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We design and implement production-grade Retrieval-Augmented Generation (RAG) and LLM platforms that connect trusted enterprise data to modern AI systems.
Our work includes:
End-to-end RAG pipelines covering document ingestion, parsing, chunking, embedding generation, vector indexing, retrieval, and response orchestration
Integration of enterprise data sources into AI workflows with controlled context selection and metadata-aware retrieval
Centralized LLM services supporting governed access, reusable embeddings, routing, and safe-response patterns
Evaluation, observability, and monitoring to support tuning, reliability, and long-term operation
These platforms are built to operate at scale, support multiple teams, and meet enterprise requirements around governance, auditability, and cost control.
We design and implement production-grade Retrieval-Augmented Generation (RAG) and LLM platforms that connect trusted enterprise data to modern AI systems.
Our work includes:
End-to-end RAG pipelines covering document ingestion, parsing, chunking, embedding generation, vector indexing, retrieval, and response orchestration
Integration of enterprise data sources into AI workflows with controlled context selection and metadata-aware retrieval
Centralized LLM services supporting governed access, reusable embeddings, routing, and safe-response patterns
Evaluation, observability, and monitoring to support tuning, reliability, and long-term operation
These platforms are built to operate at scale, support multiple teams, and meet enterprise requirements around governance, auditability, and cost control.