Discipline [03]
AI-Integrated Systems & LLM Workflows
Integrating intelligent agents, Retrieval-Augmented Generation (RAG), and deterministic tool-use pipelines into real-world business workflows.
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§ Scope of Delivery
- ›Custom enterprise RAG knowledge bases with hybrid vector & semantic search
- ›Autonomous agent workflows for data extraction, summarization, and task orchestration
- ›Guardrailed LLM integrations with fallback handling and structured JSON output validation
- ›Cost-optimized inference strategies (caching, batching, open-weights self-hosting)
§ Methodology & Standards
- ›Deterministic evaluation metrics to test hallucinations and response fidelity
- ›Chunking and embedding pipelines customized to enterprise document structures
- ›Strict schema validation using Zod/Pydantic to eliminate non-deterministic parsing crashes
- ›Low-latency streaming UI implementations
Tools, Libraries & Protocols
LangChain / LlamaIndexOpenAI / Anthropic Claude / DeepSeekOllama / vLLM (Local Models)Qdrant / pgvector / PineconePython / FastAPI
Target Engagement Profile
Teams aiming to augment workflows, automate complex manual processing, or build proprietary AI features with production-grade reliability.
Practical AI over Hype
AI models are only as good as the software architecture around them. Rather than slapping an API wrapper on a raw LLM, I design fault-tolerant agentic architectures that feature deterministic fallbacks, structured schema guarantees, and verifiable source citations.
Key Capabilities
- Reliable RAG: Indexing internal documentation, legal contracts, technical manuals, and tickets with intelligent re-ranking.
- Workflow Automation: Connecting LLMs safely to internal APIs, database queries, and messaging channels with strict permissions.
- Observability: Complete prompt tracking, token accounting, latency monitoring, and evaluation datasets.
Direct Access
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