From raw documents to intelligent insights. Building advanced RAG systems, local LLM deployments, and scalable vector databases.
The process transforms unstructured data into AI-ready knowledge through a robust 6-step workflow.
Robust data collection from multiple sources (PDF, SQL, Web, API) with error handling.
Intelligent document parsing, cleaning, and context-aware segmentation.
Transforming text into high-dimensional vectors using OpenAI or local embedding models.
Hybrid storage architecture leveraging pure vector speed and graph relationships.
Hybrid search with semantic reranking to ensure the most relevant context is selected.
Generative AI (LLM) constructs accurate, context-aware responses based on the retrieved data.
From initial discovery to production monitoring, leveraging cutting-edge tools to build robust AI solutions.
For clients with strict data privacy requirements, vLLM solutions are deployed on-premise. This enables fast inference without sending data to external API services.
> Initializing vLLM engine...
> Loading model weights (AWQ)...
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