Enterprise RAG Chatbot Development
Connect AI chatbots directly to your company's internal PDFs, Notion pages, SharePoint files, and SQL databases using production-grade **Retrieval-Augmented Generation (RAG)** and vector databases.
Zero-Hallucination Search Over Corporate Knowledge
Based on our experience building highly secure knowledge bases, standard search engines return links, and basic LLMs generate generic text. **RAG combines both**—retrieving the exact paragraphs from your verified enterprise repositories and summarizing them into instant, clear answers with precise source citations.
Hybrid Dense-Sparse Vector Search
Combines semantic vector embeddings with BM25 keyword matching to find exact part numbers, policy codes, and technical jargon. (Read our technical deep dive on Advanced RAG Architecture).
Cohere Re-Ranking Layer
Filters retrieved document chunks through a cross-encoder re-ranker before feeding context to the model, maximizing accuracy.
Exact Source Citation Links
Every generated answer includes clickable citation badges linking back to the exact PDF page, Notion document, or database entry.
Vector Databases & RAG Frameworks
Pinecone & Qdrant
High-performance managed and self-hosted vector databases built for sub-100ms similarity search over millions of vectors.
Managed & Self-HostedPGVector (PostgreSQL)
Store vector embeddings directly inside your existing PostgreSQL database, keeping infrastructure simple and unified.
Native SQL Vector SearchMilvus & Weaviate
Distributed open-source vector engines engineered for massive scale, multi-tenancy, and high-concurrency enterprise workloads.
Distributed Enterprise ScaleNot Sure Which AI Architecture Fits Your Budget?
Use our interactive LLM Selector and AI Chatbot Cost Calculator to get a tailored architecture estimate based on your specific security, token volume, and deployment requirements.
Unlock Your Enterprise Knowledge Base
Stop letting corporate knowledge sit buried in scattered PDFs and drive instant answers for your team and customers.
Discuss Your RAG ArchitectureTalk Directly to an AI & ML Solutions Architect
Book a zero-pitch, 20-minute engineering session to evaluate your dataset readiness, scope vector database options (Pinecone/Milvus), map LLM architectures (RAG/Agentic), or calculate model training costs.
Book a 20-Min Technical Strategy Call
Discuss your architecture, feasibility, hardware sizing, or custom software requirements directly with a senior engineer.
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