We build retrieval-augmented generation pipelines that ground AI responses in your data. Get accurate, cited answers from your documents, databases, and knowledge bases.
RAG Pipelines
Retrieval-Augmented Generation
Turn your documents into searchable AI knowledge.
Find information by meaning, not just keywords.
Get accurate answers from your documents.
LLMs grounded in your proprietary data.
State-of-the-art tools for building production RAG systems
Pinecone
Managed vector database for production
Weaviate
Open-source vector search engine
Qdrant
High-performance vector similarity search
Chroma
Lightweight embedding database
pgvector
Vector search in PostgreSQL
OpenAI Embeddings
text-embedding-3-small/large
Cohere Embed
Multilingual embedding models
Voyage AI
Domain-specific embeddings
BGE/E5
Open-source embedding models
LangChain
Comprehensive RAG tooling
LlamaIndex
Data framework for LLM apps
Haystack
End-to-end NLP framework
Unstructured
Document parsing and chunking
Intelligent document splitting for optimal retrieval.
Sub-second search across millions of documents.
Your data stays secure with enterprise-grade protection.
Combine semantic and keyword search for best results.
Industry standards for building accurate RAG systems.
The tools, languages and frameworks we reach for to ship RAG pipelines.
Model-agnostic by design — we route each workload to the right model for quality, latency and cost.
Anthropic Claude & OpenAI GPT for grounded generation
Embedding models (OpenAI, Cohere, open-weight)
Rerankers for retrieval precision
Where it runs, how it scales, and how we keep it observable.
Let's create AI that knows your business inside and out.
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