development

Conversational Interface

Natural language Q&A over your knowledge base. Context-aware follow-up questions. Slack, Teams, or custom UI integration.

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Continuous Learning

Feedback loops to improve retrieval quality. Analytics on query patterns. Automatic re-indexing when documents update.

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Why enterprises

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Built around how teams in enterprises work

Global timezone overlap for clear, real-time collaboration.

AI answers grounded in your proprietary data, not hallucinations

Source citations for every response—verify what AI says

Role-based access control for sensitive documents

Integration with existing knowledge systems (Confluence, Notion, SharePoint)

Hybrid search: semantic understanding + keyword precision

Enterprise-grade security and data handling

FAQ

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Frequently asked questions

RAG (Retrieval-Augmented Generation) connects LLMs like GPT-4 to your actual data before generating answers. Without RAG, AI can only use its training data and often "hallucinates" facts. With RAG, AI retrieves relevant documents from your knowledge base first, then generates answers based on that real information—with citations you can verify.