01
Document Ingestion
We ingest your PDFs, Google Docs, Notion pages, Confluence articles, Slack threads, and CSVs, any format, any source.
RAG Pipeline Development
Make AI Know Your Business
Generic AI gives generic answers. We build custom RAG pipelines that make AI fluent in your company's knowledge, your docs, your processes, your products. Accurate. Sourced. Yours.
Pinecone · Weaviate · Qdrant · pgvector · LangChain · LlamaIndex · Starting at $1,500
01Pipeline
How a RAG Pipeline Works
02
Chunking & Embedding
Documents are split into semantic chunks, converted to vector embeddings using OpenAI or Cohere, and stored in a vector database.
03
Retrieval & Generation
When a user asks a question, the system retrieves the most relevant chunks and passes them to the LLM as context for a precise, cited answer.
02Use Cases
RAG Use Cases We Build
- 01Internal AI assistant trained on company knowledge
- 02Customer-facing AI chatbot with accurate product answers
- 03AI support agent that knows your policies and FAQs
- 04AI sales rep trained on your pricing and case studies
- 05Due diligence AI that reads and summarises contracts
- 06Compliance monitoring AI trained on regulatory docs
03Tooling
Our RAG Tech Stack
LangChainLlamaIndexOpenAI EmbeddingsPineconeWeaviateQdrantpgvectorAnthropic ClaudeGPT-4oCohere Rerank
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Build Your Custom RAG Pipeline
Tell us your document sources and use case. We'll design the RAG architecture and show you a working prototype, free.
Free consultation • No obligation • Reply within 24 hours
Location
Remote team serving US, UK, Europe & Australia
Reply Time
Within 24 hours
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Book a free 30-minute discovery call to discuss your automation needs. Remote-first agency serving US (EST/PST), UK (GMT), and AUS (AEDT) timezones.
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