RAG pipelines
Fast, trusted answers from your own content — with sources.
Your team and customers lose time hunting through documents, wikis and tickets for answers that already exist somewhere. We connect AI to your own knowledge so anyone can ask a question in plain language and get a trustworthy, cited answer in seconds — instead of digging, guessing or asking around.
01 / What it solves
Ask a question and get the answer itself, pulled from your documents — not a pile of files to open and read.
People find the right information even when they don’t know the exact words or where it lives.
Pull the specific answer out of contracts, manuals or reports in seconds, with the source to check it.
Replies are grounded in your own data, so they fit how your company actually works — not the open web.
02 / Stack
Industry-standard, boring in the good way: tools with a future, that the next engineer will know too.
Vector Databases
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
Embedding Models
OpenAI Embeddings
text-embedding-3-small/large
Cohere Embed
Multilingual embedding models
Voyage AI
Domain-specific embeddings
BGE/E5
Open-source embedding models
RAG Frameworks
LangChain
Comprehensive RAG tooling
LlamaIndex
Data framework for LLM apps
Haystack
End-to-end NLP framework
Unstructured
Document parsing and chunking
03 / Approach
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.
04 / Standards
We adhere to industry standards and proven methods on every project — they are what makes the estimate hold.
05 / Infrastructure
Start
Tell us what your documents need to answer. A senior engineer writes back with an honest scope and price.
Let’s manage or open the client Kanban