Production-Ready RAG
Unlock AI Excellence with FloTorch’s RAG Pipeline
Empower your AI applications with FloTorch's advanced RAG solutions, ensuring accurate, real-time insights tailored to your business needs.
Preprocessing
Preprocessing
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Source data
Chunking
Strategies
Strategies
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parameter fine tuning
Users Query
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Embedding
Strategies
Strategies
parameter fine tuning
Embedding
Embedding
Vector
Database
Retrieval
Strategies
parameter fine tuning
Interface
Strategies
parameter fine tuning
Evaluation/Results
Evaluation/Results
Ground
Truth
Truth
Evaluation Metrics
Experiment Results &
Hyper parameter configuration
Expert
Evolution
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Smarter Retrieval Generation
Optimized RAG Workflows, From Ingestion to Inference
FloTorch’s RAG Pipeline is purpose-built to help teams build robust, production-grade Retrieval-Augmented Generation systems quickly, securely, and cost-effectively within their AWS environment. Here’s what makes it stand out:
RAG Agent Optimization
Optimize your pipeline configurations automatically—retriever selection, chunking, reranking, and LLM choice—to reduce latency, improve answer accuracy, and align performance with your domain-specific goals.
Enterprise Monitoring and Observability & Traceability
Track performance across every step—retrieval latency, token usage, relevance scoring, and document grounding—using prebuilt dashboards and full trace logs for debugging, audit, and governance.
Hyperparameter Tuning and Evaluation Framework
Run experiments with different retrieval strategies, document chunking methods, LLMs, and prompts—all within your private AWS environment. No data leaves your control, ensuring sovereignty and security.
Data Preprocessing, Embedding Strategy, Vector Database Optimization
The pipeline is fully compatible with AWS services like Textract, Amazon Titan Embeddings, and OpenSearch, enabling secure, performant ingestion, embedding, and retrieval of enterprise data.
Embedding Strategy and Data Preprocessing
The pipeline enforces grounding by design, ensuring responses are always tied to retrieved, trusted documents—reducing hallucinations and building user trust in AI-assisted decision-making.
Pipeline YAML Architecture and LLMOps/FMOps Optimization
FloTorch pipelines are defined as modular, version-controlled YAML templates—easily cloned, modified, and deployed by teams. The architecture supports large datasets, high query volumes, and multi-tenant use cases.