Linq API for RAG: Enterprise Vector Search for LLMs
Linq is an enterprise-grade RAG API that powers LLM applications with hyper-accurate vector search, letting teams build searchable knowledge bases in a single day.
Overview
Linq is a purpose-built search engine for enterprises looking to deploy workplace LLM applications without the heavy lifting of building a retrieval pipeline from scratch. Instead of stitching together separate tools for extraction, embedding, indexing, retrieval, and reranking, Linq consolidates the entire Retrieval-Augmented Generation (RAG) workflow into a single, unified API—cutting integration time down from months to just a day.
What sets Linq apart from a typical vector database is its focus on accuracy and usability. It combines hybrid search and multimodal AI capabilities to ensure that results aren't just fast, but genuinely relevant, even across diverse data formats. Paired with an intuitive admin dashboard, teams can manage their knowledge base, upload documents, and connect existing apps without needing deep infrastructure expertise.
Ultimately, Linq positions itself as more than infrastructure—it's a complete vector search solution designed to help data scientists, AI engineers, and enterprise IT teams ship reliable, source-cited LLM applications faster.
Capabilities & Features
- Vector search
- Enterprise search
- LLM
- RAG
- Knowledge management
- AI search engine
- Document understanding
- Hyper-accurate search
- Hybrid Search
- Multimodal AI
Core Features
- Hyper-accurate vector search engine
- Single API for full RAG orchestration
- User-friendly admin page for knowledge base management
- Hybrid search combining keyword and semantic retrieval
- Multimodal AI support for diverse data types
- Automated extraction, embedding, indexing, and reranking
Use Cases
- Building enterprise-grade AI search engines for internal knowledge
- Accelerating document understanding with a unified API
- Orchestrating end-to-end RAG pipelines for LLM apps
- Creating source-cited Q&A systems for internal teams
- Connecting existing workplace apps to a centralized searchable index
Best For
- Data scientists
- AI engineers
- Enterprise IT professionals
- Knowledge managers
- LLM application developers
Pros
- •Drastically reduces RAG development time from months to days
- •Single API simplifies a typically complex multi-step pipeline
- •Hybrid and multimodal search improve retrieval accuracy
- •Non-technical admin page makes knowledge base management accessible
- •Answers come with cited sources for transparency and trust
Cons
- •No publicly listed pricing makes budgeting difficult upfront
- •SOC 2 and GDPR certifications are still pending, which may concern some enterprises
- •As a specialized RAG solution, it may require integration work with existing LLM stacks
- •Limited transparency on scalability for extremely large, complex datasets
How to Use
1. Upload your documents or connect existing enterprise apps to build a searchable knowledge base. 2. Query the knowledge base to retrieve accurate answers complete with cited sources. 3. Generate final answers from the search results and edit or refine the output as needed.
Frequently Asked Questions
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Pricing
Pricing details are not publicly listed; interested teams will likely need to contact Linq directly for a custom enterprise quote.
Pricing data is provided as a summary. Visit the vendor website for full tier details.