AI & ML
RAG & LLM Systems
Production RAG — not a chatbot on top of a PDF. Systems that retrieve the right context and generate grounded answers at scale.
14
Stack tools
10
Deliverables
Production
Focus
Focus
AI & ML
Production-ready delivery
Tech
AWS BedrockWhat we build
Document ingestion pipelines (PDF, DOCX, HTML, Markdown, structured data)
Chunking strategies tuned per document type (semantic, recursive, sliding window)
Hybrid search — BM25 + vector + graph retrieval combined and re-ranked
GraphRAG — subgraph extraction from Neo4j/Apache AGE injected as LLM context
Multi-tenant RAG with per-user namespace isolation
Structured output with constrained decoding — guaranteed JSON schema compliance
LLM integration and multi-model routing — cheapest/fastest model per query type
Fine-tuning with LoRA/QLoRA, GPTQ/AWQ quantisation, ONNX export for production serving
Evaluation pipelines — faithfulness, relevance, and groundedness scored on every release
CI gate that blocks deployments on eval regression
Technology stack
AWS BedrockDelivery
Production RAG API with eval dashboard, latency benchmarks, and a CI gate.
Next step
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