Ground your AI in proprietary knowledge with enterprise-grade retrieval systems.
Models hallucinate without access to proprietary knowledge, making them unreliable for enterprise use cases. Generic LLMs cannot answer questions about your specific data, products, or processes. Simple vector search returns irrelevant context, chunking strategies fail on complex documents, and maintaining fresh, accurate knowledge at scale becomes overwhelming. Teams struggle to build retrieval systems that actually ground model outputs in factual, up-to-date information.
Grounded AI with your data that dramatically reduces hallucinations through optimized retrieval strategies. Hybrid search combines semantic and keyword matching for superior relevance, intelligent chunking preserves document structure and context, knowledge graphs capture relationships between concepts, and automated refresh pipelines keep information current. Your AI systems provide accurate, verifiable answers grounded in your organization's knowledge with full citation tracking for transparency.
All our solutions are deployed on our production-grade cloud-native platform, designed for enterprise AI workloads at scale.
Pinecone, Weaviate, Chroma, Qdrant, custom implementations
OpenAI, Cohere, Voyage, custom fine-tuned models
Neo4j, Amazon Neptune, custom graph implementations
Unstructured, LlamaIndex, LangChain, custom parsers
2 weeks
Basic RAG implementation with vector search and simple chunking.
6-8 weeks
Production-ready knowledge platform with hybrid search and optimization.
Ongoing
Fully managed knowledge platform with continuous optimization.
Discover how our RAG platform can eliminate hallucinations and deliver accurate, verifiable answers.